From 557c96ebfaf9240b77ddc0fbd0b1bcf498305f44 Mon Sep 17 00:00:00 2001 From: Guglielmo-Sanchini1 Date: Thu, 25 May 2023 14:58:26 +0200 Subject: [PATCH 1/3] reformat gitignore --- .gitignore | 161 ++++++++++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 160 insertions(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index 9bea433..73d4ca5 100644 --- a/.gitignore +++ b/.gitignore @@ -1,2 +1,161 @@ - .DS_Store +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/#use-with-ide +.pdm.toml + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +#.idea/ From 328b3c2ec84f5d2d4a5159730c6289c9c8b5785e Mon Sep 17 00:00:00 2001 From: Guglielmo-Sanchini1 Date: Thu, 25 May 2023 15:04:24 +0200 Subject: [PATCH 2/3] typos correction --- examples/cp/jupyter/scheduling_tuto.ipynb | 655 +++++++++++++-------- examples/mp/jupyter/pasta_production.ipynb | 38 +- 2 files changed, 426 insertions(+), 267 deletions(-) diff --git a/examples/cp/jupyter/scheduling_tuto.ipynb b/examples/cp/jupyter/scheduling_tuto.ipynb index 3e35971..e6073c0 100644 --- a/examples/cp/jupyter/scheduling_tuto.ipynb +++ b/examples/cp/jupyter/scheduling_tuto.ipynb @@ -1,6 +1,7 @@ { "cells": [ { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -8,6 +9,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -30,8 +32,8 @@ "\n", "Each chapter of this notebook is a self-contained separate lesson.\n", "\n", - "* [Chapter 1. Introduction to Scheduling](#Chapter 1. Introduction to Scheduling)\n", - "* [Chapter 2. Modeling and solving a simple problem: house building](#Chapter-2.-Modeling-and-solving-a-simple-problem:-house-building)\n", + "* [Chapter 1. Introduction to Scheduling](#Chapter-1.-Introduction-to-Scheduling)\n", + "* [Chapter 2. Modeling and solving a simple problem: house building](#Chapter-2.-Modeling-and-solving-house-building-with-an-objective)\n", "* [Chapter 3. Adding workers and transition times to the house building problem](#Chapter-3.-Adding-workers-and-transition-times-to-the-house-building-problem)\n", "* [Chapter 4. Adding calendars to the house building problem](#Chapter-4.-Adding-calendars-to-the-house-building-problem)\n", "* [Chapter 5. Using cumulative functions in the house building problem](#Chapter-5.-Using-cumulative-functions-in-the-house-building-problem)\n", @@ -42,6 +44,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -50,6 +53,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -64,6 +68,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -75,10 +80,11 @@ "A scheduling problem can be viewed as a constraint satisfaction problem or as a constrained optimization problem. Regardless of how it is viewed, a scheduling problem is defined by:\n", "* A set of *time intervals*, to define activities, operations, or tasks to be completed\n", "* A set of *temporal constraints*, to define possible relationships between the start and end times of the intervals\n", - "* A set of *specialized constraints*, to specify of the complex relationships on a set of intervals due to the state and finite capacity of resources." + "* A set of *specialized constraints*, to specify the complex relationships on a set of intervals due to the state and finite capacity of resources." ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -89,7 +95,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -99,7 +105,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -107,6 +113,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -115,6 +122,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -127,6 +135,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -136,9 +145,9 @@ " * *interval* variables\n", " * *sequence* variables.\n", "\n", - "*Activities*, *operations* and*tasks* are represented as interval decision variables.\n", + "*Activities*, *operations* and *tasks* are represented as interval decision variables.\n", "\n", - "An interval has a *start*, a *end*, a *length*, and a *size*. An interval variable allows for these values to be variable within the model. \n", + "An interval has a *start*, an *end*, a *length*, and a *size*. An interval variable allows these values to vary within the model. \n", "The start is the lower endpoint of the interval and the end is the upper endpoint of the interval. \n", "By default, the size is equal to the length, which is the difference between the end and the start of the interval. \n", "In general, the size is a lower bound on the length.\n", @@ -147,13 +156,14 @@ "If an interval is not present in the solution, this means that any constraints on this interval acts like the interval is “not there”.\n", "The exact semantics will depend on the specific constraint.\n", "\n", - "The following example contains a dictionary of interval decision variables where the sizes of the interval variables are fixed and the keys are 2 dimensional:\n", + "The following example contains a dictionary of interval decision variables where the sizes of the interval variables are fixed and the keys are 2-dimensional:\n", "\n", " itvs = {(h,t) : mdl.interval_var(size = Duration[t]) for h in Houses for t in TaskNames}\n", "" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -170,6 +180,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -178,15 +189,16 @@ "The *constraints* indicate the conditions that are necessary for a feasible solution to the model.\n", "\n", "Several types of constraints can be placed on interval variables:\n", - "* *precedence* constraints, which ensure that relative positions of intervals in the solution (For example a precedence constraint can model a requirement that an interval a must end before interval b starts, optionally with some minimum delay z);\n", - "* *no overlap* constraints, which ensure that positions of intervals in the solution are disjointed in time;\n", - "* *span* constraints, which ensure that one interval to cover those intervals in a set of intervals;\n", + "* *precedence* constraints, which ensure that relative positions of intervals be respected in the solution (for example a precedence constraint can model a requirement that an interval *a* must end before interval *b* starts, optionally with some minimum delay *z*);\n", + "* *no overlap* constraints, which ensure that positions of intervals in the solution be disjointed in time;\n", + "* *span* constraints, which ensure that one interval cover those intervals in a set of intervals;\n", "* *alternative* constraints, which ensure that exactly one of a set of intervals be present in the solution;\n", "* *synchronize* constraints, which ensure that a set of intervals start and end at the same time as a given interval variable if it is present in the solution;\n", "* *cumulative expression* constraints, which restrict the bounds on the domains of cumulative expressions." ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -195,6 +207,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -205,7 +218,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -222,6 +235,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -235,27 +249,28 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ - "mdl0.add( mdl0.end_before_start(masonry, carpentry) )\n", - "mdl0.add( mdl0.end_before_start(masonry, plumbing) )\n", - "mdl0.add( mdl0.end_before_start(masonry, ceiling) )\n", - "mdl0.add( mdl0.end_before_start(carpentry, roofing) )\n", - "mdl0.add( mdl0.end_before_start(ceiling, painting) )\n", - "mdl0.add( mdl0.end_before_start(roofing, windows) )\n", - "mdl0.add( mdl0.end_before_start(roofing, facade) )\n", - "mdl0.add( mdl0.end_before_start(plumbing, facade) )\n", - "mdl0.add( mdl0.end_before_start(roofing, garden) )\n", - "mdl0.add( mdl0.end_before_start(plumbing, garden) )\n", - "mdl0.add( mdl0.end_before_start(windows, moving) )\n", - "mdl0.add( mdl0.end_before_start(facade, moving) )\n", - "mdl0.add( mdl0.end_before_start(garden, moving) )\n", - "mdl0.add( mdl0.end_before_start(painting, moving) )" + "mdl0.add(mdl0.end_before_start(masonry, carpentry))\n", + "mdl0.add(mdl0.end_before_start(masonry, plumbing))\n", + "mdl0.add(mdl0.end_before_start(masonry, ceiling))\n", + "mdl0.add(mdl0.end_before_start(carpentry, roofing))\n", + "mdl0.add(mdl0.end_before_start(ceiling, painting))\n", + "mdl0.add(mdl0.end_before_start(roofing, windows))\n", + "mdl0.add(mdl0.end_before_start(roofing, facade))\n", + "mdl0.add(mdl0.end_before_start(plumbing, facade))\n", + "mdl0.add(mdl0.end_before_start(roofing, garden))\n", + "mdl0.add(mdl0.end_before_start(plumbing, garden))\n", + "mdl0.add(mdl0.end_before_start(windows, moving))\n", + "mdl0.add(mdl0.end_before_start(facade, moving))\n", + "mdl0.add(mdl0.end_before_start(garden, moving))\n", + "mdl0.add(mdl0.end_before_start(painting, moving))" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -264,6 +279,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -272,7 +288,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 148, "metadata": {}, "outputs": [ { @@ -293,6 +309,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -349,6 +366,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -360,10 +378,11 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "Graphical view of these tasks can be obtained with following additional code: " + "Graphical view of these tasks can be obtained with the following additional code: " ] }, { @@ -387,14 +406,12 @@ "outputs": [ { "data": { - "image/png": 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9fPKvaJw+lYBK99wLD09PlL27InasXYkqVaogJCQEoaGhea8zxcbGwt/fHyNGjMDw4cOxd+/eQm+DChUq3PxGRcDZvoK40fO9Yev7sf6nb+HIygKQsw/aMzMBAL5+ZZCRload61cDAHxKlkLxEiWv+3g0atMO6376Glm5Q/fkX9G4lJ4uv5gJ8AjKBfQZPhIzx76Ilf+bi/ot2+Rdv+PnFfh15WK4u7ujdFl/9B85Gr6l/dD+oQF4bUBPADknSdSoUx9J8XE30l+XkAf74scZUxDSs49Tu1iVqrUC0e+ZURg/pB9sNjfcE1QPw9+chM/fGYfRvToi25GFOsEt8Z+JH9zW/Qx4bgymjRmJ7WtWoG6zVvArVx7ePiWK5PDzL4/HR7+Ot4b0zzlJomJFNO/YDQCuur5J2w551wPAPUF1sXnpfFSpGYDDu3bAw7MYylasfN37qFm/EZq174KVX82Ft08JBDRqCv/KVfH5O2/Aq5g3GrW5P++23QYOxdy3xmL0gx3g5u6G5ydPh4dnzllxQcEtcGjnVnh6eiIkJATx8fF5A2rLli348MMP4eHhgRIlSuCbb74p9DaIiopCzZo1i7TdXOkriBs93zs9PBAn/4rBS707ws3dA536D0SPx59Ep/6D8FKvjihXqTJq1vv37MvnJ0/LO0niysejU/+BOJ0Qh1f6doUBAyX97sJrs+e5pJurMeXNYidPnpx3VhKRw861qxD2yzq8MGWm2VGcSsTir5Ted+yXM2GzucHN3R2R+3bjs4ljMXXZxttyOrvzP76MtDR4+/ggMyMd4x/vi2femYIadRvcsnPcuHFOy5iRkQFv71tbenWFD1D/55izHxNnovSbxRK5fDHpDezbuhlvzP3W7CjacfpkAj4a/R9kZxtw9/DAs5NCzY50Q+ZMeAXx0cdwOTMT7fsMuOXhJIOi/A2YGT7iGjig7kCeGv+e2RG0pWL1GghdusHsGIVi9NRPzI5wQ2JjYxEQEKCsj7gG/lpBCFEOZw8TDidrYsoRVOPGjdE90Hln1SQmJqJ8+fJO88lw6phRRud5d9+NvgrvO1bo7GwfAKxp7NzPRbLC46L6zzFnPyZmYMoRVJMmTcy4W3IH0LNnT7MjuBxnd5axDXV8TqveWfV8hYEf+U4IIcSlFPYsPlOOoI4dO3bzG5nok+HUMSM7q+nUMSM7WxNTjqAyMzOd+vb3zvbJcOqYkZ3VdOqYkZ3VQukjqOzsbKV9Mpw6ZmRnNZ06ZmRna2LKgEpIKPxHEJjhk+HUMSM7q+nUMSM7WxOeJEEIIcSlKL3Ed+rUKaV9Mpw6ZmRnNZ06ZmRna2LKgPJw8qe7Otsnw6ljRnZW06ljRna2JlziI4QQ4lKUXuKLiIhQ2ifDqWNGdlbTqWNGdrYmphxB2e12px5+Otsnw6ljRnZW06ljRnZWC6WPoDJzP+ZYVZ8Mp44Z2VlNp44Z2dmamDKgkpKSlPbJcOqYkZ3VdOqYkZ2tCU+SIIQQ4lKUXuKzwl9MM6N6PhlO1X0ynDpmZGdrYsqA8vb2Vtonw6ljRnZW06ljRna2JlziI4QQ4lKUXuI7cuSI0j4ZTh0zsrOaTh0zsrM1MeUIyuFwwM3N7bY9snwynDpmZGc1nTpmZGe1UPoIKi0tTWmfDKeOGdlZTaeOGdnZmpgyoJKTk5X2yXDqmJGd1XTqmJGdrQlPkiCEEOJSlF7ii4uLU9onw6ljRnZW06ljRna2JqYMKF9fX6V9Mpw6ZmRnNZ06ZmRna8IlPkIIIS5F6SW+8PBwpX0ynDpmZGc1nTpmZGdrwiMoQgghLkXpI6iUlBSlfTKcOmZkZzWdOmZkZ2tiyoBKTU1V2ifDqWNGdlbTqWNGdrYmXOIjhBDiUpRe4ouNjVXaJ8OpY0Z2VtOpY0Z2tiamDCg/Pz+lfTKcOmZkZzWdOmZkZ2tiyoDy8fFR2ifDqWNGdlbTqWNGdrYmpgyoyMhIpX0ynDpmZGc1nTpmZGdrwpMkCCGEuBSlT5I4d+6c0j4ZTh0zsrOaTh0zsrM1MWVAZWRkKO2T4dQxIzur6dQxIztbEy7xEUIIcSmFXeJzd0WYa4mJiUGNGjWU9clw6phRRue5S39GuTqNnepUndhff0a1+3uYHaNA3CLC0Lt3b6f5rLAvqp5RxnOlbOKfaNu2rVOdBWHKEp+/v7/SPhlOHTPK6Hz26H6nO1Un40yi2RFuyuHDh53qs8K+qHpGGc+Vbdu2Od1ZEKYMKC8vL6V9Mpw6ZpTRmeiBFfZFK2S0OqYMqOjoaKV9Mpw6ZpTRmeiBFfZFK2S0OqYMqNq1ayvtk+HUMaOMzlZg18Y1iIs6Ztr9//jxFBzY8VuBtwnftQMRe//Iu7zup2+wZdlC2dEKjRX2RStkvBVU2hdMGVBnzpxR2ifDqWNGGZ1l4MjKcqovbOM6xN9gQDn7vq7HY6NeRcP7Cn4h+3DYDkTu+/eM3K6PDkG7Pv1lRys0VtgXrZDxVlBpXzDlLD673a60T4ZTx4wyOt+MLcsWYvm8ORBCoFpgEO7r9iAWz5mBLPtllCjthxc/nI3SZcth/sxQnEtKxOmEOPj6lUHD1vcjbOMa2C9fRlL8CYQ88BAGPD8GAPDrisX4+dsvkWW/jFoNmmDEW/+Fm5sbBjWpiR6Dn8KeLRvhWawYxs7+Cn+fOI7dm9fjyB87sWjOdLzy8Rf45I0xCGwcjIi9f6B+y9bYvHQBZq7dBncPD6RfTMVLvTpi1rrtcPfwuG6n5ORk/F/3EAQ0aIKYo+GoWL0GRn0wA8vnzcHuzRtwOfMSAhsF45l3pkAIgZljX0Rwu05o1e0BPNOhOdr16Y/dWzbAYc/CmBlz4elZDOvnfwubzQ2/rVyM4W++h0M7t6JYcR/0Hv4sJgzuh1oNGyN81w6kXbiAke9NRZ3gFsjMSMfM10cjISYKle+tidMJ8Xhq/GTUrN/Q6Y+jFfZFFTIeP34c3bp1Q5s2bfD777+jYcOGGDZsGN566y1ERkbilVqNcHfV6pj9xktIjDsBL29vPPPOFFStVRsjO7XE1GUb4FOyFADguS734b0flmPdj1/fcF94oEtHAEB6ejqeeOIJREREICgoCMePH8fs2bMRHHzTM8eLhClHUBUqVFDaJ8OpY0YZnQvixJ+RWDxnBiZ+vQAfLd+IJ8e9g6CmzfHf+asQunQD2vTojWVffJJ3+5jDB/HaJ19h9NSc6/48tB8vfjgLocs2YMe6VYg6dADx0X9i+8/L8d4PyzF12UbY3NywdeUSAMCl9HQENGyCj5ZvRJ3gFti48HvUbtIMwe27YMgr4zF12UbcXbU6ACAt9QImfbcEA54fg7rNW2HPrxsBANtWL0fLLj1vOJz+4eRf0ej8yCBMW7EJxUuUwNofvkb3QcMwZdEaTF+5GZczL2H35g3X/d6SfmUQumQ9uj42BCvmzYF/5Sro8shgPDB0BKYuy8l+LY4sBz5Y+DOGjZuIBbOnAgDW/vA1SpQshWkrNqH/s6MRffhg0R6gImCFfVGVjFFRUXjhhRdw8OBBRERE4IcffsC2bdvQrVs3LJ77MebPDEWNoHqYtmITBo0ei5mvjYLNZkOzjl2xa8MaAMCxA3tRrlJllC5bLp//yn3hl19+AQB88skn8PPzw8GDBzF+/Hjs2bPn1osXgCkDKioqSmmfDKeOGWV0Lojw37ehZdcHUNLvLgCAb2k/nP37FCYNfwyjH+yA5V9+iriof99As1mHLvAq5p13ueF9beHrVwZexbzRonN3ROwNw8GdWxFz+BBe698dY/p0wqGdW5EYl/M5O+4enghu3xkAcG/dBkhKiLthttbde+X9v1P/gdi8ZD4AYPOS+ejQ95GbditboSJqN2kOAGjbqx+O7g1D+K4dGDugJ0Y/2AHhv2+/qtuVtOiS8zdUNW6S8Upadume1+t0QjwA4OjeMLTukfO3TlUDaqNaYFChXLeCFfZFVTLec889qF+/Pmw2G+rWrYuOHTtCCIG7774bpxPiELE3DG17PwwAqN+yDVJTkpGWegGtu/fC9jUrAADbf16O1t2v/3dsV+4LycnJAHJON3/00UcBAPXq1UODBg1uKfvNMGWJr1KlSkr7ZDh1zCijc0EYhgEhxFXXffnum3hw2NNo1qErwnftwIJZU/O+5uVd/KrbClz9vRACMAy069Mfj48Zl+/+3D3c8+7PZnODI8txw2xX3lftJs2RlDAOh8N2IjvbgaoBhXhx/JpeQgh8/s7rmLJoDcpWqIT5M0Nhz8y87rd6eHrmZcwuIOOVuHvknPJsc7P9+7pZEd515naxwr6oSsYrT0+32Wx5l4UQcDgccHNzy/c9QggENg7G3yeO4/y5swjbuBYPP/PCdf1X7gvZ2dkAcp5rrsCUIyibzbl362yfDKeOGWV0Loj6rUKwY81KpCbnvElmakoy0i9eQBn/nKWTLcsWFPj9B3b8htSUZGReykDYxnWo3bgZ6rcKwc71q3H+7Jk8Z1LuEcWN8PbxQUbaxQJv0673w5g2ZiTaF+LoCQDOnEzIO6lh2+plCMo9mvL1K4OMtDTsXL+6UJ5/M5a4acZrqd20OXasXQkAiIs6hhPHIor0/UXBCvuiFTICQJ3glnnL0uG7dqCkXxkUL+ELIQSad+qG/73/NirdWwu+fmUK7WzTpg0WLMh5Ph05cgSHDh2Skp0f+e4ip44ZXf2R01VrBaLfM6Mwfkg/vNS7E/73/kQMeH4MQl98Gm8O6nPTJ2Dtps3x8Wuj8HKfzmjVpQdq1m+IKjUDMPCFV/HO8EcxuldHvPPko0g5XfA7O7Tp2RvL532Klx/qjL9PHL/ubUIe7Iu0C+cR0rNPobpVvrcWtixbiNG9OiI1JQVdHxuCTv0H4aVeHfHB88NQs17RTlQIbt8ZuzauxZg+nXBk965CfU+3x57AhXNnMbpXRyz7fDaqBQShuK9vke63sFhhX7RCRgAY8PwYRIcfwOheHfHdR5Px/Psz8r7Wuntv/LZi8VVL0IVh5MiROH36NBo0aIAPPvgADRo0QKlSpZwdnW8WS6zF5MmTUbvfMKd7f1kyH9HhBzBiwmSnu6/HzrWrEPbLOrwwZebNb/vFR5i/fCWmr9zsgmQ3xuFwwJFlh6dXMfx94jjefmIAZq7dBg9PT0Qs/grjxuVfBiXmIeO58s/j7HA4YLfbUaxYMURHR6Njx444duwYPHOXk2+G0m8Wm5iYiPLlyyvrk+HUMaOMzncCX0x6A/u2bsYbc781O0qRuJyRgbeGPoysrCwYhoGn334/7/UtZ2OFfdEKGWWRnp6O9u3bw263wzAMfPrpp4UeTkXBlAFFiGp06PtIoc6mcwZPjX+vSLf38/Mz/egJALxLlMCUxWvNjkEUwNfXF65YTTPlNShn/5Yg47cOZlTPR/TBCvuiFTJaHVOOoI4dO4aAgABlfTKcOmaU0TkoKAgPBTrvDySt0PmzsmXRV+HOALA0yLl/E2WFx0X1jM5+rgDAb23aONV3M0w5SSIzM9Opby3vbJ8Mp44Z2VlNp44Z2VktCnuShClLfP/8sZeqPhlOHTOys5pOHTOyszUxZUAlJCQo7ZPh1DEjO6vp1DEjO1sT/h0UIYQQl6L0Et+pU6eU9slw6piRndV06piRna2JKQPK4yYfLWC2T4ZTx4zsrKZTx4zsbE24xEcIIcSlKL3EFxHh3HdBdrZPhlPHjOysplPHjOxsTUw5grLb7U49/HS2T4ZTx4zsrKZTx4zsrBZKH0Fl3uCD1VTxyXDqmJGd1XTqmJGdrYkpAyopKUlpnwynjhnZWU2njhnZ2ZrwJAlCCCEuReklPiv8xTQzqueT4VTdJ8OpY0Z2tiamDChvb2+lfTKcOmZkZzWdOmZkZ2vCJT5CCCEuReklviNHjijtk+HUMSM7q+nUMSM7WxNTjqAcDgfc3Nxu2yPLJ8OpY0Z2VtOpY0Z2Vgulj6DS0tKU9slw6piRndV06piRna2JKQMqOTlZaZ8Mp44Z2VlNp44Z2dma8CQJQgghLkXpJb64uDilfTKcOmZkZzWdOmZkZ2tiyoDy9fVV2ifDqWNGdlbTqWNGdrYmXOIjhBDiUpRe4gsPD1faJ8OpY0Z2VtOpY0Z2tiY8giKEEOJSlD7ZEJ/nAAAM6UlEQVSCSklJUdonw6ljRnZW06ljRna2JqYMqNTUVKV9Mpw6ZmRnNZ06ZmRna8IlPkIIIS5F6SW+2NhYpX0ynDpmZGc1nTpmZGdrYsqA8vPzU9onw6ljRnZW06ljRna2JqYMKB8fH6V9Mpw6ZmRnNZ06ZmRna2LKgIqMjFTaJ8OpY0Z2VtOpY0Z2tiY8SYIQQohLUfokiXPnzintk+HUMSM7q+nUMSM7WxNTBlRGRobSPhlOHTOys5pOHTOyszXhEh8hhBCXovQSX0xMjNI+GU4dM7Kzmk4dM7KzNTFlQPn7+yvtk+HUMSM7q+nUMSM7WxNTBpSXl5fSPhlOHTOys5pOHTOyszVxN+NOo6OjUbt2bWV9Mpw6ZpTRed6qDShXp7FTnYTcEod3om/fvk7TWeH552pMOYJy9kaT8SAwo3o+ADh7dL/TnYTcChEREU71WeH552pMGVBnzpxR2ifDqWNGGZ0JuVPh8y8/pgwou92utE+GU8eMMjoTcqfC519+TBlQFSpUUNonw6ljRhmdCTBhcD9EHTrgFNegJjWve/2PH0/BgR2/OeU+SOHg8y8/pgyoqKgopX0ynDpmlNGZuIbHRr2Khve1NTuGVvD5lx9TzuKrVKmS0j4ZTh0zyuisE0nxcZg0YiACGjRBzNFwVKxeA6M+mHHVbQY1qYnv9+b8INq5dhV2b9mI/3t/OmaOfRGexYohISYKp0/G4/nJ07Bl2QJE7t+DWg2a4P/en57n+N/7ExEeth0lSpbG6I8+Rakyd2Hm2BcR3K4TWnV7AM90aI52ffpj95YNcNizMGbGXFSuUQvnz53F9JdHIjUlGTXrNcK+bZvx4eK1KOl3l0u3050Cn3/5MeUIymZz7t062yfDqWNGGZ114+Rf0ej8yCBMW7EJxUuUwNofvi7096ZdSMHErxdi2Otv47/PDsUDQ5/G9FVbcOLYUfx1NBwAcCk9HTXq1kfokvWo06wlFsyael1XSb8yCF2yHl0fG4IV8+YAABbMmor6LdogdMl6tOjcHWdOJtx+YY3h8y8//Mh3Fzl1zHgnfOS02ZStUBG1mzQHALTt1Q9H94YV+nuD23eBEAJVA4JQqmw5VAsMgs1mQ5VagUhKiAOQ80Osdfdeef6IG/hbdOkBAKhRt0He90bsDUPrnr0BAI1D2qNEqdK3VpIA4PPvepgyoAICApT2yXDqmFFGZ+0Q4pqL4tob5P3v8uXMq77i4eEJALAJGzw8Pa9w2JCd5bjB3V3rz3Xlfr/N5pb3vUV4n2lSCPj8y48pAyoxMVFpnwynjhlldNaNMycTELkv5xMEtq1ehqDco6l/KF22HOKj/0R2djZ2bVhTZH92djZ2rluV41+1NO9orTAENW2GHWtWAAD2b9uCi+dTinz/5F/4/MuP9RcpCbmDqXxvLWxZthCje3VEakoKuj425KqvP/7SOEx+ZgjeHtoffuWK/uagxYoXR1zUMbzStysO/b4d/Z97qdDfO+C5MTiw/Te83LcL9m3dDL9y5eHtU6LIGQi5Efw8KGIpJk+ejNr9hpkdwyUkxcdh8rNDMH3lZrOjXBf75UzYbG5wc3dH5L7d+GziWExdttHsWC4jYvFXGDdunNkxLInSnwd17NgxpX0ynDpmlNGZqMPpkwl4rX93vNS7E758bzyenRRqdiRLw+dffkz5O6hq1aop7ZPh1DGjjM464V+5irJHTwBQsXoNhC7dYHaMOwY+//JjyhFUdna20j4ZTh0zyuhMyJ0Kn3/5MeUIKiEhATVrXv89wFTwyXDqmFFG5zp16qBPoPPeYywqKsqpGZ3tk+HUMaOMzsvq1HGqzwrPP1fDkyQIIYS4FKVPkjh16pTSPhlOHTOys5pOHTOyszUxZUB5eHgo7ZPh1DEjO6vp1DEjO1sTLvERQghxKUov8UVERCjtk+HUMSM7q+nUMSM7WxNTjqDsdrtTDz+d7ZPh1DEjO6vp1DEjO6uF0kdQmZmZN7+RiT4ZTh0zsrOaTh0zsrM1MWVAJSUlKe2T4dQxIzur6dQxIztbE54kQQghxKUovcSXkODcj4Z2tk+GU8eM7KymU8eM7GxNTBlQ3t7eSvtkOHXMyM5qOnXMyM7WhEt8hBBCXIrSS3xHjhxR2ifDqWNGdlbTqWNGdrYmphxBORwOuLm53bZHlk+GU8eM7KymU8eM7KwWSh9BpaWlKe2T4dQxIzur6dQxIztbE1MGVHJystI+GU4dM7Kzmk4dM7KzNeFJEoQQQlyK0kt8cXFxSvtkOHXMyM5qOnXMyM7WxJQB5evrq7RPhlPHjOysplPHjOxsTbjERwghxKUovcQXHh6utE+GU8eM7KymU8eM7GxNeARFCCHEpSh9BJWSkqK0T4ZTx4zsrKZTx4zsbE1MGVCpqalK+2Q4dczIzmo6dczIztaES3yEEEJcitJLfLGxsUr7ZDh1zMjOajp1zMjO1sSUAeXn56e0T4ZTx4zsrKZTx4zsbE1MGVA+Pj5K+2Q4dczIzmo6dczIztbElAEVGRmptE+GU8eM7KymU8eM7GxNeJIEIYQQl6L0SRLnzp1T2ifDqWNGdlbTqWNGdrYmpgyojIwMpX0ynDpmZGc1nTpmZGdrwiU+QgghLkXpJb6YmBilfTKcOmZkZzWdOmZkZ2tiyoDy9/dX2ifDqWNGdlbTqWNGdrYmpgwoLy8vpX0ynDpmZGc1nTpmZGdrYsqAio6OVtonw6ljRnZW06ljRna2JjxJghBCiEtR+iSJM2fOKO2T4dQxIzur6dQxIztbE1MGlN1uV9onw6ljRnZW06ljRna2JlziI4QQ4lKUXuKLiopS2ifDqWNGdlbTqWNGdrYmphxBZWRkwNvb+7Y9snwynDpmZGc1nTpmZGe1UPoIymZz7t062yfDqWNGdlbTqWNGdrYm/Mh3Fzl1zMjOajp1zMjO1oQnSRBCCHEpSi/xJSYmKu2T4dQxIzur6dQxIztbE+svUhJCCLkj4RIfIYQQl6L0Eh8hhBByMzigCCGEKAkHFCGEECXhgCKEEKIkHFCEEEKUhAOKEEKIknBAEUIIURIOKEIIIUrCAUUIIURJivROEkKI0wCc8Ra5ZQGccYJHd7gdbx9uQ+fA7Xj76LQNqxmGUe5mNyrSgHIWQojdhXmbC1Iw3I63D7ehc+B2vH24DfPDJT5CCCFKwgFFCCFEScwaUJ+ZdL93GtyOtw+3oXPgdrx9uA2vwZTXoAghhJCbwSU+QgghSsIBRQghRElcPqCEEN2EEJFCiCghxFhX378VEUJUEUJsFkIcFUIcFkK8kHt9GSHEBiHEn7n/+pmdVXWEEG5CiH1CiFW5l+8RQuzK3YbzhRCeZmdUHSFEaSHEIiFERO4+2Yr7YtEQQozOfS6HCyF+FEIU476YH5cOKCGEG4DZALoDqAPgMSFEHVdmsChZAMYYhhEEoCWA53K321gAmwzDqAVgU+5lUjAvADh6xeUPAEzL3YbJAIabkspazACw1jCM2gAaImd7cl8sJEKISgBGAQg2DKMeADcAj4L7Yj5cfQTVHECUYRgxhmFcBvATgN4uzmA5DMM4ZRjG3tz/pyLnB0Il5Gy7r3Nv9jWAPuYktAZCiMoAegL4IveyANABwKLcm3Ab3gQhREkAbQF8CQCGYVw2DCMF3BeLijsAbyGEO4DiAE6B+2I+XD2gKgGIu+JyfO51pJAIIaoDaAxgF4DyhmGcAnKGGAB/85JZgukAXgWQnXv5LgAphmFk5V7m/nhzagA4DeCr3KXSL4QQPuC+WGgMw0gAEArgBHIG03kAe8B9MR+uHlDiOtfxPPdCIoQoAWAxgBcNw7hgdh4rIYR4AECSYRh7rrz6Ojfl/lgw7gCaAPjUMIzGANLA5bwikfv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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -422,17 +439,19 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "##### Note on interval variables\n", "\n", - "After a time interval has been assigned a start value (say s) and an end value (say e), the interval is written as [s,e). \n", - "The time interval does not include the endpoint e. \n", - "If another interval variable is constrained to be placed after this interval, it can start at the time e." + "After a time interval has been assigned a start value (say *s*) and an end value (say *e*), the interval is written as [*s*,*e*). \n", + "The time interval does not include the endpoint *e*. \n", + "If another interval variable is constrained to be placed after this interval, it can start at the time *e*." ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -440,6 +459,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -457,12 +477,13 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Problem to be solved\n", "\n", - "The problem consists of assigning start dates to tasks in such a way that the resulting schedule satisfies precedence constraints and minimizes a criterion. \n", + "The problem consists in assigning start dates to tasks in such a way that the resulting schedule satisfies precedence constraints and minimizes a criterion. \n", "The criterion for this problem is to minimize the earliness costs associated with starting certain tasks earlier than a given date, and tardiness costs associated with completing certain tasks later than a given date.\n", "\n", "For each task in the house building project, the following table shows the duration (measured in days) of the task along with the tasks that must finish before the task can start.\n", @@ -472,6 +493,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -492,6 +514,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -499,6 +522,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -512,6 +536,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -523,13 +548,15 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "Solving the problem consists of identifying starting dates for the tasks such that the total cost, determined by the earliness and lateness costs, is minimized." + "Solving the problem consists in identifying starting dates for the tasks such that the total cost, determined by the earliness and tardiness costs, is minimized." ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -537,7 +564,7 @@ "\n", "The first step in modeling the problem is to write a natural language description of the problem, identifying the decision variables and the constraints on these variables.\n", "\n", - "Writing a natural language description of this problem requires to answer these questions:\n", + "Writing a natural language description of this problem requires answering these questions:\n", "* What is the known information in this problem ?\n", "* What are the decision variables or unknowns in this problem ?\n", "* What are the constraints on these variables ?\n", @@ -545,6 +572,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -571,6 +599,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -578,6 +607,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -587,6 +617,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -607,6 +638,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -632,6 +664,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -639,6 +672,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -647,25 +681,27 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Precedence constraints are used to specify when an interval variable must start or end with respect to the start or end time of another interval variable. \n", "\n", "The following types of precedence constraints are available; if *a* and *b* denote interval variables, both interval variables are present, and *delay* is a number or integer expression (0 by default), then:\n", - "* *end_before_end(a, b, delay)* constrains at least the given delay to elapse between the end of a and the end of b. It imposes the inequality endTime(a) + delay <= endTime(b).\n", - "* *end_before_start(a, b, delay)* constrains at least the given delay to elapse between the end of a and the start of b. It imposes the inequality endTime(a) + delay <= startTime(b).\n", - "* *end_at_end(a, b, delay)* constrains the given delay to separate the end of a and the end of ab. It imposes the equality endTime(a) + delay == endTime(b).\n", - "* *end_at_start(a, b, delay)* constrains the given delay to separate the end of a and the start of b. It imposes the equality endTime(a) + delay == startTime(b).\n", - "* *start_before_end(a, b, delay)* constrains at least the given delay to elapse between the start of a and the end of b. It imposes the inequality startTime(a) + delay <= endTime(b).\n", - "* *start_before_start(a, b, delay)* constrains at least the given delay to elapse between the start of act1 and the start of act2. It imposes the inequality startTime(a) + delay <= startTime(b).\n", - "* *start_at_end(a, b, delay)* constrains the given delay to separate the start of a and the end of b. It imposes the equality startTime(a) + delay == endTime(b).\n", - "* *start_at_start(a, b, delay)* constrains the given delay to separate the start of a and the start of b. It imposes the equality startTime(a) + delay == startTime(b).\n", + "* *end_before_end(a, b, delay)* constrains at least the given delay to elapse between the end of *a* and the end of *b*. It imposes the inequality endTime(a) + delay <= endTime(b).\n", + "* *end_before_start(a, b, delay)* constrains at least the given delay to elapse between the end of *a* and the start of *b*. It imposes the inequality endTime(a) + delay <= startTime(b).\n", + "* *end_at_end(a, b, delay)* constrains the given delay to separate the end of *a* and the end of *b*. It imposes the equality endTime(a) + delay == endTime(b).\n", + "* *end_at_start(a, b, delay)* constrains the given delay to separate the end of *a* and the start of *b*. It imposes the equality endTime(a) + delay == startTime(b).\n", + "* *start_before_end(a, b, delay)* constrains at least the given delay to elapse between the start of *a* and the end of *b*. It imposes the inequality startTime(a) + delay <= endTime(b).\n", + "* *start_before_start(a, b, delay)* constrains at least the given delay to elapse between the start of *a* and the start of *b*. It imposes the inequality startTime(a) + delay <= startTime(b).\n", + "* *start_at_end(a, b, delay)* constrains the given delay to separate the start of *a* and the end of *b*. It imposes the equality startTime(a) + delay == endTime(b).\n", + "* *start_at_start(a, b, delay)* constrains the given delay to separate the start of *a* and the start of *b*. It imposes the equality startTime(a) + delay == startTime(b).\n", "\n", "If either interval *a* or *b* is not present in the solution, the constraint is automatically satisfied, and it is as if the constraint was never imposed." ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -678,23 +714,24 @@ "metadata": {}, "outputs": [], "source": [ - "mdl1.add( mdl1.end_before_start(masonry, carpentry) )\n", - "mdl1.add( mdl1.end_before_start(masonry, plumbing) )\n", - "mdl1.add( mdl1.end_before_start(masonry, ceiling) )\n", - "mdl1.add( mdl1.end_before_start(carpentry, roofing) )\n", - "mdl1.add( mdl1.end_before_start(ceiling, painting) )\n", - "mdl1.add( mdl1.end_before_start(roofing, windows) )\n", - "mdl1.add( mdl1.end_before_start(roofing, facade) )\n", - "mdl1.add( mdl1.end_before_start(plumbing, facade) )\n", - "mdl1.add( mdl1.end_before_start(roofing, garden) )\n", - "mdl1.add( mdl1.end_before_start(plumbing, garden) )\n", - "mdl1.add( mdl1.end_before_start(windows, moving) )\n", - "mdl1.add( mdl1.end_before_start(facade, moving) )\n", - "mdl1.add( mdl1.end_before_start(garden, moving) )\n", - "mdl1.add( mdl1.end_before_start(painting, moving) )" + "mdl1.add(mdl1.end_before_start(masonry, carpentry))\n", + "mdl1.add(mdl1.end_before_start(masonry, plumbing))\n", + "mdl1.add(mdl1.end_before_start(masonry, ceiling))\n", + "mdl1.add(mdl1.end_before_start(carpentry, roofing))\n", + "mdl1.add(mdl1.end_before_start(ceiling, painting))\n", + "mdl1.add(mdl1.end_before_start(roofing, windows))\n", + "mdl1.add(mdl1.end_before_start(roofing, facade))\n", + "mdl1.add(mdl1.end_before_start(plumbing, facade))\n", + "mdl1.add(mdl1.end_before_start(roofing, garden))\n", + "mdl1.add(mdl1.end_before_start(plumbing, garden))\n", + "mdl1.add(mdl1.end_before_start(windows, moving))\n", + "mdl1.add(mdl1.end_before_start(facade, moving))\n", + "mdl1.add(mdl1.end_before_start(garden, moving))\n", + "mdl1.add(mdl1.end_before_start(painting, moving))" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -703,6 +740,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -712,10 +750,11 @@ "Weighting this value with the cost per day of starting early determines the cost associated with the task.\n", "\n", "The cost for ending a task later than the preferred date is modeled in a similar manner using the expression *endOf()*. \n", - "The earliness and lateness costs can be summed to determine the total cost." + "The earliness and tardiness costs can be summed to determine the total cost." ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -737,15 +776,17 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "Solving a problem consists of finding a value for each decision variable so that all constraints are satisfied. \n", - "It is not always know beforehand whether there is a solution that satisfies all the constraints of the problem. \n", + "Solving a problem consists in finding a value for each decision variable so that all constraints are satisfied. \n", + "It is not always known beforehand whether there is a solution that satisfies all the constraints of the problem. \n", "In some cases, there may be no solution. In other cases, there may be many solutions to a problem." ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -754,7 +795,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 149, "metadata": {}, "outputs": [ { @@ -823,6 +864,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -850,14 +892,12 @@ "outputs": [ { "data": { - "image/png": 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -885,6 +925,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -892,6 +933,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -899,6 +941,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -917,6 +960,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -924,10 +968,11 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "The problem consists of assigning start dates to a set of tasks in such a way that the schedule satisfies temporal constraints and minimizes a criterion. \n", + "The problem consists in assigning start dates to a set of tasks in such a way that the schedule satisfies temporal constraints and minimizes a criterion. \n", "The criterion for this problem is to minimize the tardiness costs associated with completing each house later than its specified due date and the cost associated with the length of time it takes to complete each house.\n", "\n", "For each type of task, the following table shows the duration of the task in days along with the tasks that must be finished before the task can start. \n", @@ -938,6 +983,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -958,6 +1004,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -965,6 +1012,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -980,13 +1028,15 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "Solving the problem consists of determining starting dates for the tasks such that the cost, where the cost is determined by the lateness costs and length costs, is minimized." + "Solving the problem consists in determining starting dates for the tasks such that the cost, where the cost is determined by the lateness costs and length costs, is minimized." ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -994,7 +1044,7 @@ "\n", "* What is the known information in this problem ?\n", "\n", - " There are five houses to be built by two workers. For each house, there are ten house building tasks, each with a given duration, or size. Each house also has a given earliest starting date. For each task, there is a list of tasks that must be completed before the task can start. Each task must be performed by a given worker, and there is a transition time associated with a worker transferring from one house to another house. There are costs associated with completing eachhouse after its preferred due date and with the length of time it takes to complete each house.\n", + " There are five houses to be built by two workers. For each house, there are ten house building tasks, each with a given duration, or size. Each house also has a given earliest starting date. For each task, there is a list of tasks that must be completed before the task can start. Each task must be performed by a given worker, and there is a transition time associated with a worker transferring from one house to another house. There are costs associated with completing each house after its preferred due date and with the length of time it takes to complete each house.\n", " \n", " \n", "* What are the decision variables or unknowns in this problem ?\n", @@ -1013,16 +1063,17 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "## Step2: Prepare data\n", + "## Step 2: Prepare data\n", "First coding step is to prepare model data:" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -1063,16 +1114,18 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "One part of the objective is based on the time it takes to build a house.\n", "To model this, one interval variable is used for each house, and is later constrained to span the tasks associated with the given house. \n", - "As each house has an earliest starting date, and each house interval variable is declared to have a start date no earlier than that release date. \n", + "As each house has an earliest starting date, each house interval variable is declared to have a start date no earlier than that release date. \n", "The ending date of the task is not constrained, so the upper value of the range for the variable is maxint." ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1081,7 +1134,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 63, "metadata": {}, "outputs": [], "source": [ @@ -1093,7 +1146,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 64, "metadata": {}, "outputs": [], "source": [ @@ -1101,6 +1154,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1108,30 +1162,32 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Each house has a list of tasks that must be scheduled. \n", - "The duration, or size, of each task t is Duration[t]. \n", + "The duration, or size, of each task *t* is Duration[t]. \n", "This information allows to build the matrix *itvs* of interval variables." ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 65, "metadata": {}, "outputs": [], "source": [ "TaskNames_ids = {}\n", "itvs = {}\n", "for h in Houses:\n", - " for i,t in enumerate(TaskNames):\n", - " _name = str(h)+\"_\"+str(t)\n", + " for i, t in enumerate(TaskNames):\n", + " _name = str(h) + \"_\" + str(t)\n", " itvs[(h,t)] = mdl2.interval_var(size=Duration[i], name=_name)\n", " TaskNames_ids[_name] = i" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1139,6 +1195,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1147,7 +1204,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 66, "metadata": {}, "outputs": [], "source": [ @@ -1157,6 +1214,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1165,6 +1223,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1172,6 +1231,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1183,15 +1243,16 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 67, "metadata": {}, "outputs": [], "source": [ "for h in Houses:\n", - " mdl2.add( mdl2.span(houses[h], [itvs[(h,t)] for t in TaskNames] ) )" + " mdl2.add(mdl2.span(houses[h], [itvs[(h,t)] for t in TaskNames]))" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1199,6 +1260,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1211,7 +1273,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 68, "metadata": {}, "outputs": [], "source": [ @@ -1219,6 +1281,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1230,6 +1293,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1237,6 +1301,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1249,7 +1314,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 69, "metadata": {}, "outputs": [], "source": [ @@ -1259,6 +1324,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1266,6 +1332,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1279,15 +1346,16 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 70, "metadata": {}, "outputs": [], "source": [ "for w in WorkerNames:\n", - " mdl2.add( mdl2.no_overlap(workers[w], transitionTimes) )" + " mdl2.add(mdl2.no_overlap(workers[w], transitionTimes))" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1297,6 +1365,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1304,6 +1373,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1312,11 +1382,11 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 71, "metadata": {}, "outputs": [], "source": [ - "# create the obj and add it.\n", + "# create the objective and add it to the model\n", "mdl2.add( \n", " mdl2.minimize( \n", " mdl2.sum(Weight[h] * mdl2.max([0, mdl2.end_of(houses[h])-DueDate[h]]) + mdl2.length_of(houses[h]) for h in Houses) \n", @@ -1325,6 +1395,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1332,6 +1403,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1342,7 +1414,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 150, "metadata": { "scrolled": true }, @@ -1366,7 +1438,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 83, "metadata": { "scrolled": false }, @@ -1375,7 +1447,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Cost will be 17065\n" + "Cost will be 18740\n" ] } ], @@ -1388,19 +1460,17 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 84, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -1435,6 +1505,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": true @@ -1458,6 +1529,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1472,6 +1544,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1492,6 +1565,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1500,6 +1574,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1528,6 +1603,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1537,7 +1613,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 86, "metadata": {}, "outputs": [], "source": [ @@ -1546,31 +1622,32 @@ "\n", "mdl3 = CpoModel()\n", "\n", - "NbHouses = 5;\n", + "NbHouses = 5\n", "\n", "WorkerNames = [\"Joe\", \"Jim\" ]\n", "\n", - "TaskNames = [\"masonry\",\"carpentry\",\"plumbing\",\"ceiling\",\"roofing\",\"painting\",\"windows\",\"facade\",\"garden\",\"moving\"]\n", + "TaskNames = [\"masonry\", \"carpentry\", \"plumbing\", \"ceiling\", \"roofing\", \"painting\", \"windows\", \"facade\", \"garden\", \"moving\"]\n", "\n", - "Duration = [35,15,40,15,5,10,5,10,5,5]\n", + "Duration = [35, 15, 40, 15, 5, 10, 5, 10, 5, 5]\n", "\n", - "Worker = {\"masonry\":\"Joe\",\"carpentry\":\"Joe\",\"plumbing\":\"Jim\",\"ceiling\":\"Jim\",\n", - " \"roofing\":\"Joe\",\"painting\":\"Jim\",\"windows\":\"Jim\",\"facade\":\"Joe\",\n", - " \"garden\":\"Joe\",\"moving\":\"Jim\"}\n", + "Worker = {\"masonry\":\"Joe\", \"carpentry\":\"Joe\", \"plumbing\":\"Jim\", \"ceiling\":\"Jim\",\n", + " \"roofing\":\"Joe\", \"painting\":\"Jim\", \"windows\":\"Jim\", \"facade\":\"Joe\",\n", + " \"garden\":\"Joe\", \"moving\":\"Jim\"}\n", "\n", "\n", - "Precedences = { (\"masonry\",\"carpentry\"),(\"masonry\",\"plumbing\"),\n", - " (\"masonry\",\"ceiling\"),(\"carpentry\",\"roofing\"),\n", - " (\"ceiling\",\"painting\"),(\"roofing\",\"windows\"),\n", - " (\"roofing\",\"facade\"),(\"plumbing\",\"facade\"),\n", - " (\"roofing\",\"garden\"),(\"plumbing\",\"garden\"),\n", - " (\"windows\",\"moving\"),(\"facade\",\"moving\"), \n", - " (\"garden\",\"moving\"),(\"painting\",\"moving\") }\n", + "Precedences = {(\"masonry\", \"carpentry\"), (\"masonry\", \"plumbing\"),\n", + " (\"masonry\", \"ceiling\"), (\"carpentry\", \"roofing\"),\n", + " (\"ceiling\", \"painting\"), (\"roofing\", \"windows\"),\n", + " (\"roofing\", \"facade\"), (\"plumbing\", \"facade\"),\n", + " (\"roofing\", \"garden\"), (\"plumbing\", \"garden\"),\n", + " (\"windows\", \"moving\"), (\"facade\", \"moving\"), \n", + " (\"garden\", \"moving\"), (\"painting\", \"moving\") }\n", "\n", "Houses = range(NbHouses)" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1578,6 +1655,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1596,12 +1674,12 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 87, "metadata": {}, "outputs": [], "source": [ - "Breaks ={\n", - " \"Joe\" : [\n", + "Breaks = {\n", + " \"Joe\": [\n", " (5,14),(19,21),(26,28),(33,35),(40,42),(47,49),(54,56),(61,63),\n", " (68,70),(75,77),(82,84),(89,91),(96,98),(103,105),(110,112),(117,119),\n", " (124,133),(138,140),(145,147),(152,154),(159,161),(166,168),(173,175),\n", @@ -1616,7 +1694,7 @@ " (670,672),(677,679),(684,686),(691,693),(698,700),(705,707),(712,714),\n", " (719,721),(726,728)\n", " ],\n", - " \"Jim\" : [\n", + " \"Jim\": [\n", " (5,7),(12,14),(19,21),(26,42),(47,49),(54,56),(61,63),(68,70),(75,77),\n", " (82,84),(89,91),(96,98),(103,105),(110,112),(117,119),(124,126),(131,133),\n", " (138,140),(145,147),(152,154),(159,161),(166,168),(173,175),(180,182),(187,189),\n", @@ -1635,7 +1713,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 88, "metadata": {}, "outputs": [], "source": [ @@ -1645,12 +1723,12 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 89, "metadata": {}, "outputs": [], "source": [ "Calendar = {}\n", - "mymax = max(max(v for k,v in Breaks[w]) for w in WorkerNames)\n", + "mymax = max(max(v for k, v in Breaks[w]) for w in WorkerNames)\n", "for w in WorkerNames:\n", " step = CpoStepFunction()\n", " step.set_value(0, mymax, 100)\n", @@ -1661,6 +1739,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1671,6 +1750,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1679,11 +1759,10 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 91, "metadata": {}, "outputs": [], "source": [ - "#TaskNames_ids = {}\n", "itvs = {}\n", "for h in Houses:\n", " for i,t in enumerate(TaskNames):\n", @@ -1692,6 +1771,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1699,6 +1779,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1707,7 +1788,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 92, "metadata": {}, "outputs": [], "source": [ @@ -1717,6 +1798,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1724,6 +1806,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1735,15 +1818,16 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 93, "metadata": {}, "outputs": [], "source": [ "for w in WorkerNames:\n", - " mdl3.add( mdl3.no_overlap( [itvs[h,t] for h in Houses for t in TaskNames if Worker[t]==w] ) )" + " mdl3.add(mdl3.no_overlap([itvs[h,t] for h in Houses for t in TaskNames if Worker[t]==w]))" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1751,6 +1835,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1758,7 +1843,7 @@ "A task could still be scheduled to start or end in a weekend, but, in this problem, a worker’s tasks cannot start or end during the worker’s days off. \n", "*CP Optimizer* provides the constraints *forbid_start* and *forbid_end* to model these types of constraints.\n", "\n", - "With the constraint *forbid_start*, a constraint is created to specifies that an interval variable must not be scheduled to start at certain times.\n", + "With the constraint *forbid_start*, a constraint is created to specify that an interval variable must not be scheduled to start at certain times.\n", "The constraint takes as parameters an interval variable and a step function. \n", "If the interval variable is present in the solution, then it is constrained to not start at a time when the value of the step function is zero.\n", "\n", @@ -1769,7 +1854,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 94, "metadata": {}, "outputs": [], "source": [ @@ -1780,6 +1865,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": true @@ -1789,6 +1875,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1798,7 +1885,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 95, "metadata": {}, "outputs": [], "source": [ @@ -1806,6 +1893,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1813,6 +1901,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1823,7 +1912,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 151, "metadata": {}, "outputs": [ { @@ -1845,7 +1934,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 97, "metadata": {}, "outputs": [ { @@ -1857,20 +1946,19 @@ }, { "data": { - "image/png": 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\n", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ "if msol3:\n", - " print(\"Cost will be \" + str( msol3.get_objective_values()[0] )) # Allocate tasks to workers\n", + " print(\"Cost will be \" + str(msol3.get_objective_values()[0]))\n", + " # Allocate tasks to workers\n", " tasks = {w : [] for w in WorkerNames}\n", " for k,v in Worker.items():\n", " tasks[v].append(k)\n", @@ -1880,7 +1968,7 @@ " import docplex.cp.utils_visu as visu\n", " import matplotlib.pyplot as plt\n", " %matplotlib inline\n", - " #Change the plot size\n", + " # Change the plot size\n", " from pylab import rcParams\n", " rcParams['figure.figsize'] = 15, 3\n", "\n", @@ -1896,6 +1984,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1914,12 +2003,13 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Problem to be solved\n", "\n", - "The problem consists of assigning start dates to a set of tasks in such a way that the schedule satisfies temporal constraints and minimizes a criterion. The criterion\n", + "The problem consists in assigning start dates to a set of tasks in such a way that the schedule satisfies temporal constraints and minimizes a criterion. The criterion\n", "for this problem is to minimize the overall completion date. Each task requires 200 dollars per day of the task, payable at the start of the task. Every 60 days, starting\n", "at day 0, the amount of 30,000 dollars is added to the cash balance.\n", "\n", @@ -1927,6 +2017,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1947,6 +2038,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": true @@ -1961,11 +2053,13 @@ "| 2 | 90 |\n", "| 3 | 120|\n", "| 4 | 90 |\n", + "\n", "Solving the problem consists of determining starting dates for the tasks such that\n", "the overall completion date is minimized." ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": true @@ -1996,29 +2090,30 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Step 2: Prepare data\n", - "In the related data file, the data provided includes the number of houses (NbHouses), the number of workers (NbWorkers), the names of the tasks (TaskNames), the sizes of the tasks (Duration), the precedence relations (Precedences), and the earliest start dates of the houses (ReleaseDate).\n", + "In the related data file, the data provided includes the number of houses (*NbHouses*), the number of workers (*NbWorkers*), the names of the tasks (*TaskNames*), the sizes of the tasks (*Duration*), the precedence relations (*Precedences*), and the earliest start dates of the houses (*ReleaseDate*).\n", "\n", "As each house has an earliest starting date, the task interval variables are declared to have a start date no earlier than that release date of the associated house. The ending dates of the tasks are not constrained, so the upper value of the range for the variables is maxint." ] }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 98, "metadata": {}, "outputs": [], "source": [ "NbWorkers = 3\n", "NbHouses = 5\n", "\n", - "TaskNames = {\"masonry\",\"carpentry\",\"plumbing\",\n", - " \"ceiling\",\"roofing\",\"painting\",\n", - " \"windows\",\"facade\",\"garden\",\"moving\"}\n", + "TaskNames = {\"masonry\", \"carpentry\", \"plumbing\",\n", + " \"ceiling\", \"roofing\", \"painting\",\n", + " \"windows\", \"facade\", \"garden\", \"moving\"}\n", "\n", "Duration = [35, 15, 40, 15, 5, 10, 5, 10, 5, 5]\n", "\n", @@ -2033,7 +2128,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 99, "metadata": {}, "outputs": [], "source": [ @@ -2041,6 +2136,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": true @@ -2051,7 +2147,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 100, "metadata": {}, "outputs": [], "source": [ @@ -2061,7 +2157,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 101, "metadata": {}, "outputs": [], "source": [ @@ -2070,17 +2166,18 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 102, "metadata": {}, "outputs": [], "source": [ "itvs = {}\n", "for h in Houses:\n", " for i,t in enumerate(TaskNames):\n", - " itvs[h,t] = mdl4.interval_var(start = [ReleaseDate[h], INTERVAL_MAX], size=Duration[i])" + " itvs[h,t] = mdl4.interval_var(start=[ReleaseDate[h], INTERVAL_MAX], size=Duration[i])" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2093,6 +2190,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": true @@ -2102,10 +2200,11 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "A **cumulative function** expression, can be used to model a resource usage function over time. \n", + "A **cumulative function** expression can be used to model a resource usage function over time. \n", "This function can be computed as a sum of interval variable demands on a resource over time.\n", "An interval usually increases the cumulated resource usage function at its start time and decreases it when it releases the resource at its end time (pulse function).\n", "For resources that can be produced and consumed by activities (for instance the contents of an inventory or a tank), the resource level can also be described as a function of time. \n", @@ -2114,6 +2213,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2136,17 +2236,18 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 103, "metadata": {}, "outputs": [], "source": [ "workers_usage = step_at(0, 0)\n", "for h in Houses:\n", " for t in TaskNames:\n", - " workers_usage += mdl4.pulse(itvs[h,t],1)" + " workers_usage += mdl4.pulse(itvs[h,t], 1)" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": true @@ -2156,10 +2257,11 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "A cumulative function *cach* is also used to model the cash budget. \n", + "A cumulative function *cash* is also used to model the cash budget. \n", "To set the initial cash balance of 30,000 dollars and increase the balance by 30,000 every sixty days, the function *step_at()* is used to increment or decrement the cumulative function expression by a fixed amount on a given date.\n", "\n", "Each task requires a cash payment equal to 200 dollars a day for the length of the task, payable at the start of the task. \n", @@ -2168,7 +2270,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 105, "metadata": {}, "outputs": [], "source": [ @@ -2182,6 +2284,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": true @@ -2191,6 +2294,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2199,16 +2303,17 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 109, "metadata": {}, "outputs": [], "source": [ "for h in Houses:\n", " for p in Precedences:\n", - " mdl4.add( mdl4.end_before_start(itvs[h,p[0]], itvs[h,p[1]]) )" + " mdl4.add(mdl4.end_before_start(itvs[h,p[0]], itvs[h,p[1]]))" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": true @@ -2218,6 +2323,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2226,14 +2332,15 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 110, "metadata": {}, "outputs": [], "source": [ - "mdl4.add( workers_usage <= NbWorkers )" + "mdl4.add(workers_usage <= NbWorkers)" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": true @@ -2243,6 +2350,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2251,14 +2359,15 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 111, "metadata": {}, "outputs": [], "source": [ - "mdl4.add( cash >= 0 )" + "mdl4.add(cash >= 0)" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": true @@ -2268,6 +2377,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2277,18 +2387,19 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 112, "metadata": {}, "outputs": [], "source": [ "mdl4.add(\n", " mdl4.minimize( \n", - " mdl4.max( mdl4.end_of(itvs[h,\"moving\"]) for h in Houses)\n", + " mdl4.max(mdl4.end_of(itvs[h, \"moving\"]) for h in Houses)\n", " )\n", ")" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2296,6 +2407,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2306,7 +2418,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 152, "metadata": {}, "outputs": [ { @@ -2328,7 +2440,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 115, "metadata": {}, "outputs": [ { @@ -2340,14 +2452,12 @@ }, { "data": { - "image/png": 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\n", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -2360,7 +2470,7 @@ " %matplotlib inline\n", " #Change the plot size\n", " from pylab import rcParams\n", - " rcParams['figure.figsize'] = 15, 3\n", + " rcParams['figure.figsize'] = 15, 6\n", "\n", " workersF = CpoStepFunction()\n", " cashF = CpoStepFunction()\n", @@ -2387,6 +2497,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": true @@ -2406,6 +2517,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": true @@ -2419,6 +2531,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": true @@ -2441,6 +2554,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": true @@ -2450,6 +2564,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": true @@ -2472,17 +2587,17 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ - "For Jack, if he performs the roofing task or facade task on a house, then he must perform the other task on that house. For Jim, if he performs the garden task or moving task on a house, then he must perform the other task on that house. For\n", - "\n", - "Joe, if he performs the masonry task or carpentry task on a house, then he must perform the other task on that house. Also, if Joe performs the carpentry task or roofing task on a house, then he must perform the other task on that house." + "For Jack, if he performs the roofing task or facade task on a house, then he must perform the other task on that house. For Jim, if he performs the garden task or moving task on a house, then he must perform the other task on that house. For Joe, if he performs the masonry task or carpentry task on a house, then he must perform the other task on that house. Also, if Joe performs the carpentry task or roofing task on a house, then he must perform the other task on that house." ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": true @@ -2512,72 +2627,73 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Step 2: Prepare data\n", - "In the related data file, the data provided includes the number of houses (NbHouses), the names of the workers (Workers), the names of the tasks (Tasks), the sizes of the tasks (Durations), the precedence relations (Precedences), and the overall deadline for the construction of the houses (Deadline).\n", + "In the related data file, the data provided includes the number of houses (*NbHouses*), the names of the workers (*Workers*), the names of the tasks (*Tasks*), the sizes of the tasks (*Durations*), the precedence relations (*Precedences*), and the overall deadline for the construction of the houses (*Deadline*).\n", "\n", - "The data also includes a tupleset, Skills. Each tuple in the set consists of a worker, a task, and the skill level that the worker has for the task. In addition, there is a tupleset, Continuities, which is a set of triples (a pair of tasks and a worker). If one of the two tasks in a pair is performed by the worker for a given house, then the other task in the pair must be performed by the same worker for that house.\n", + "The data also includes a tupleset, *Skills*. Each tuple in the set consists of a worker, a task, and the skill level that the worker has for the task. In addition, there is a tupleset, Continuities, which is a set of triples (a pair of tasks and a worker). If one of the two tasks in a pair is performed by the worker for a given house, then the other task in the pair must be performed by the same worker for that house.\n", "\n", "Two matrices of interval variables are created in this model. \n", "The first, tasks, is indexed on the houses and tasks and must be scheduled in the interval [0..Deadline]. \n", - "The other matrix of interval variables is indexed on the houses and the Skills tupleset. \n", + "The other matrix of interval variables is indexed on the houses and the *Skills* tupleset. \n", "These interval variables are optional and may or may not be present in the solution. \n", "The intervals that are performed will represent which worker performs which task." ] }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 116, "metadata": {}, "outputs": [], "source": [ "NbHouses = 5\n", - "Deadline = 318\n", + "Deadline = 318\n", "\n", "Workers = [\"Joe\", \"Jack\", \"Jim\"]\n", "\n", - "Tasks = [\"masonry\", \"carpentry\", \"plumbing\", \"ceiling\",\"roofing\", \"painting\", \"windows\", \"facade\",\"garden\", \"moving\"]\n", + "Tasks = [\"masonry\", \"carpentry\", \"plumbing\", \"ceiling\", \"roofing\", \"painting\", \"windows\", \"facade\", \"garden\", \"moving\"]\n", "\n", "Durations = [35, 15, 40, 15, 5, 10, 5, 10, 5, 5]" ] }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 119, "metadata": {}, "outputs": [], "source": [ - "Skills = [(\"Joe\",\"masonry\",9),(\"Joe\",\"carpentry\",7),(\"Joe\",\"ceiling\",5),(\"Joe\",\"roofing\",6), \n", - " (\"Joe\",\"windows\",8),(\"Joe\",\"facade\",5),(\"Joe\",\"garden\",5),(\"Joe\",\"moving\",6),\n", - " (\"Jack\",\"masonry\",5),(\"Jack\",\"plumbing\",7),(\"Jack\",\"ceiling\",8),(\"Jack\",\"roofing\",7),\n", - " (\"Jack\",\"painting\",9),(\"Jack\",\"facade\",5),(\"Jack\",\"garden\",5),(\"Jim\",\"carpentry\",5),\n", - " (\"Jim\",\"painting\",6),(\"Jim\",\"windows\",5),(\"Jim\",\"garden\",9),(\"Jim\",\"moving\",8)]" + "Skills = [(\"Joe\", \"masonry\", 9), (\"Joe\", \"carpentry\", 7), (\"Joe\", \"ceiling\", 5), (\"Joe\", \"roofing\", 6), \n", + " (\"Joe\", \"windows\", 8), (\"Joe\", \"facade\", 5), (\"Joe\", \"garden\", 5), (\"Joe\", \"moving\", 6),\n", + " (\"Jack\", \"masonry\", 5), (\"Jack\", \"plumbing\", 7), (\"Jack\", \"ceiling\", 8), (\"Jack\", \"roofing\", 7),\n", + " (\"Jack\", \"painting\", 9), (\"Jack\", \"facade\", 5), (\"Jack\", \"garden\", 5), (\"Jim\", \"carpentry\", 5),\n", + " (\"Jim\", \"painting\", 6), (\"Jim\", \"windows\", 5), (\"Jim\", \"garden\", 9), (\"Jim\", \"moving\", 8)]" ] }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 120, "metadata": {}, "outputs": [], "source": [ - "Precedences = [(\"masonry\",\"carpentry\"),(\"masonry\",\"plumbing\"),(\"masonry\",\"ceiling\"),\n", - " (\"carpentry\",\"roofing\"),(\"ceiling\",\"painting\"),(\"roofing\",\"windows\"),\n", - " (\"roofing\",\"facade\"),(\"plumbing\",\"facade\"),(\"roofing\",\"garden\"),\n", - " (\"plumbing\",\"garden\"),(\"windows\",\"moving\"),(\"facade\",\"moving\"),\n", - " (\"garden\",\"moving\"),(\"painting\",\"moving\")\n", + "Precedences = [(\"masonry\", \"carpentry\"), (\"masonry\", \"plumbing\"), (\"masonry\", \"ceiling\"),\n", + " (\"carpentry\", \"roofing\"), (\"ceiling\", \"painting\"), (\"roofing\", \"windows\"),\n", + " (\"roofing\", \"facade\"), (\"plumbing\", \"facade\"), (\"roofing\", \"garden\"),\n", + " (\"plumbing\", \"garden\"), (\"windows\", \"moving\"), (\"facade\", \"moving\"),\n", + " (\"garden\", \"moving\"), (\"painting\", \"moving\")\n", " ]\n", " \n", - "Continuities = [(\"Joe\",\"masonry\",\"carpentry\"),(\"Jack\",\"roofing\",\"facade\"), \n", - " (\"Joe\",\"carpentry\", \"roofing\"),(\"Jim\",\"garden\",\"moving\")]" + "Continuities = [(\"Joe\", \"masonry\", \"carpentry\"), (\"Jack\", \"roofing\", \"facade\"), \n", + " (\"Joe\", \"carpentry\", \"roofing\"), (\"Jim\", \"garden\", \"moving\")]" ] }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 121, "metadata": { "scrolled": true }, @@ -2588,6 +2704,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": true @@ -2598,7 +2715,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 122, "metadata": {}, "outputs": [], "source": [ @@ -2608,7 +2725,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 123, "metadata": {}, "outputs": [], "source": [ @@ -2617,7 +2734,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 124, "metadata": {}, "outputs": [], "source": [ @@ -2631,6 +2748,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": true @@ -2640,6 +2758,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2648,16 +2767,17 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 125, "metadata": {}, "outputs": [], "source": [ "for h in Houses:\n", " for p in Precedences:\n", - " mdl5.add( mdl5.end_before_start(tasks[h,p[0]], tasks[h,p[1]]) )" + " mdl5.add(mdl5.end_before_start(tasks[h,p[0]], tasks[h,p[1]]))" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": true @@ -2667,10 +2787,11 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "the specialized constraint *alternative()* is used to constrain the solution so that exactly one of the interval variables tasks associated with a given task of a given house is to be present in the solution, \n", + "The specialized constraint *alternative()* is used to constrain the solution so that exactly one of the interval variables tasks associated with a given task of a given house is to be present in the solution.\n", "\n", "The constraint *alternative()* creates a constraint between an interval and a set of intervals that specifies that if the given interval is present in the solution, then exactly one interval variable of the set is present in the solution.\n", "\n", @@ -2679,7 +2800,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 129, "metadata": {}, "outputs": [], "source": [ @@ -2689,6 +2810,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2700,7 +2822,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 130, "metadata": {}, "outputs": [], "source": [ @@ -2718,6 +2840,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": true @@ -2727,6 +2850,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2735,15 +2859,16 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 131, "metadata": {}, "outputs": [], "source": [ "for w in Workers:\n", - " mdl5.add( mdl5.no_overlap([wtasks[h,s] for h in Houses for s in Skills if s[0]==w]) )" + " mdl5.add(mdl5.no_overlap([wtasks[h,s] for h in Houses for s in Skills if s[0]==w]))" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": true @@ -2753,6 +2878,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2762,18 +2888,19 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 132, "metadata": {}, "outputs": [], "source": [ "mdl5.add(\n", " mdl5.maximize(\n", - " mdl5.sum( s[2] * mdl5.presence_of(wtasks[h,s]) for h in Houses for s in Skills)\n", + " mdl5.sum(s[2] * mdl5.presence_of(wtasks[h,s]) for h in Houses for s in Skills)\n", " )\n", ")" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2781,6 +2908,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -2789,7 +2917,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 153, "metadata": {}, "outputs": [ { @@ -2811,7 +2939,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 134, "metadata": {}, "outputs": [ { @@ -2823,20 +2951,18 @@ }, { 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ "if msol5:\n", - " print(\"Cost will be \"+str( msol5.get_objective_values()[0] ))\n", + " print(\"Cost will be \" + str( msol5.get_objective_values()[0]))\n", "\n", " worker_idx = {w : i for i,w in enumerate(Workers)}\n", " worker_tasks = [[] for w in range(nbWorkers)] # Tasks assigned to a given worker\n", @@ -2873,6 +2999,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": true @@ -2890,6 +3017,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": true @@ -2903,6 +3031,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": true @@ -2928,6 +3057,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": true @@ -2958,20 +3088,21 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Step 2: Prepare data\n", - "In the related data, the data provided includes the number of houses (NbHouses), the number of workers (NbWorkers), the names of the tasks (TaskNames), the sizes of the tasks (Duration), the precedence relations (Precedences), and the cleanliness state of each task (AllStates).\n", + "In the related data, the data provided includes the number of houses (*NbHouses*), the number of workers (*NbWorkers*), the names of the tasks (*TaskNames*), the sizes of the tasks (*Duration*), the precedence relations (*Precedences*), and the cleanliness state of each task (*AllStates*).\n", "\n", "Each house has a list of tasks that must be scheduled. The duration, or size, of each task *t* is *Duration[t]*. Using this information, a matrix task of interval variables can be built." ] }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 135, "metadata": {}, "outputs": [], "source": [ @@ -2979,24 +3110,24 @@ "NbWorkers = 2\n", "AllStates = [\"clean\", \"dirty\"]\n", "\n", - "TaskNames = [\"masonry\",\"carpentry\", \"plumbing\", \"ceiling\",\"roofing\",\"painting\",\"windows\",\"facade\",\"garden\",\"moving\"]\n", + "TaskNames = [\"masonry\", \"carpentry\", \"plumbing\", \"ceiling\", \"roofing\", \"painting\", \"windows\", \"facade\", \"garden\", \"moving\"]\n", "\n", - "Duration = [35,15,40,15,5,10,5,10,5,5]\n", + "Duration = [35, 15, 40, 15, 5, 10, 5, 10, 5, 5]\n", "\n", - "States = [(\"masonry\",\"dirty\"),(\"carpentry\",\"dirty\"),(\"plumbing\",\"clean\"),\n", - " (\"ceiling\",\"clean\"),(\"roofing\",\"dirty\"),(\"painting\",\"clean\"),\n", - " (\"windows\",\"dirty\")]\n", + "States = [(\"masonry\", \"dirty\"), (\"carpentry\", \"dirty\"), (\"plumbing\", \"clean\"),\n", + " (\"ceiling\", \"clean\"), (\"roofing\", \"dirty\"), (\"painting\", \"clean\"),\n", + " (\"windows\", \"dirty\")]\n", "\n", - "Precedences = [(\"masonry\",\"carpentry\"),(\"masonry\",\"plumbing\"),(\"masonry\",\"ceiling\"),\n", - " (\"carpentry\",\"roofing\"),(\"ceiling\",\"painting\"),(\"roofing\",\"windows\"),\n", - " (\"roofing\",\"facade\"),(\"plumbing\",\"facade\"),(\"roofing\",\"garden\"),\n", - " (\"plumbing\",\"garden\"),(\"windows\",\"moving\"),(\"facade\",\"moving\"),\n", - " (\"garden\",\"moving\"),(\"painting\",\"moving\")]" + "Precedences = [(\"masonry\", \"carpentry\"), (\"masonry\", \"plumbing\"), (\"masonry\", \"ceiling\"),\n", + " (\"carpentry\", \"roofing\"), (\"ceiling\", \"painting\"), (\"roofing\", \"windows\"),\n", + " (\"roofing\", \"facade\"), (\"plumbing\", \"facade\"), (\"roofing\", \"garden\"),\n", + " (\"plumbing\", \"garden\"), (\"windows\", \"moving\"), (\"facade\", \"moving\"),\n", + " (\"garden\", \"moving\"), (\"painting\", \"moving\")]" ] }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 136, "metadata": {}, "outputs": [], "source": [ @@ -3004,6 +3135,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": true @@ -3014,7 +3146,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 137, "metadata": {}, "outputs": [], "source": [ @@ -3024,7 +3156,7 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 138, "metadata": {}, "outputs": [], "source": [ @@ -3033,7 +3165,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 139, "metadata": {}, "outputs": [], "source": [ @@ -3044,6 +3176,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": true @@ -3053,18 +3186,19 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "As in the example Chapter 5, “Using cumulative functions in the house building problem”, each task requires one worker from the start to the end of the task interval. To represent the fact that a worker is required for the task, a cumulative function expression *workers* is created. \n", "This function is constrained to not exceed the number of workers at any point in time.\n", - "he function pulse adjusts the expression by a given amount on the interval. \n", + "The function *pulse* adjusts the expression by a given amount on the interval. \n", "Summing these pulse atoms over all the interval variables results in an expression that represents worker usage over the entire time frame for building the houses." ] }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 140, "metadata": {}, "outputs": [], "source": [ @@ -3075,6 +3209,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": true @@ -3084,6 +3219,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -3093,16 +3229,16 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 141, "metadata": {}, "outputs": [], "source": [ - "Index = {s : i for i,s in enumerate(AllStates)}" + "Index = {s: i for i, s in enumerate(AllStates)}" ] }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 142, "metadata": {}, "outputs": [], "source": [ @@ -3112,6 +3248,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": true @@ -3121,11 +3258,12 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Certain tasks require the house to be clean, and other tasks cause the house to be dirty. \n", - "To model the possible states of the house, the state function *function* is used to represent the disjoint states through time.\n", + "To model the possible states of the house, the function *state_function* is used to represent the disjoint states through time.\n", "\n", "A state function is a function describing the evolution of a given feature of the environment. \n", "The possible evolution of this feature is constrained by interval variables of the problem. \n", @@ -3136,14 +3274,15 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 143, "metadata": {}, "outputs": [], "source": [ - "state = { h : state_function(ttime, name=\"house\"+str(h)) for h in Houses}" + "state = {h: state_function(ttime, name=\"house\"+str(h)) for h in Houses}" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": true @@ -3153,10 +3292,11 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "To model the state required or imposed by a task, a constraint is created to specifies the state of the house throughout the interval variable representing that task.\n", + "To model the state required or imposed by a task, a constraint is created to specify the state of the house throughout the interval variable representing that task.\n", "\n", "The constraint *always_equal()*, specifies the value of a state function over the interval variable.\n", "The constraint takes as parameters a state function, an interval variable, and a state value.\n", @@ -3167,21 +3307,22 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 144, "metadata": {}, "outputs": [], "source": [ "for h in Houses:\n", " for p in Precedences:\n", - " mdl6.add( mdl6.end_before_start(task[h,p[0]], task[h,p[1]]) )\n", + " mdl6.add(mdl6.end_before_start(task[h,p[0]], task[h,p[1]]))\n", "\n", " for s in States:\n", - " mdl6.add( mdl6.always_equal(state[h], task[h,s[0]], Index[s[1]]) )\n", + " mdl6.add(mdl6.always_equal(state[h], task[h,s[0]], Index[s[1]]))\n", "\n", - "mdl6.add( workers <= NbWorkers )" + "mdl6.add(workers <= NbWorkers)" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": true @@ -3191,6 +3332,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": true @@ -3201,14 +3343,15 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 145, "metadata": {}, "outputs": [], "source": [ - "mdl6.add(mdl6.minimize( mdl6.max( mdl6.end_of(task[h,\"moving\"]) for h in Houses )))" + "mdl6.add(mdl6.minimize(mdl6.max(mdl6.end_of(task[h,\"moving\"]) for h in Houses)))" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -3216,6 +3359,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": true @@ -3227,7 +3371,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 154, "metadata": {}, "outputs": [ { @@ -3249,7 +3393,7 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 147, "metadata": {}, "outputs": [ { @@ -3261,14 +3405,12 @@ }, { "data": { - "image/png": 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8km9mY2NtgV5vxNpKTVpGLg521ijkMhrV9WL2J/0QBIEJc9aybscJ1CoFbpXsydUU8tvWo6z+8yhJaVkYDEbUVrb07t2bixcvYmtry7Vr16hZsyZ9+vRhzpw5JXVTFEWWL19OWloaEyZMQK/Xo9VqUalUeHh4MGbMGMaOHUu3bt3Ys2cPxcXFuLm50bVrV1555RV+//13Tp48eU8dvnLlCgsWLECtVuPv71/SfoxGI2PGjCEpKYn3338fX19fCnRr75mHmExG/lr/HbFnTyAg0LRNT1p1ep09fy7l/OkQ9Dotnj71ad/tbZbO+xDXyp7ExhxHZWGJT51GZKYnMXN8L/oNnUrksV1EntiJXlfMIhd7ls8chrWlitb9p9OjYzBnLyXi71OFY5GXcHdx4HpSBr06N+ajIS/R6o0v0BpEDMYZWFnb063vKNYsmUJg4w5ciD4KCBgMOmSCDEEup1GLl3mx5zBmTngNTHqMM7TYOKh4f3ZjfpoQzrS1bVn1VTTJ1/K5majBvYYN3nUdqexpS7chtcnIyEAQhNlAO8ABGCKKYoggCFbASqAOcAHwBEaKohh+v76uvD5BppUnnETFyUtKo9O00Th6DWHLsM+5su8Y3X+YwvXQCKJW/0m7Se/xynefI1PISQw/y8kl6+k8YwwX/txP3d4v4tu5BUa9AdFkIj02jtgdh+m5aBqI8Md7k6ncwA+1rTW5Sam0nzKS1p8MZd+UhcQdPolv55YA6DSFvPLd5wDkp2Zw4/hpPFs14ur+E3i1bvz/UgFym8ybiQwaOQu3tz9lwbRBRJ7YzahJP3Mu6gj7tq2g/7CpjJy4BLlcwaVzYezY+CNvf/A1xw5splWnNwhu3hWDQY9oMpIQf4GTodsYPXkliCILvngb7zrBWFnZkpGWwID3ZtD3nc9Y9cNEosMPENzcrCwoKsxn5MQlAGRnJHP+TCj1gtsSFbaH+o3aPXEFSGrSVfZtW86oSctQ5J2kR5ACAYFtSz5GEATWbDvGj7/tY8oHvQCIjk1gy09jsVSrWLfjBKcvXGf/qk+xtFDx8rtz6NA8ACsLNVv3R/LHTx+hVMiZOHcdm/ecok/XphQW6WgY4MWE4d2Z8eMf/Lb1GGPefpFOLeuVDDJuk5dfxKbvxwCQkJrJ/mNnAc97yubuZ7CxdaBQk8vm1XNo3aU/3rUakJ2ZypK5HzB+5oZyl4u1jQMfTVvN0f0bOLRrNa+/8xl7/lyKr18jOnQbzMXoY5w4VLb5fVkkXs7lz0UXmPJ7O+wc1WhydKycHkXXt32pE+xMRnIhs94NYe6OLuVO09ZRzVebO7J3zVW2L7/EsBmN2PT9eQKautJjeB3OhKRyYH1cudPLiksk6tc/6fHDFCwcbCnO03D025XU79sV9/q10aRlsGPcbPqunlPuNC3sbXlt2Zec27KXM2u302b8UCJWbMajoT9BA3qQEHaGi9tKmyjfLUftmExcOoSBAF+sa48gCBzcEMe2n2MZMCEQgLhz2Uxd0w6VhZzDm+O5GpPN7K2dUFsq+KzPfoLauKO2UnB8RyJT17RDoZSxfFokodtu0LpnDbSFRnwCnXh9bF3WzInmwIY4Xh3hR3A7j5LJ820K8/VMXt2WxMu57Fh5iZeH1KKa8VXCzqZjZWNPr4GfIAgCJw7/wcEdq+jebywAifEXGTVpKUqVBSdDtnHj2jn+9+VaVCoLvp02CP/AFqjUlpwO28sHk5YhVyjYtGoWkcd30ajFy+i0RdSoWZeXer/PtnULOXH4Dzp1H0JAg1YlyrvblNWn+LrD8e0JNOlc5akrQG6TEpfPsC+Dqd3QmcWfhrN3zdVyxy3I0zHpl9ZEHEhh7oijTFnTjqEzgvms937iL+Tg6eeAttCIZ4AjAyYEsvmH82z6/vw9ikR4/O3maRN7LYWFq3bz508f4eRgQ3ZeAZ/NX8/Qvu1pEliTpNQs+o/7gcO/fV7uNJ3srdm9fAIrNx9h0e/7mTvhTeav2EmL4Fp8MLALB0+c57etR5/gU/1zcm+k0Gb8UNzr1ebQrCWc27Kv3HG1+QV0+3YS10Mj2D1hLj1+mILjJ++yZdjnZFyOx9nXE0ORFudaXrwwagARKzcTsWLzPUpgeLQ+79/C9evXWb9+PbNnz8be3p78/HwWLVpEjx49CAgI4ObNm0yZMoWffvqp3Gna2dmxYMECtm/fzpYtWxg9ejS///479evXp0+fPkRERLB7924AxowZg7+/PwsWLGDHjh00a9aMMWPMY4B58+Zx6tQpmjS59yjOsvLo2rVridID4MyZM6XiaDQaZs+eTWFhIQsXLmTHjh3IZDK6tQvi60/6MeiTRRw4ca7MZyqrvQzs0RJrSzUvtwvihb5TadWoNu2a+jFn6XYCfKrQwK8G568m07ieNzMXbePqtVScnJyoU6cOR48eRaVSkZGRgYODAx06dODIkSMl+SUnJ9O5c2eUSiVarRadScWeP35Gr9diY+uIhaU1Dk6uZKWnUKDJ4ca181haWiOaTPQZ/BmbVs1Eq9ViZ2PFzbOXKcjIxtqtEhhNWLs4kXM9GdcAH04tXY9oMtFr2ZfIFAo2vzuJ6OhoOnbsyJo1a3B1dcXS0pLU1FTUajUajYbMzEwyMjIAGD9+PEuXLsXW1hY7a0tOnrnCL3+EUNXdkaruTgAUFutoEliTz7/diLuLPWe2zaRhz0kgigQFBdGguhI7G0squzow9svVnIq5yuqtR4lPSmfD92M4dOI8OXkFTBjenczsfNRqJTZWFnww/Rf2Hj2Lm7Md63eeYMWs4TSq50XjXp+jVino2iYQURTJyNag1em5nC5jz549uLi4kJeXx/Tp05HL5SxevBh/f3/69evHqVOn2L17N++88w6nT5+mWrVqvPTSSzRp0oTFixdjaWnJqlWrKC4upqCggICAANzc3LCxsaFt27YAdOvWjX79+t1ThxcsWMCwYcOoV68ey5cvL/mt9+7dS8uWLSksLOTrr79mwoQJvD3mB1zcq5eah5w4tIWs9GQ+mrYauVxBoSYXgJYd+tK5h3lRZc3iyVw+f4rMm4mIoomh475l29qF3EyJx8nZg5d6j2TTL7NQKNW06tyfFkFV+GXp97w/ZTm/zn0fo8mEV1UXvp00kLYDvkQEti/9H+9PWUFoZCwiZuWaf00/6rV4k/UrZhC6bz0m0USBJo/AJp24FhtFoSaXwoI8fHwbc+VCOLtEUKut8K/jTduhcpZ8FsHmny4S3MGDVV+dwdpOxdDpDVn11Rmunc3Gu67j35ugQhTFJoIgvARMAToC7wPZoijWFwShLvDQPXkPnF0JgrANEO93XxTF7g/LQOLB2Lq74FTTfAKxo1dVqjQMQBAEnGpWR5Oajk5TxKGvFpObaF7NNhnNxxe5BfgQ9eufFKRn4dW6MfbV3EmNjsWrdWOUlhYAeLZuTGr0RWq0CMbW3QVnX08AnGt5kZ9yZzeTd/tmJZ/rdGvLmTV/4dmqEbE7D9P6f+8+jWJ4Zji5eFC5mvm8b7cq3vj6NUYQBCpXrUl2RjJFhRp+XzqNjLQbIAgYDQYAPH3qsW/bcnKzb1IvuB0u7tWJu3Saeg3bolZbAlAvuB1xl6IIaNAaJxcPqtSoDUBVzzpkZdzxfdGgSaeSz03b9OTgjlXUC27LqZC/6DP40ydeBpfPh1O/UQdsbB0ozgNHO2suXE1ixJTl3MzMRac3Ur1ypZLwnVvWw1KtKvneqlEdnOzNK4Nd2wRyMvoaCrmMmNgbvPTu1wAUa/U4O5rDqJQKOrWoC0C92tUIecA+2+4dGpZ87t+tOT+u2Uebrp73lM3dzwBgZWPP5XMnSbvLV0JxUQHFReXf81q/UTsAqnr6ERNxEIC4S6d5e7RZAVCnfnMsre/dJnE/zp1Ip0mXqiUryzYOKs4ev0nS1Ts+J4o0eoo0+nKn2bhzFQC8Ahw4uTcJgEuRGYz9vjkAga3csbYvv3lpcuQ5vNo2KVmdtLCzISn8HDnxSSVhdIVF6AqLyp2mV2vzKr1LbS/ij5wCIC0mlk4zzMqBak0D7zHDLS1HJjYOKm7E5vLd2BPkpBdj0JtwqXonTnB7D1QWd0xY6zZ3xfZWOTfuVIXYyExkcoG4c9l83me/+TmKjdg5mcMolDIatjNvw/IKcHyg/4gXulYDzL9ncAcPTu1JoloHOHNyP227DmDJ3A/Iy83AaNDj5HxnS5N/UGuUKouS77UCmmJtY66v5r7iNDK5gsTrF/l22lsAtwa45oGjXKHEv4HZBLWqZx0unTt5XxnL6lN8e3fh8OZ4hk6/dwvT06JSZUtqN3QGoGX36uz69Uq54zZs54EgCFSrZYedswXVa9sDUNXXjvSkAjz9HBBk8EJXs8KqRffqfPvB8TLTetzt5mlzNDKWl9s2KLHIcLSzJiQ8lkvxqSVhNAXFaAqLy51m1zYNAKhfuzo7D5sniyejr7LsK/Ngul0zfxxsrR7XIzwRrF0r4V7P/J717dSCs5t2lztujeYNS8Zelo72pcZlmtQMnH09EWQCNW+Nl3w7t2TvZ9+Umdaj9Hn/FqKjo2nevDn29ub2ZWtry5kzZ0hISCgJU1hYWKFjQps3N7crHx8fjh83t8nz58/z6afmd3hwcDBWVlZYWlqWrIi3bduWbdu2UVxczIYNG9BqteTn51O9evUylSBl5fEw7O3t8ff3Jzs7G0EQOH/+PImJidxQGOjw1pfk5BXi4lS2pU5Z7eVuqro74eHqSGJqFi8E+ZKclkW3dkHUqenB6QvXuZ50Ex8fHy5evMjgwYO5cOECKpUKpVJJdnY23t7epZQgbm5uODo64uzsTFpaGqFHT2BpbY82uxDHSm5UcqtGWtI11BaW6HTF5GSm0rjHuyQnXKZ+o3Yc3v0bKTcuIlcrS97f7vVqE3/4JI3e7UNS+Fnsq7ijKyxGkMmID4nAs1Uwlk72ZGZmUqVKFYxGI1WrVsXe3p6kpCTUajW5ubmkpKTQtGlTEhISiI+Px87ODicnJ2xtICTiEhnZGt5+tRVHwmPp1i6Inzccwqe6G3maItKz8pi3bDuIIh5uTqSmpnIh6gLnryRhb2uFTC6joEjH4bDzyOUy6nhVJie3gHEzV6NUyLGyUrP9QBRFWh05eYXU9qxMbFwKSoWCtk3N26A6tajLkVMXib2Wwqwl29AUmPtFhVKFra1ticLi9jaYc+fOldTNxo0bY2Nj7md1Oh3x8fEsXbqUb7/9Fp1Oh7W1NTY2NigUCnJycujatSsajYaoqChsbc11Jzo6ms2bN5eqwwEBAWg0GurVM5870q5dOyIiIgCIiooiPj6erKwsJk+eTFFREWt/nkZxkabUPOTSuZO80O61ki2vVjbmNnvlQgQHd65CpyumUJOHrYMzjpXcMeh1+Pg1xq2KN24enkQe30XlqjXJykhBr9dSXKQh+kQxSsFEjqYITUEx7s4OhMfEIZPJEARo38w8R3V2siX3TCEno6/h4mhLQEAAvv6NzdatmanYO7iQfCOWDt0GEXfpNAFBrUm6EYuVtR1pSVeJu3waWzsnAgICCGiWhUFn5GaChj4f+DN/1HE+mN+U4zsSaPuaJwa9qawmuPnW/wjMFh8ALYEFAKIonhUEIbrMxnsXD1tinnvrfy/AHVh963s/IP5hiT+PtHUt/8ruPyVdV0CIVVFJnhctE6jvbKCp603SdQWECjkk/jaP1q0cePGtZqQnFjD9rcO0dd1C2zchrVU9og5dYOf4bQybEYyPTS4ao64kvZvWF7GzURNcKaNUPhq7WIoLDbR1hRBlOs0qH8Pb9dZEtAOcXngVt/gl2Mgy6dUsHLivJdFjJ44g3vB5Os4pE6wz2WhlKMnvhH0mbapdp5tPGAnWmWxSaIjdN4O+raoxpE9vElIy6f3BAt7wCeMNH0fi27/F/mPn+HnBMOZM6E+QczI5qoKS9G44JVPJIY/OnqdL5ZPjkkhBkZY3fMLYaJlHN5+rBPqYOzV8YP/aq9TIX42jKo+xbTOBzCdaDgVR8WTI83nDJ4yVSRCZXYOJcxbRs2dvmjZtSkxMDGvWrCEyuwYpxQ5YWFgQmV0DgOsFl8nW25R8Tymyp0BphyAItGrXmUGDBpXKKzIbZHIFUTlZVoUWAAAgAElEQVSe5jIqSiSt8DqR2TXI1FoTV+BSkla+wYIb+hqob32XV6vBpcQtVI+Lw2QyUrmqz10piwiCUCovk2hi9OfLS008K4JcYVb0yGSyEuXjfTXC5UAURf4mIiaTyLS17Usm8Nnn3bG0MU9kDCnVsb08CHXWGtSFFthe7oUiLwvL1CbYXm6BzHAEh4R+2ObZY5N0GSE/HdvLgxCKw7GO64VtkTsAgnEXNldfx/bWoPYOZbwfRBHhnksmevw0DcVdiq+KIFeaJ5OCTIbJaLqdzYMpQ45fZkTx0uBaBLf34HzYTTZ9f77kntqy9B5e4e+xBUCE1j1r8Ma4evwduVIoqT8ymYDJcH8Bb+cliiLOla2IvJhC3K06Gbp3HcFtAnEY0JHkqPNErNhMrPclMhwzUVpaEOt9CYDU2FTyEvNLvmc6ZqGx0yHIBLxffoEmw98olWcslxCUMi7VvGyOfyONnLgsYr0vkWebR7JbMha30iq0LCLFOx3Dre94W5KyNv6WjCLVatkTv60dapd7JxJPkqz0ZEy6KFJOjAcg8/wptNnr0RXkkXF2EJYF/mDaUXI//fwOCtNPknJiPIXpU9HEtyLlRAey0pMRDJdKwhVnTCXzQitSbDqAuJmUsP8hlyvIvJmIvvBaSfzsy61IceiAUXuUzOgxaG0dyIo7T3HWAlJOjEdXcIabUcMxJpoVJCbDHtLCR1NQzq1uT449JZ/Wn9Lj4+PDjQJHcrWykr4SQGcU+GKW2bT6Npe0cL0ggXRt9p3+G4t7+ludScHFAk9SFPZcLtCRo1USmV2DQoOSs7lVyLA09yUGUcaZnKrYi/fvS5pEJ9C1wdPfLpMgy+WIYGKKzLwdKFSWzwpBTy56hsryCJRl8xuU3N+kzyFE0DFFlk22oKOjWks3WTYJ8jyiVLKScNkyPR1NeXSTZfMz8LksG4VMznUhl5hb+WULOjoKBXSTZbMDExPUBTjJBM4oNKQZtUyRZRMiGvlQlkt1mbk/XI/I/2Q5OMkMT72sbrPyrs8xF2+gdmnCzQJbNMUGLmW6l9zTG2H4xFWl3qWJRZCqOUdhfhYNHa9z0CIHa0s1DR2vU0ldgJd1Og0dr6OSGQh2TsXJQYPcIQ0roYCYizcoNiiIy3EhT2nOxyjKMJjkJfkm5iVQoLdk69atfPfdd7i4uLBmzRr0+rIXCRS3rEJlMhnGW+/r25yMjyUtsDKxai1KCwGlfR7ZciM6OWyzz6NYLKRQNJCmMpKamsp3k9+iR4dgPp73J/sOn+D3JDgjqsgWrYkwOZKDjIVyZyxMtqQL2Vw1yphmciRctEQpWnDFZI/GwpJDoiX5ogoQ0aDkF4UTmSYN6ViQZwIPQUCtVhMREUHlymYl/JUrV9DptNg2OoVptYaiygcwWqUgt9GhdTqNhZUCeUYVLKxs0GkLkMuVOLlUITsjhZysm1ha2fytXG69fwUBCwsLCjNzMeoN3H5LCkYjlkl7EARo7rKdSCcNJlc1Db3j2T1rHzJdMSqVmoSEBERRRCaT3fJzYqRQn41CJWCwSsS5kRzXk5ZY+8WSuOU6crmcxr7ehJy6iF5voGkDH7JyC5j922nkCgvWXmlKgbwqnfsOIaFQQ3rOXgwyW4pOnKBvx/qkpufSpkkddh45Aw7unEpKQFW1MtNMjlC/MS2+8yEs5BTHFvxK0/f707hvV8KXb2K3FmQKOVpBMIfF/NtpkDH4yzXY1vSkWf9uFGZmk7frFF988QXffPMNFhZ36rZWX0CB12byby20iHItmprrEOVamr7ixs2zdjRq1Iju3buTlpbGp59+WlL/BEEoVQd1Oh2LFi1i/vz5peqweTwoEJt8FI82Vyg05WBS55Dv+wt66+sMnObNsik5fLoukMXvJ1LDvz2tOr1BVnoyP856D3vNfqpaZ9GuykVa3zUULtbqmTl2Bjt+/oQqbo7MW7ad3PwbJFoZKCrWkZN/mHy7fBSuOWhVOuJqXMcoGLFydaT9/A+pd7WQt9vY06jXZyCAnY0l8UnpzDuSQXxaDlX61WWayZEw0YI8k4BBlCFDQbMSPyoCotGEpa0d2emJOMoTyMtOpVlQd/5KOouduhgZBtRyPUolpfyvGA0ivkGVKC4wUpCnI3xfMj3f82Pf2jIdW2tvR+OOLuPvw8aH8kCbWFEUD4uieBgIEkXxdVEUt936649Z4yLxhCnU6HFyM1sWHN4SX3I9LUGDazVrXnzLl+D2lbkRm0udRs6E70tGW2SguNBA+L5k6jRyrnCerXrU4LtxYbTp5fmYnuL5JV9ThLuLecC3fseJkuvXkzKo4eHMkD5t6dSyHheuJtMs0IfdIdEUFesoLNKy68gZmgZW3JlP7xebMHLqCvq+1OzhgR8DLYNrs+1AJFm5Zg/f+fn5FBYWUqmS2fpj//79D4x/+vRp8vPz0Wq1hIWF4efnR2BgIEePHiUnJ6ckzYc5d7KysqKo6MEWBu3bt2fdunU0afVKqeu+/o05fXIfBRpzfoWaXGrXbUbovjvbX5Kuxz4w7fLg5RvImZNmE+vYsycoKij/ySF1X3DlxM5E8rPNfbcmR0f9Fm7s/u3Oavj5/Xcm3xVZabsbf39/QkNDAYiMjKyQ53aP4LpcOxhGca7Zs3xxnoaqjetxbvOdiVjG5fhHkutu3OvV4tpBs1Iw8WT0PV7p/y6HJkdHocaAo6u5L3yYl/CYY2locnToio2E70umdlAlAl5wJWxPErmZxSVppic92DLIwlpBUUHZk5Tbv2eTzlVYt24dDZp0pKhIg7PCLOOlXSEPTDspPIbiPA0GrY74kHDc69WiSnAA1w6dpCjbbNZanKchP/XBPsiVVpboH7LiX6tLS9atW1fSp4u6nAeGf1JkZ6YSf8U8YY46sRsv38BS923snEhLjsNkMhETcajC6YuiiehT5m0GkSd24+XboNxx/0nbflrc7hMCAwMJDQ0lL88sY35+PkFBQWzffsfr/rVr//xElEfpS9JyjQ8N86RISssm/Kz5uf/cF0Hj+qXfvy5OtlyOT8VkMrHryL2r9w/DZBLZfigKgC17w2lS/15nxPejSX1vth0wxz188gI5+Y/Wvz8pbvcJFX2XFmrLXKV9aF5/b2/aIg252TfL7B/s7OwoKiri6NGKbceytLSkqKgIizxtmfeLsnJJO2tWLBuKtbjXq4VcLick/CIFhVr2HIkkIzsfm9TyvUNVVhbo/zaGsXF1JuX0hVv++0xc3X8MtwBfZEol8fHxVK9enf3795OZmUnt2rXJzs5GqVTds6jzdxwrVaawIA8rKxvcq9Yk7tJplCo1IqDXFePo7M65SLN/wPTU6+RkplKpUiVunr+CrbsLJqOB68eicK7kwv61cSXTx8qeNhQXGGjf15uG7TwoKjDg5OTErl27UCqVXLx4kaioKFQqNQW5OpRqGT4NKnF02w1kchl+jV1QqmRotVrkMhlV3Z0o1uoRTSJNAmsSdvwIKrVZ4VC5ak0uRh+jWdtXUanU5OeZTwkzW9E48Oe+CFRKBTU9qqG5mYmuoBiTwUh+ajqCTIZPxxYo1CryktLQFxYTd9hsHVm1cT1MegNJEefQFxaTcNy8K0JXVES1poGc27yXy3vMdSkpKekepVmNajU5uu0GAKePpFCQa1a8qdRyIg+mUlBQQKVKlcjPz2fr1q0l8YKCgti5cycmk7lN5Ofno9PpgHvrsI2NDdbW1sSGmxc5b+cHUL+lW6mJf15eXomVx6nQvwBzP9u6SR1+/TMUg8Esf3ZeAVqdWVYnB2sKCrVsP2R+drlMhp2NJanR5rabeuaOBbbSUg2iyOXd5jHLxl1hONhaYWNl/p1ebB3IgfV/oLKxQmllWaqs3APrlIxXrlyIwMrGDkFmVrB5eHiwcuVKrK2tKSgo4Pr1O+O2gICAkvnB+bCb2NirkMnMi1Eelauydt5ZPGrakZupJeFSLuUkFOgLIAiCP3DvitffKK+zARdBELxFUbx2K3EvwKW8Ukk8Ot2G1GbRhFPsWHkZ/2Z3ivzEjkRCt91AoRCwd7ag1/v+2DioaP1qDT7vax4Etuvthae/I+mJ5d8CANDileqsX3CW5i9Xe6zP8jwy4s2OjJnxK0vWHaRFw1ol17ceiGDz7lMoFHJcnewYO7grjnbW9OnalJeHmrdL9HulOXVrVSMhpWKWHL06N2bO0r/o2fHpmK3X9q7M6EFd6D1qAblFYsleyFmzZlGpUiVq165NWtr9twf4+fkxf/58UlJSaNOmDb6+vgAMHDiQyZMnI4oicrmc9957D1dX1/um06pVK77//nu2bdvGhAkTygzTtm1bVq1aRVDT0n4z3KvUpOMrg/lx5nAEmZwq1WvR882P2fzrbOZ+1g+TyYh3rSB6vz3xEUroDp17DmX1T5M4fXIvNWs3xM7BGQuL8pmIV/W1p+d7dZj+1mFkMgFPPwfe+qwBK7+IYnz3vZiMJjyc6tCCGg9P7AH069ePOXPmEBISQt26dXFycsLKqnwyOnlVJWhgD7aNnoEgk+HsW4MWowcR+s0KNr49AZPRSOXAOrT6eMg/kjF4cC/2T/uBqwdOULlBHawqOaC0urMSc7cce4oM1Dql5rVR/iwYcwInNwt8Ais9sF+rHezMj+NPknZdQ/Nu1fGuZ95S0vfDAGYNCcFkArlCYPDkIFyq3N8s/YWXq/Hz5xHsXn2FMQtKKyVv/55/LoklPz+fxPhYuvQcxu+/fok6dD+u/j6lth3+Hff6tTk44yfyktLw6fgCLnXME6rG7/Zhx7hZiCYRmUJOi7FvY+t+/9dtzQ7NOPL1z5zdtJtOX3xYZhifTi0IX7L+mffpbh5ehIduZ+PKmTi7VaN5+96cP31HWfRyn1Es+2YsDk5uuFetiba4YhNFldqS1KSrfDNlIBZWNgwc8VW54/6Ttv20qVGjBn379mXixInIZDK8vb0ZNmwYixYt4oMPPsBoNBIQEHDPkYsV5Z/0Jc8CX093NuwMY8KctXhVdWHQq63YdzSm5P7E93ow6JNFeLg6UNvbg4KisifH98PKUkVsXCovvjMbWxtLFk0bXO64H73zEu9PXcHWAxE0a+CLWyU7rK3+fU53n9W71MbWESsb+3v6h/y0M3zwwQe4urqWjC3KS5MmTZg1axaHDh2isd3Qe+7bVnbh0q4jpJyJRTSJ+PfsiDImgT/3hvHnvggsLG1wcbQrZSXwIKq3aMi+zxdw7WAYxluTUZW1JU2Gvc6ROcvY/8UPeLVpTPVmDQhfvpHO7Tpy8OBBiouLSU9PJzk5GaVSWcqa6344VnLnWmwkr/Qbw75tKxBFkbycdARBhkKhxLdOY27EnSMjLYFVP37KG+9OIfzAcq7FxVG1SX0QRXISUsg3QXGBAZncrAVxcrOkqEDPoMDNyGQCTTpXoTDRkcTERNq0acPRo0cxmUxUqVKF+HgNSpWctq95EnkgmaSreUzouQ/3GtZ4unuaf4NAH7LzC5n2/RYUChk6nQ5be4tbz1CZ0P3riRq6B4NeT6+3xuNimc3MRZsp1uqxtFQjCAKenp7oNIU4NavKxsET0BcVYzKasHKyR2VrxY1jUeTeSC55h7r61cS3S0t2fvI1cpUSGzdntLn5NB7Sh9NrtiGaRAzFxWTpjPzwww84Opb2N9G+fXu2HlzKp3uT8GvsjLOHuc+TK2T0GFabzd/GM3fuXBQKBS1atCiJ17lzZ5KSkli2bBlarRZfX1+6detG586dy6zDH374IbNnz+bgSR31W7qVXG/Xx4v0pAJOH05h2psHociW3VuWcGz/Rnz8GpWE69+tOdcSbtLx7Zko5HLe7N6cwa+1of8rzen41ldUda9EoF/1kvDzJw5g8GeryCkuoHKDO45Y1bbWVGlcj+h1Ozit/RNLtZLVc+84le7eoSGLfp+DW707c6DbBA9+jSt7j7Fu3TpsHEJ4dcAn/LboMwA6derE0qVLUavVhIWF4enpifKWZXD//v05fPgw69atw+kYDJocxKLxp0rF0xUb2bY0lmq17FGqynU65I/AL7e2wURhNlF8oAZFEB9qlwyCILwILAFuq6Y8geGiKJZ/w+VzQJUqVcS5B5o/azGeOWG7Eok4kMz7Xz9dc2mAuE1BvN3m76a2/y3+OhjF7tBovvt80MMDP2ZWHs6t0HFs9/MO/6Q4evQou3btYuCYpU8lv79j0OsQZDLkcgXxV6LZ9Mssxk1fc0+44qQ9eL0WVeH04zYFlcSL2xRU4aPxAPR6PTKZDLlczsWLF/nxxx9ZuHDhPeGio6O53sqzwuk/Dow6PYJMhkwhJ+3sZULnL+e15TPLDFsjJL5CZXm/k3WeFGG7Ejm8srikThYn7Xloud7vZJ0nxbVDYWRsPsIna812s3GbgrCo0vmp5H2brPRkln07lv99ue6p5lteytu2nzbFSXtK3okV7Z//KY/Sl9QIiX8m7/CElEyzI8tfJz31vMuDVqdHLpOhUMgJP3uNiXPXsXflP1Mk/FPurk/R0dGP1CfcXT8rkq/CtW2p9rZu2XRkMtk9/UNx0p5/XOfLetflp6Sza8Jc+vwyu9T1aoev0b+5DRZqJfM2X2P9mpUMHfEhSe18eNzUCIknICAAo9GISqUiJSWFzz77jJEjR+L7esx945XVf2uLC1FbWKHTFvPDzGH0eftTqnrWKRWmrHdTed6vZY1FoqOjHxjvfmP5lYdzH1jPyqpPKw/nVnisoi8sRmllgaFYy9YPptP64yE41/YqFaZGSHyZdas8z/a4+uGH5XU7v7+X2aO0O3h4Wd6v/y5PvL/LWJiwC39//1J1e9GiRSWKECj7+a9uCKRa90hUajlpNzR8+fYR5u96EYXKvHnl4/bHSEpKusdUShAEOaAURbFYEISawH6gliiKuvvJXd7TYXYJguCL+dgZgIuiKFZMhS7xXLByehRnQlL5ZLG02+lZ8Nk36zl44jyr5ox4eOD/GIsXLyYiIoI33njj4YGfENmZqaz6cSKiKKJQKOgz+N834E5PT2f27NmYTCYUCgWjRo16rOlvGz2DZu/3L1l1eRQ0aZnsm7qQzCs3cKntRau/OWAOX7YR98A6VG1U95+K+0S53V/2f23YsxaF+JBw7Ku54+hZtdT1o9/+QkLYGYb07Q8klB35X8KPM4fzyhsfUs3L/+GBH8LE4a2ZufjIPdd3bV6Ed+0gagU0LXX9eWjbT5u7+5Li4mIGDhz4rEV6ZHqP+pbPR71KYJ1/ZmkH4NvpIy7vnX/P9Tk//0XTQB9aN65zz72ktGzem7wck0lEpZQzZ3z/fyzH0+DMyX3s2rIYW/tKvPL6aMKP7uDVAR8/NN62A5HMXbYdFyc7Ph/Zk427TjJ9TJ+S+39vby/3Gcn6FV9y6VzYPW3zbmJiYlAoFPj5mZ1e7ty5E7VaTfv27Sv8bLqCQtYP+B+WlexpNqI/l3aH8FpAM159fz56g4ksjYG5H7/ODV3ZU6XyvKeSo84jUyhwv7WKfv7PfSjUamq9aHZyrdVqmTRpEoZbzi5HjBhR4l/i72xYeI46jZwpy+POhpVfkZZ0jQJNHv6BzUsUIMcObEKltqBRi5fLWywAnNiVyMaF58jL1NL3NTuOHz/O8OHDHxovPbGAOSOOMuLtR1+EaNp7Mjt//gQnBxuWbTjEd2sOY3/Ql/aTy2/ZdmTuz+TEJ2HQ6an1Yqt7FCAA2dnZjBw5kh9++AGArVu3snPnTipVqsT41ypmdfQw0tLS+OKLL0ryqigzxnVnzNRVJc7/y8vdZfk04t1d3/R6PePHjy9Vt+9WgNzNvrVXUVkoaN2zBnq9nmn9D2E0mBBFeGdqUIkC5CFYAQcFQVBi3uA14kEKECinEuTW2bsfATVEURwqCIKvIAi1RVH8qzzxJZ4PjAYTb3/+dFZPJe7FYDAyY2zfZy1GhejYsSMdO3Z8KnndfgFHRz/U4fMjYTQaSrxs3w8X9+qM++K3J5L/48LDw4MFCxY8azEeiH01d15b9hXLu7zDq0um33O/0ZDej5x2m16eT82f0e3+Mm5TxXwv1e7aBt9Oj1fRHB8STvXmQfcoQVqMGYTJYMT5eALPUgni5OLxr7ACebFX2VZrz0PbrghGo7GU07lH4e6+5Jtvvrlven/fU/8sqFa50jO3Avnfu93ue8+7mit7VpS9zfNpIYpiiXPL8hJ25E96DfyEmnWCkclk5VZQrv3rOF+Ne71kG/HflU9ltbe6Dds+NN2YmBgsLCxKlCBdu3YFKv5stpVdcPSsSoP+3fBoGGCWqY436pB4di4zO11eeTiX9i/Ys/Jw2Rb15XlPJUddQGlpUaIE8e9RerxkZWXFN9+UPmXofmOcPqPNcsZtuvfegPdmALB7yxJUFnf8NjRv/9otR6YV891yaGMcgycHEdDMlbhNNXn11VcrFL+8PGzc9cuWEN566y3yezS8b5iy6DC54os/O3bsYOrUqbf811XckvdxYTSYkCsqfoy9wWBEoah4n28yGJE9QryyaN7+tZLParX6nrp9Pzq+ccd/k1qt5stNHSqctyiK+UCjhwa8i/L6BFmB+RiaF259TwQ2AJIS5B+QnljArKGh1A6uxJUzWVSvbU+bXp5s+u48uVlaRs4xb0f59avT6LQmVGoZw79qjIe3LYmXc1n0aThGvQmTCcYsbEZlT1u2r7jE4c3xgNknSNdBvqQnFjB7WCi1g525FJWJk6sF435sgcpCzvSBh/ANqsSlyEwCmrlyZEs883a9iEIpo1CjZ0L3vczfbf7+/42ElEzeHPcjTep7E3kuHn+fKvR9qRnzlu8gIzuf7yebt6NMWbiJYq0eC7WS+Z8OwKe6G7HXUvho5mp0egOiKLJkxrt4V3Nl8dr9rNtudqDa75XmDO3bjoSUTAZ8/CNN6tckPOYa7i4OLJ81DEu1it6jviW4njfhMddo0bAW63eGEfL7ZJQKOfkFRXQcNJPQtVNQPqYO6t9EWloaU6ZMwd/fn9jYWLy8vErOos/JyeHjj82rTUuXLkWn06FSqfjwQ7PPg9Skq6z9+QuMBrOX7UGjZuPiXp3Du37jZIjZUVXT1j1o3aU/WenJLJ3/IV6+gcRficbe0ZV3PpyLUmXBjzOH4+lbn7jLZ/Dxa0x46F9MmLUJuUJBcZGGuZ/1Z+LszcjvszLz/5n8lHR2/G82rn4+ZF6Ox76aO+0mlbZQWt7lHd7ZbT7f/tqhMG4ci6Ltp+9x6KtFyNUqcm4ko0nNoM3E4VzaFcLNs5dx9a9J20/vTESPf7+a5KgLqG2t6TB1FJYOdhz6ahHVmwfh3bYpc+bMoX1SZSIPpWDUmxi9oBlVvO3Iy9Ly/cdhaHJ0eNd1JDo0jRmbOpQcQfw0iYqK4uhPK0AQcKtkjZusA5Gr/sBkMGBhZ0O7z0di5WRP+PJNFGZmk5+SjoWDLVUb1yP+SDhGvYH8lHR8Or5A8GDzIOLynlDObtyNyWDAxc+Hlh8NRiaXsbzLO9Tt/SI3jkWhUCvp/NU48pLSuH40kpTTF4la9Qedpo/h8OyluNX1JS3mEh4N/dn0x0EWdG+HQimjuLiYueO6P7O6nZWezJJ5o6lRsy5J12Nxca9Ov6HTSoW525LjzKn9nD8dQr+hU/l96VSUKgtupsSTnZnCG0Mmc+rodq5fiaG6dwD9hk4tSWPr799w5WIElla2DBzxFTZ2jvy+dCr+DVoR2LgDM8Z1p1HLlzl/OgSj0cBb78/CzcMTTV42qxd/RqEml2pe/sTGHGfM1F8rvAr3uNiwM4zFa/eTXWCiTp06tGrVinXr1mEwGLC1tWXcuHE4OjqyZs0asrKySEtLw87OjqCgIE6cOIFeryctLY02bdrQr18/AA4ePMi2bdswGAzUqlWLESNGIJfL6dOnD6+88gqnTp1CrVYzadIkUlNTOXnyJGfPnmXdunVMnDiRhQsX4ufnx/nz53Fzc+PUj9/x+hrzgYLP8t11+70e5F+Dc5cT8armysLP3ioV5m5Ljr8ORrHv2Fm+nTSQMV/+ioVayZXraSSlZjH/0wFs2BlGxLk4gvw9+XbSHUuYad9t5ljUJextrfhp6mAqOdoy5stf6di8Lt3aBdG092T6dG3K3qMxGAxGFk8fgk8NdzKz8xk5bSXZeQUE1qnBobDz7Fo2vsIrrhUpjwEf/0jzoFpEnIvj3b5tWfz7fkQRXKv6lJj1nzlzhiM/LQdRxC+wJc3bvca30wZRVJjP1YsR+Pg14mbKdQo0OTRv3xulUk12VirxF08wdUoWjvbW9O7ShEnv96TP6IUcj7rM8dOXzSdSVXbiZmYerZv4UbOaKxv3xZCV/SUGvY6qXn58MGkZgiAwb/KbaPKycKviTfzlaDx96/N/7N13eBTV+sDx75uyaSQECDVAaIEQqhB6EVBRuYL+QERUBK+KetGroldBUZSrQgBBlK5eBWzgVa+gSO9dQgdBWuiEGtKzKef3x25CgJBs1pBN4P08Dw+zk5k578ycOTN79sw5CReOYbFYeP3117FYLPz222+4ubmxYsUKevfuzdSpU6lSpQpWqzV7CNKUlBQsFgvDhg2jQYMGREVFMe+D98i0plGqcnksfr4EhYZweuc+Vn94lpB2zajepilbvvwR67GzfP0fL06cuUhaBkz5xBvfgCAuzkonPdWKu8U2xKx/xSDiTsRQsVE9ugx7jm8eepG693TgyNqtZKanc9eIF3G3ePLH3KWImxsHFq+h7Yv9ORG1G08fb5r0/RtTpkwhNjYWESE5OZmwsDCGDx/O4sWL+T1yLakpGZSt6EOpQE/+/nYzFn19kNs6VaYCt+Vadnl6Wli//AfEzZ1Nq+aSZk3Bz78McbFn6fbgIH6Y8QHuvt6IuxsWXx86vv40G2b/zJHx+7CmZJCelomPnyfunkL8BSt/bj2PCPS834vvvvuOixcv4uXlxenTp8nIyKDUZDe69Q+lbl81TVIAACAASURBVLMgxjyzhvhYa/ZIbBMnTmTNgvJs2HGQpORULJ6eTH53AFCVd1/uRmj9CGIvnOH0iUO07NCdE0f2cfLYfoLKBpCekUn/16dy+NgZLsYl8dFHH+H1pT8mMxPfcmXw8LJw7kA0jR7qxpG1W0iJjcOrlB9uHu6Ede9Mw153E/Xljxxdt5X0VCsVG4bS4dUnERHO7jvMylHT8fC2ULfM5RGQJk6cyMmTJxk0aBAWiwXfKQYff88rvndlZhi+HbuDzfNX4+PjQ9euXenevTvffvstmzZtwmq1Ur9+fQYNGoSIMHnyZBYvXozFYqFs2bIkJCSwcOFCFixYwJkzZ8jIyMDPz4/uaVXZv+08fqUtHPkjlrS0DOLOp5J4yUqmgbIBG0hNSWTamEG2llPuHqSnxvL1F26kpKbRpXU4uw+cYOP2A/j7eROfmIIghNepQkJiCg+/PDG7BdqZS2lYv/AkLcVKpUZ1ObhsAxnWNLz8/bAmJuGGMM7fh9j4JHbuO0bj+tXp+9JE/jh0CtzdqNq6KS0HPsT3j78OGLxK+ZGZkUF6UgqVqs4g9vxpbmtzD/c8MJCR77+Ph4eHrR8Yf39efvllPvroI0aPHs2nn37KiRMnOHnyJJW/8qFWwzJUruHPfU/W47PPPqPhYU92bzxLUlwaA99vTlhEeVKT05k6dDNnzpxBRGZj65pjkDHG6SFMHX36qW2M6SMifQGMMcmSX9fFyiExRxN48aPWVB0RwLAHl7Lul2MM/6YTUctO8fO0vTwX2YK3v+qEu4cbO9fFMHv8Ll7+pA1LvjvEPY+H0r57ddKtmWRmGg7tusiqH6MZMbsLGHirzzLqtwjCL8DC6SMJPP9hK57+d3MmvLSBTYuO076HrWY+KT6Nt7/qBMDZE4lsXXmKFncGs/7XY7TsGnxTVoBkiT5xlmn//jujX6tMt6fG8L/Fm/nf5JdZtGYnn8xaxIRh/fhx4kt4eLiz6ve9RE6by6fvP82sn1fzZO9O9OzaAmtaOhmZmezYe5Q58zfwy/RXMQbuGziGNk3rUNrfl8PHzzLpnScY8/ojPPPW58xfsY1ed9squeLik/lh4ksAHDt9nqXrdnFPxyb8vCSKbrc3vSkrQLKcOnWKIUOGUL16dQYPHszKlSuJjIxk48aNzJkzh8GDBzNq1Cjc3d3Ztm0bM2fO5KGHHmL90q94qOe9dOrUibS0NDIzMzl2bA3b1//EJx+NxRjDK6+8QudWIdQsU4pzMUd5c8jL1Kr1FKNGjeLM3p/o3LkzPp5WLBkxTBg7AoCP4g8Se2gebdq0YcGCBdzeoRX1K54r8H4duOiL/35n+nTZQd96tl74P8DxTh2d4evrS/dLAdf9e0x8Mt8ePcUbg14i/PlwJkyYgPl2NeXS3emQ4EfopQBmGsnextokXzKtnnS/FMABqydpiVYi37Wdy3Gvf8jo0aOp/oztPDfYeo5atWoxPTmVrtXC6dTvH3z77bdcmPYLzz77LAesnkQk+dLuUgDfixCU3oFPIrvz66+/smj8Qf75z/58PXUqzercQ+/evYmKimLZnHeuMxTwjXXkyBFWrnyX8ePHU7p0aTZs2MDxxiE8MPVdRIS9vyxn+zfzaPP8YwCc23eYHpOG4+FlYd9vKznzxyF6zxiFh7cXPw18i+ptbsPD24uDyzZw/+ThuHl4sGbcFxxYvJa693QgPTmViuF1aPn0Q2yY8g175y2jWf//sz3I2yuOslgTkuj+yVsApO85yldzQ6jRIYKYHXNoHNHZpZV7Z08foc+Tb1EztAnffT6Ctcu+z38lu+TEOJ57fQq7t67i849e4YU3P6PiE7WY8G5/ThzZR3BIPaypyQSHhNGj78ss+vlTFv38KT37vXbNtvxKBTL43a9Yu/R7Viz4ij5/H8ainz8ltH4Ed9z3BHt3rGPDip8Kc9cL5MyFJL75eiXPvzkDj7hN1KxZExFh7NixiAgLFy7kxx9/5MknbZ0VHzhwgMjISLy8vFiyZAl//vknEydOxMvLi8GDBxMREYG3tzerV69m9OjReHh4MHnyZFauXEmXLl1ISUkhLCyMxx9/nC+++IJFixbRp08fWrZsScuWLa/oCDAhIYFRo0Zx4MABRIQKS/aBn5/L710Hj8bw4ZBHaNG4NoM/+IoZP177WtT1XIpP4vuP/8miNTsZ8Po0/jflZcbWfIRuT41h1/7jNAytSlKylUb1qjH8hZ6M/+I3xn3xG+8PvrYVZ9nSfiz8zxC+/HEVU79dytghjzLui99o17wuL/S7m+Ub9vD13IKNeOKMg0fPMG7oY7w44G66D/yQBZ+/Rml/X+4eOIEzO+fSNDyEhQsXMvjf3+Pj58/0sS+wt3wwSYmXqFKtLvc++Bz//XIkDzz6KhtW/o9jh/dQyr8MZ88cw2q1smTGUHo8+yFb/zjClG+XEBuXSIvGtRjQsyP/GP4FoSGVqFmtAv6+3mz/4whubm6M+GQxKSlJjHipG7uiVhBYriIXz52iZ79/Ed60I28NugOLxYcXBg9j844jfPndQvr8fRgtb++NxduHzvf248LZk8TFjaZRi7u5474n+OBf99OiQ3f+77F/Mf6dx5kw8TOefuVj5s+fT0jHCDoNGcjPg97h7L5DtH95ALFHT2W/2nlyq23I9XPnzlG2xe14e5+klqUU999/P6NGjeKx+dM4f+Aov74yko7/epqqLRvxdc8rWxt4l/an1+fvs/unxWz/7lduf/1p6ve4I7vSA+BE1O7s5Y27kJCQQI9Jb2NNSGbV6E8Zu+wn/NzcaHVPVZ4a0Zwxz65l26pTV6TjFRSPu9clqjTczBORjVj8zUE2bfkXA9+L4K7oinj7etDq7qq8dNdvNOnkTWCFIBb/bzRVqpWnfntfju67RN1mgez5cjInDqYQWMlCaKdybFx4HE8vNy7EJFO6nDcd/y+Edb8cwyMgmQzfU5zcc55OvWrg9qcXlrTKtHzQwvcf7cYvwBN3DzeCa/tTr3kQy78/zKX4WI5nZtCoSxDPjmzB58OjeGbEZ7w2+E38A8px/Mg+nn9jOtPHvsDWDYtodXsPMjLSaRgeys8/fkeDlg9w56PdGD+8H88//w8udg1n45RvqdiwLr5BZZg/eCQVw+tQqnxZdsz5jdp3tKb5E71IibON5NOgZ1eaD+gJwLL3JnN03VZC2jVj5chptH2pP1Wa1mfnu9OJd89kXuk4khpVxXOtDw9/M47g9Uf49ucpvPRxG2KOJmR/71o65xBnjicxeHh/qnU5RELsaeIDZ9Dpn1buC7SNRDb5tU2sPD6KMuW9WbJsBa9ObUto03K80GU+nhY3Gj16hOTyAcRd8CItNYPUIzVZ9v0GgoJ9ib+YysOvNOLz4VE0alsRv9KebFhwgsrBto5pxy25nfgLqYx9bh3Nb+vIp290o2PfEazbup/If/Wl+zNjaRZek0Z1qzHjp1UYIDY+idXfvs352AQGDJnGbbe1oOqQR5n96KvE7DlAZlo6GEOFcNuPXX4WbwIa1OL8knW8PmcNXv5+nE1O4+2332ajfwpLhn/MkbVbMJmZYAwdX3uajdO+JcmaTuU7G+EdXZY9B9YRuywWi8VCp06diIiI4JNPPmH27Nm0atWKzz77jFKlSvH888/zySefcGzfMepVa4XXuWD89/cEoggNuI+5Oz5k/vz5jBs3jncf/Z6xY8fSpHoF9vrNPnXp0qV/A9v+atno6BOQVUR8AANg73BE+wQpBOWr+lG9nu2hvWpoAA3aVEBEqFY3gHMnEkmOT2PqkN85fSQBAdLTbR3ZhjYtx/+m7eXC6SRa3BVM5Rr+7Ntyjoi7gvH2tZ3WFndVYe/mczTvUoXyVf2oUd/2K1bNBoGcPXG51/02914eMaBz75r88tk+WtwZzMofo3n630UzQomrVKtcjvq1gwGoW7My7SPqISKE1arCsVPniUtI4aX3ZnH4+FlEIC3d1qSweYOafDxzIafOxHLv7U2oVa0Cm3Yc5J6OTfD1sf0Sfe/tTdm4/SBd2zeiWuVyNAy1NVNvXK86x05dyI6hxx2Xm/k9cl9bJn+zhHs6NmH2/A0l5r1hZ1WsWJEaNWoAUL16dZo0aZLdG/iZM2dITExk/PjxnDx5EhEhPT2dOnXqcPz4cebMmcO5c+do27YtVapUYc+ePbRu3Tq7J/e2bduye/duWrVqRcWKFalVy9aHRZ06da4YrrdDhw7Z0127duXHH3+kTZs2LFmyhBdeeMGp/apTp/A7UStsjsQYFBREeLit+XOnTp2YN2+ew9tv2bJl9rkMDAy84jzHxMRQq1Yt3Nzcso9/586d+eCDayt+PD09adu2bXbM69evB2DPnj288cYbADRv3pxSpW7ML6n52bFjB506daK0vfLF19eXxDPnWTL8Y5LOx5KZnoF/5cuju4S0a4aHlyX7c9UWDfEu7Q9AzY4tOL1jH+Luzrl9h/lpoL0CIzUN70BbZZObpwfV29pewylftybHN++6bmy1ulwe0Sbk8W5s/+YXanSIYMuWLfR5ZnQhHQHnBJatmD0EZvM297J6seOvyoQ37YCIULlqbfxLl6VyNVterhhciwvnThEcUg8RN5q2uit7+19+cm0FCEDjiM4AVK1Rn51RywE4/Oc2BvzTNspXWOO2+Phdv7LwRjtyOpXGEXdQyj+QlDjw9/cnOjqayMhILl68SHp6OhUrXh5ZoFWrVleMLtG0aVMCAmzxt2nThj179uDu7s7BgwcZPHgwAFarlcBA2/OBh4cHLVq0AGzX29at128WnnXt1qlTJ7vs7NGjh8vvXVUqlMkeHrfn3S34z39XOrzuXe0aZT8DBJX1v+L54Pip8zQMrYqbm9Cji+2+3bNrC556M/eOuu+93fbFqHG96vy20jYk76YdB/n8A9tIJZ1bhxPof+NH26laqSzNG9Zk4eodtLktlHJlbOXNUz1bs2G7rQKrZs2alAqwjZLRrPU9RB/cQZlylfH28SPmxGFqhzXHx9cfEaFZ63vYuOp/VKxUg/KB3tQJqUT5sgHc0aYBS9bu4u4OjdmwdT8+Xp74+XpRoVwAJ8/G0vPuFrzx4WzKlq3IpJEDsVpTMCaTo4d3c/H8KQLLVcLdw4K3jx+eFm8qVa2NV/mWVK3hm31tXs3btxRlgipz7PAevH39ad62G+7uHrRo9zcW/fwZxw7vwcvLi7BuHXHzcKd+9zuIP3X9ssavQjlKVSxHQHAFGmf624eDtfDTwLdJunAJk5FJ3PHTWDq1xK9CuSvWrdnRdt2Ur1eT6FW/53terL4WfMoEUKlRPZIuXAIRTu/cR/rh05joOF7vvoiES1ZKB105Ok2V2w8A0KKrLW/WbBDIpsUnrtl+UBVfylby4dyJJOq3LM/5k0m0uqcu1eqW5tDOC8QcSaBOnTD+2LeLvq824s8t5/H0csfTy53YsynUCAtk3S/HKNfgFGyxfV8JLO9NUBVfLv1RlYTY45QqY+HSuRRCm5SjTpOytLw7mA2/HSe4th+noxOIPZvC690XkZKUjqfFjfPnz+NfuiyJ8bEcObCL+o3bs/y3WTRufge/r/6Fjj2msmD+XLZsXMiJo3+Snm7l66+/xvqjBwlnznN41WZ8ytjKs+ptb2PxWxOo1aklCWdsz9TeAbbngJNb9rD9219IT0klNT6RsjWqUrlJGKkJSVRpanuVqspj97B3hK2PjuO/7yAtOYV5L/4bjwQrl+Ljee/xlfj4eWR/79q17gx3PlyLau1sY4SUCrTdx/dsPMO8z/dhTc4g4ZKVqnUCOL4/Dg9PNxq3t7U2adapMjvXxXB8fxyzx+8iJTEdg8HicYRSZQW/0p60716d/dvO07xLFaKWneSlT9pgTcmkTAXYud2dsc+u5dzJJOLOp7L87HLu+nM7MefjaNcslKjdh3FzE2LOX+L9+x5i14HjXIhNoJSPF/1fm8qJmAucvRhPzPnl+P65i6TzsVQIr41JzyDhzHkSz12gQngd/ILKkG5Nw+LnQ8KZ81w4dIyMtDQmT55MWikL1sQk4o7H4BdUhsRzFwlp34xN078jKLw2CTHn8SlTmtPxezm9cx+lSpWiTZs2hIeHk5aWxunTp3n00Ud5//33ee2111i1ahXdu3dn/vz51+Tdnj1tFVjNmzcnOjoagDVr1vDiiy8yffr0k8aYXfZRYP4SR3/iHw4sAKqJyNfYelzN/YlCFYhnjs5e3ETwtLe6cBMhI8Pw/YTdhLcsz+h5XXl1SjvSUm3v3rbrXp1XJ7fF4uXOqKfWsHvDGXsVlQPpuAkZ6ZffD/TyufxrTb1mQZw9kcQfm87amjHWvblHavHyvFwP6OYmWOyf3dyEjIxMxnz2C22b1WXZrDf5MvLZ7DG4/69rC76IfAZvL08eHTyJNVH78jr8V6Tj7iZXvEPt63P5C1GLxrU5duoC67fampGG1apSSHtaPOXsJElEsj+L2I7RV199RaNGjZg0aRJvvfUWaWm249+pUyfeeustLBYLb7/9Ntu3byevka5ypuPm5nbF8c85/F14eDgxMTHs3LmTzMxMQkL+eid6JdnVDf7y+my1Xtn/VM5zefV5vt77yddrYJjVUVzOc+fIyGZFwRhzTdxrJ8ykQc+u9J4RSYdX/549XCKAxzXDLV61z2JrT1z3ng70+s9Iev1nJH2+HkvE322vybh5uGenJ+5umDz6Y/D0vvxluFKjesSfPsfJbX+QmZlJ5aquragrSN5KT7vyNxcPT4t9GdtwkDnXycy8zvG4Tt5y97Bty83NjcysvOVA/EXn2vw1bdo07rvvPiZOnMigQYOuuPauHl4zt+NqjKFLly58/PHHfPzxx0ydOpVHHrFVWnh4eGSvc3VZebXcys7Dhw+7/N519am++sxLjjmpOa5N4IpngKufD9IzClZuZa3v7i45yq18wy90vt4We9q5J369+RYv2/k117ki3HL0EePu5pZruX7tkRH27dvH44NG8a/3vsPHz580a2quKWT1QZPz2rx2Gc8r9iG7PHBzy+4npCD3iqxT6Z7j/hUfH0+7lwfQ5JH7KBdag/Sr8kx2LFnruLmReZ28cr0Es/ZRRDh9+jS9BtUncl5XOveuicnMPf6sziLd3ITM9GvT87L/IJrz/GV9x0CEzAxb2eLl7c6ONTFUrF6KMhW8SUlMJ92aSfmqV1bQZX2P8PB0sz2jpWfi5ibXpJHlQkwydz9Wh8h5Xek5KDw77/uVCuTi+dMc+nMrterdhsXixdZNi7I7c3Vz9+DJF8fh7Wsbvr5Vq1ZUqF+blk8/xONzp3DPqFdB7NedMdfcB9NTrawd/wV3jXiR3jMiCbuvM+nWNPu9OvfTYIytAqX7hLeoWrUqDw9uyMfLul3xvQvMNRnamprBf0Zs5aUJbbLPlzU147p5burQ3wkJC+SVyW156t3mhIWFMWFpN8pW8MHLx+OK9XKWK6lJGTz8SkPuHxhG4w4VCQsLY/GXQ7n39ib0vLulvT8cyTq1iEBmpiEpxcobz/bg+X5d6dwqnLCwMHr9ZyQ1O0ZQo0ME7hZP2zOFfUU3i0f2sTQZmRhjqNmxBS+88AK9/jMS37KBhPe8E0spX8CQcimehJjzBNaocnm9TJNd0OV89svIyCAsLIyUlBQSExPZsGEDbdq0ITdZ9zF3d/fsjlVvxDOfQ5UgxpjFQE9gAPAtEGGMWVHo0ahrJCWkUbairZOjlT9FZ8+POZZAhWp+3PN4KM27VObovkuERQSxeclJUpPTSUlKZ/OSk4RFFKzDPoAO94fwySsbi6xzweIsPiGZSuVtFUFz5m/Inn/kxDlCqgTxZO9O3NW+EX8cPEnrJnVYuHoHySlWkpJTWbBqO62a1L7epq/rwXtaMuidL3ioW+v8F77JJSUlUa6c7deWpUuXZs8/ffo0lSpVokePHrRq1YroaNtQcxs3biQlJYWUlBTWr19PgwYNCpxmly5dGDt2bJF1+FqcnT17lr179wKwatWq7FYhWQIDAzl27BiZmZnZLTQKIjMzk7Vrbc3BV65cec328xIeHs6aNWsA2LJlCwkJCQVOvzA0adKENWvWEBcXB9jyrDUxCb/yZQH4c8HqPNc/sXknKXEJpKdaiV69mUqN6hLcvAGHVmwi+aKtQ76UuATiT5/Nczuevj6kJaXkuUzdu9uz7N2JNG/u+hZ+F8+fJvqA7YecrRsWZrcKyVIqoCwxJw+TmZnJzqgVBd6+MZns+H0ZAFs2LKRmaFOH160Z2oTtm5YAsG/XBpIT4wqcfmEJDW/Btk1LSEyIBSA+Pv665WJutm3bRnx8PKmpqWzcuJH69evTpEkT1q5dS2zs5W3mbB2XG19fX5KTk/NcpkuXLsyePdvl964TMRfZvMv2a+3PS6KyW4VkKV/Wn/3Rp8nMzGTBqu0F3n5mpuHXFbYWMj8t3kzLxo6PlNWycS3mLbOtu3LTH8TGJ+WzRuG5LbwGG7bt50JsAhkZmfxvcRRtmoZyW3gNoqOjSYiPJTMzg60bF1K91uURTyoH1+bQvi0kJyVgjGHrxoWUKVeZwHKViI6O5kKsbf6itbu4s11DFq/dSaYxpKSmkZCUytkLtuvn5yVRBFe0tTYp5R9IakoSqcmJgO2au3Qhhoz0NFJTkkiz5l6WeXn7kppy7TELqd2QlKQEkpPiyczMYNfm5Xh4Wgip3RCr1cq+BavJzMhk7y/Ls1+ZyE1CzHkSzpwHbK382re3dWDtFeBHUGgIZ/84SGZGBmlJKSSeuXDd7WSx+HqTlsd1k3zhEjG79gOQnpJKpUZ1cXd3Z+f6M6QkprN23lHizjve8N7Hz5OUxPQr5gVV9mXv7+dIT8skM8Ow7tdjhN5WDk+LO9HR0QSHBrDyp2guxCRT97ZyxJ5NweLtft3KvZzc3AXfAAsnD8WzedlJls05TEpiGicP2c75no1nSU/LZNn3h0lNSicoKAgRwa9UabZvWkJI7UZUrRnO6sXfUbNuU/7YsZbkxDj8/AO5q4dt1LijR49iTUzG08+X+NNn2ffb5dfbqrZsxInfd2ZXFKXEJWT/6OAd6E9aUgqHV24CwMvfD4ufL6d37APgwKLLr6JVa9kYa1IKmRnppKSkIAIpSelXfO9q1K4iS787lP0jckKsNbuCxL+MFymJ6WxadCJ72fS0THatjyElMZ2tK22vNCUnptOkQ0UWf3OQNXOPAnDqcDwZ9kqzes2D2LLiFKG3lWPlD4fZtvIUJw/HYwwElPMmrEWQ7ZjaKwasaemcj02gZePaZGQYIhrV4rtfN7Br33HiEpPJNIagsgG0blKHtVv+zF4vIy2dlLj47H2r1CSM+FO2e0Di2QtYE215tspt9Tm0fCMJCQmc3LoHi78f1gTb9Sfu7qyf+BWWUr5XtG7N2l7WM9nOnTvx9/fHzc1WeRYaGsqXX35JtWrViI2N5ciRI/nmM4D27dszZ84cW9oi4UAjh1bMQ0FeCPYGLtrXCbf/muD4i5bKKfc9WY+pQ35n/pf7CW99uUn1hvnHWTPvKB4eQukgb3r+I5xSgRY6/l8Ibz1ke/Dr/GBNaoSX4ezxxAKl2a57deZM2EXbv1XLf+Gb3HOP3slL781i+uzl2b2cA8xdFsWPC3/Hw8OdCmUDePmJeykT4Efve1vxt6dtzaj7dm9Lw7rVOHbqfIHS7Nm1BWM+/YUH7nT9FxVX69WrF+PHj+fnn3++Ylz21atXs3z5cjw8PChTpgwPP/ww/v7+3HHHHbzyyiuA7dWW2rVrExMTU6A0O3XqxFdffUXHjh0LdV9KomrVqrF06VImTZpE5cqVuffee9m0aVP23/v378+IESMICgoiJCQk3y9JV/P29ubo0aO89NJL+Pn58dprjjcw7Nu3L2PGjGH16tU0bNiQsmXL4ut745uWXy0kJISHHnqIoUOH4ubmRpkyZWg+oCdL3p6AX/myVAivQ/yp61dgVGpcj+XvTSHuRAx17myTPfRwi6d6M/+VUZhMg5uHO+1eHoB/pfLX3U7tO1qzavRn7PphIXeNeDHXZerc1Y7fP/v+imvJVSpWqcnmNb/y3y9HElSxGm27PMiebZcrjP7W+3k+H/8ygWUrUqlq7Vy/+OTF4uXD6RMHGT+8H96+pej3nON97HR94Gm+mvIm2zYtpna9ZgQEBuHtXfR5C6BScG3u7P4Ek0c+AxlJhIeH07dvX0aNGkW5cuWoV69enmVc/fr1GTduHKdOneL2228nNNQ29GO/fv14++23Mcbg7u7Os88+S4UKFa67nQ4dOjBx4kTmzZvHkCG5j3LSqVMnZs6c6fJ7V2iNSnz/20aGjPmOmlXL0///OrBk7c7svw999n76vzaVKhUCqVerConJBXu729fHwr7Dp7nn75H4l/Jh6rtPOLzu4L934x/vfMHcZVG0bhpKxXIB+PkWTWfOFYNKM/SZHvT+5wSMgS5tGnB3B1tZ0LVrV6ZEPgvGENa4HfUatGLN4u8A8PMPpNuDg5j77TgSE2Jp07kXFos3Fm9b55C9/zmB6BPn6H1vK57reydJSalM/mYJn8xahL+fN0dOnufYqfN0bBFGk7DqHD9vZcywvpQNqoynxbbv1Ws1ILBsRebNnsDva37Bw8MTb2+/a/ahwW0dmDFxCLu3rOSO+y4f94DAIMqWr8J/Z9g6CK0dFsHF86cJCAyiW7duzFv4G4eWricguBJBdapj8cv9eg4MqcKFA0c5tmE7tSoH89xzz/H222/z26ujKV21IqWrV+aPn5dy9o+DeJcuhYe3JdftZKnerhlL3prAkTVRtH3x2n7C/CuX588Fqzi1fR8m0xD+wJ147jzG+l/XsX7+MQLLeRNQzgsf/9yHFr1as86V+ejFDWxccByr1fYF3aeUJ31ebshnb0cx8V+baNk1mKYdK/HfCbu5s2tXlq75hdSkDM6fSuTU4QQ8LG74+Dn+9fCJYU2ZHsN9LwAAIABJREFU/lYUx/fHceJAnL3TXT/KV/UjavlJnm75MxZvd+o1C8pu1Vmhcg3iLp3D4uVN9z4vMnbYw6xf9gNhjdviW6o0U0Y9i4fFC3Fz4/jx41i93Dm6YRuB1SoT0v5y+RL2t84cXbeVI2uj+O8TQwm7rzMNe3Ul7L7O/HfAEPwrBWXfUwFuH/pMdseoVVtcvg+G3deJDVO+Yd6L7+MWn8K3Y+NYM+8YjdpdLhM7P1iTU4cTGHL/Ytw93OjcuyZ3P1aHLr1r8nqPRQQF+1Groa2Sr3ajsrTvEcLogWvx9HIjqLIv8Zes9H4xnF8+20dmpiE1KZ301Dg+H36cwPLe2es161yZtfOOknjJijFw7kQifqU9iXxqNRWq+hEWEcSfm/dzZ/8POHU2lrCaVWhaPwR3d2GVvWLVarW9blPa35dHX5lE9crlaNWkDht37OfEgCEknruAX/ky2fvWfEBP/jtgCPsXrsW7dCl8g2yvR7Z9sT/zj45izJgxGHc3/CsFkRJrq+Byc3fjwKK1VGx0+btR9vae6MXh39YwZswYAgICeOaZZ/joo48AeOyxxxgyZAhWq5UffviBGjVqYLHkfR0B/OMf/6B///4A4cDrwA4g96GbHCSONC8RkUigD7AbyGpzZYwxPf5K4sVNcHCwGbusravDcLmNC44Ttewk/xjdssjTPvzDbQy4/eZ+BSc/vyzfysI1O/jkLWc61vxrvlx5qVh8QXKltWvXsmHDhuzKlKK0Y8eO7H4uPvjgA5eei786rv2NlpaWhpubG+7u7uzdu5fJkyfz8ccfuzosduzYwZEONRxadt9vKzm79zDtXx5wQ2PKcmjFRqLXRPHE7d3wDu5aJGnm5sLZk3z+0cvFYsjc3KSnWRE3N9zdPYg+sIMfZozilX9/4+qwSDmxqEBlwpIlSzhw4ADPPpv7sMCFbe3atSxYsIBfJz5VJOnl5tip8/R/barLh8u9nlRrGu5ubnh4uLN51yGGjp3N4i+Hujosvlx5yakyIeXEomue2RKTUvHz9WJ/9Gnu/vsofp7yCo3qXf5R7XpppaYk4eXtizU1hUkjB9J7wBvZr0j8FUnHFnC4dVU8vCzEnYjhl5c/oM/XH+LueeUX/fhTZ1kwZCy9Z0QCELI6mjfeeIMPPvggu0xPS0rB09eb9JRU5r7wbzq++iRB9Wo6FdfV6WWptvIQ1XpsweLlTszRBN4fsIpxC+7Jfv2lMB3+4TZCHthCenpmgdI7/MNt1Ox1ub+grNYn4gbvPrICa2oGg8a0pGaDMtesV9B8lnJikcP31MIQsjr6in1zVkpiOt5+HqQmpzPisZU8NaJZrsfj6rQcXe96111yipWeg8Yz+rVHrrjuwHbtOXMsQ1ZHF3i9kNXRud6vMjIyyMjIwGKxcOrUKYYNG8bUqVOzX53J+Rx89XppaWn4+PhEYauTWArUNcZYr1nYQY5W9T0A1DPGaGeoN7kv/72V7atP89q09q4O5ZY0bPwclm/Yw8wxz+W/sCp006ZNIyoqiuHDh7s6FJWPs2fPEhkZSWZmJh4eHjz//PP5r3QLW/vRDI5t3M49o/8F0Xorz8vF86eZOXmobThCDw96P1E8v1AXJ1ll58MPP+zqUIq1EzEXefbt/2QPWXkzdn7+2uhv+DP6NAlJqfj7+VzzRex6vv/yA2JOHCItzUqL9n8rlAoQsFWYz31+BJnpGWAM7Qc/cU0FiKNWjf2M2OgTpFvTqHtPB6crQPKSlpbGu4+sICM9E2Pg7+/cdkMqQLKkJqfzXv9Vfym9z96OYtvq01hTMvDx86DbgLrXfHG/1Xz2dhQnDsZhTc2k4wMhDh8PZ9fLuu5Sren0vrelw9ddUUtNTeXNN9/MfjXnueeeu6LvkOtJSkqic+fOYGsJ8hPw3F+pAAHHW4L8BvQ2xrjmpesioi1BXE9bgriWtgRxreLUEkQ5pyAtQVwlZHW0S1uCKOcUtCWIK+zYsUPv4SVQYbYEuVFpOcvZlgS5tQQpCoXVEsERubVEuNHr3SotQRzxV46jM+VscWgJkp/rtQTJIiJRxpiIAm84F3lWhYrIJ9g6Sk8CtonIUnIMjWuM+WdhBKGUUkoppZRSSil1o+XXHmyz/f8oYO4NjsXlfH196Vvve1eHcUPExMRQsWJFV4eRr1nVZlGlXb8CrVNS9s0ZRb1v1Q7N4r777iuStPS8XSsgICB7un379sWyc1Y9b3m7ePEib3Qofk3cc+7brOhZ9OtXKD+kFAu3Sp6cNeuPIiufnXXx4kWH7+G3ynkrCaodcrxMuDpPFvSZrSBpFYZZs/5wuEzOuW+r7GM/tG/fnjc6FN29eFb0LPrWu/4v4c7KLU/OquZcWn9lvYKee0fOX2Febzfq+OealgPH8XrnraDXHdiuPWeeT2ZFF3y9WdH5f5/Ibd9yPgffaI6+DuMHpBhjMuyf3QEvY0zRje1VBJo0aWK2by/4cGklQUm7IReE7lvJpPtWMum+lUy6byWT7lvJpPtWMum+lUy6byWTM/tWmK/DOFoJsgG4M6tPEBEpBSwyxtxUHWhERESYzZs357+gUkoppZRSSimlikSR9QmSg3fOTlGNMQkikvsg2yVYVFRUgojsc3UcqtgLAs65OghVrGkeUY7QfKLyo3lE5UfziHKE5hOVn5KQR0IKa0OOVoIkikgzY8wWABGJAJILK4hiZF9h1S6pm5eIbNZ8ovKieUQ5QvOJyo/mEZUfzSPKEZpPVH5utTziaCXIS8D3InIS22gxVYA+NywqpZRSSimllFJKqULmltcfRaSFiFQyxvwOhAGzgXRgAXC4COJTSimllFJKKaWUKhR5VoIA0wCrfboN8AYwCbgITL+BcbnKzbhPqvBpPlH50TyiHKH5ROVH84jKj+YR5QjNJyo/t1QeyXN0GBHZboxpYp+eBJw1xrxj/7zNGNO0SKJUSimllFJKKaWU+ovyawniLiJZ/YbcASzL8TdH+xNRSimllFJKKaWUcrn8KjK+BVaKyDlso8GsBhCROsClGxybUkoppZRSSimlVKHJ83UYABFpDVQGFhljEu3z6gKlsobMVUoppZRSSimllCru8q0EUUoppZRSSimllLoZ5NcniFJKKaWUUkoppdRNQStBlFJKKaWUUkopdUvQShCllFJKKaWUUkrdErQSRCmllFJKKaWUUrcErQRRSimllFJKKaXULUErQZRSSimllFJKKXVL0EoQpZRSSimllFJK3RK0EkQppZRSSimllFK3BK0EUUoppZRSSiml1C1BK0GUUkoppZRSSil1S9BKEKWUUkoppZRSSt0StBJEKaWUUkoppZRStwStBFFKKaWUUkoppdQtwaFKEBEJEZE77dM+IuJ/Y8NSSimllFJKKaWUKlz5VoKIyNPAf4Fp9llVgf/dyKCUUkoppZRSSimlCpuHA8sMAloCGwGMMftFpMINjcpFgoKCTI0aNVwdhlJKKaWUUkoppeyioqLOGWPKF8a2HKkESTXGWEUEABHxAExhJF7c1KhRg82bN7s6DKWUUkoppZRSStmJyJHC2pYjfYKsFJE3AB8RuQv4HphXWAEopZRSSimllFJKFQUxJu9GHSLiBjwJdAUEWGiM+bQIYisSIjIQGAhQ3r9889bxLV0c0a2t18w+9HnkUVeHodRNb/zYKVj8m7k6jHwF+R8oUWXCO88MZc/nu10dRp60nFWFqaSUJepKJa1sdZazZXJWOVnU+ftGnZe/ehyUKg4qlSt7KjY2tkphbMuRliAvGGM+Ncb0NsY8aIz5VEReLIzEiwNjzHRjTIQxJsLT39PV4dzyThw75uoQlLolmIxLrg7BISWtTAioVdrVIeSrpB1TVbyVlLJEXelWKQecLZOzjk9R5+8bdV7+6nFQqjjw8/OrXFjbcqQSpH8u8wYUVgBKKaWUUkoppZRSReG6HaOKSF/gEaCmiMzN8Sd/4PyNDkwppZRSSimllFKqMOU1Osw64BQQBHyYY348sONGBqWUUkoppZRSSilV2K5bCWKMOQIcAdr81UREpAbwizGm4V/dVgHTbQ58CfgA84EXTX49wSqllFJKKaWUUuqmlG+fICLSWkR+F5EEEbGKSIaIxBVFcIVgCraRX0Lt/+5xbThKKaWUUkoppZRyFUc6Rp0I9AX2Y2tR8RTwiRNpuYvIpyKyW0QWiYiPiDQVkQ0iskNEfhKRMgAiskJEIuzTQSISbZ9uICKbRGSbfZ1Q+/zHcsyfJiLuIlIZCDDGrLe3/pgJPOBE3EoppZRSSimllLoJOFIJgjHmAOBujMkwxnwBdHYirVBgkjGmARAL9MJWMfG6MaYxsBMYns82ngUmGGOaAhHAcRGpD/QB2tnnZwCPAsHA8RzrHrfPu4KIDBSRzSKy2Yl9UkoppZRSSimlVAmRV8eoWZJExAJsE5HR2DpL9XMircPGmG326SigNhBojFlpnzcD+D6fbawH3hSRqsCPxpj9InIH0Bz4XUTA1lrlDPBHLutf0x+IMWY6MB0gODjYzEmfe81KquiMixzFQx49XB3GLavXzD70eeRRV4eRp/Fjp2Dxb+bqMPIU5H+g2B9HgEHPtHJ1CPkaF7nc1SHk651nhrLn890AtH6/rYujyV3Oa3tc5CgmTdvo4ohUQRXncqUklCXqSjnL1pxlWEE488zginu4M8/24yJHZU8XZf7O757n7Llq/X7bv3wclLqZONISpJ99ueeBRKAa0NOJtFJzTGcAgXksm54jNu+smcaYb4AeQDKwUES6AALMMMY0tf+rZ4x5B1vLj6o5tlkVOOlE3ErdMk4cO+bqEPJlMi65OoR8lYTjqApPQK3Srg4hX5onSz49h+pGcbYMcyZPloR7eHFWEu43SpUEjlSCPGCMSTHGxBlj3jXGDAbuK4S0LwEXRaSD/XM/IKtVSDS21h0AD2atICK1gEPGmI+BuUBjYCnwoIhUsC9TVkRCjDGngHh7x64CPA78XAhxK6WUUkoppZRSqgRypBKkfy7zBhRS+v2BMSKyA2gKjLDPHws8JyLrgKAcy/cBdonINiAMmGmM2QMMAxbZt7MYqGxf/jngM+AAcBD4rZDiVkoppZRSSimlVAlz3T5BRKQv8AhQU0RyvkQWAJwvSCLGmGigYY7PY3P8uXUuy+/F1sojyzD7/JHAyFyWnw3MzmX+5pzpKqWUUkoppZRS6taVV8eo67B1ghoEfJhjfjywoyCJiEgN4BdjTJFWSIjI+9hegyljjClVlGkrpZRSSimllFKqeLnu6zDGmCPGmBXAncBq+ygup7B1MCpFE95fNg9o6eoglFJKKaWUUkop5XqO9AmyCvAWkWBsnZA+AXzpRFruIvKpiOwWkUUi4iMiTUVkg4jsEJGfRKQMgIisEJEI+3SQiETbpxuIyCYR2WZfJ9Q+/7Ec86eJiDuAMWaDvYNUpZRSOQRXq+bqEBxSUuLMEndIRz5Qt5aSdo0qGz1vecs6PkV9nIrbeSlu8ShVWBypBBFjTBK2YXE/Mcb8HxDuRFqhwCRjTAMgFugFzAReN8Y0BnYCw/PZxrPABGNMUyACOC4i9bF1mNrOPj8DcHjQchEZKCKbRWRzgfdIKaVKqD6POFxMulRJiTPLns93uzoEpYpUSbtGlY2et7xlHZ+iPk7F7bwUt3iUKix59QmSRUSkDbaKhScLsN7VDhtjttmno4DaQKD9NRuAGcD3+WxjPfCmiFQFfjTG7BeRO7ANp/u7bSRcfIAzjgZljJkOTAcIDg42D3n0cHRVdQO0fr8tc9Ln5r+guiHGRY5ydQgOGfRMK1eHkKdxkcuZNG2jq8PIU5D/gRLxcPPOM0OLfcVCSSi3xkWOKvZ5Ul0r53U6LnK5i6NRNzNnyjBnnxmK8h5e0q6b8WOnYPFvlucyt096s8DbTY299rnk7Jb/OXV/DX+ygVPr9ZrZp0Q8d6hbgyOVGS8CQ4GfjDG7RaQW4EyJkppjOgMIzGPZdC63UvHOmmmM+UZENgJ/AxaKyFPY+ieZYYwZ6kRMSil1Szpx7JirQ3BIQK3Srg5BKZcpKdepUqpwmIyie6XS2furs+tpeaaKk3xfhzHGrDLG9DDGRNo/HzLG/LMQ0r4EXBSRDvbP/YCsViHR2Fp3ADyYtYK9AuaQMeZjYC62YXSXAg+KSAX7MmVFJKQQ4lNKKaWUUkoppdRNxJE+QW6k/sAYEdkBNAVG2OePBZ4TkXXYhujN0gfYJSLbgDBgpjFmDzAMWGTfzmKgMoCIjBaR44CviBwXkXeKYqeUUkoppZRSSilV/DjTt0eBGWOigYY5Po/N8efWuSy/F1srjyzD7PNHAiNzWX42MDuX+a8Brzkbt1JKKaWUUkoppW4eRdISRERqiMiuokgrR5q+IvKriOy1D8tbMnp8VEoppZRSSiml1A2RbyWI/ZWSABHxFJGlInJORB4riuAKwVhjTBhwG9BORO51dUBKKaWUUkoppZRyDUdagnQ1xsQB9wHHgbrAv5xIy11EPrW3ylgkIj4i0lRENojIDhH5SUTKAIjIChGJsE8HiUi0fbqBiGwSkW32dULt8x/LMX+aiLgbY5KMMcsBjDFWYAtQ1Ym4lbplBFer5uoQ8lUSYlSqOBN3HXGnJNKyT90ozuYtZ9Yr6nx8s103zpbfWu4rdSVH+gTxtP/fDfjWGHNBRJxJKxToa4x5WkTmAL2w9dfxgjFmpYiMAIYDL+WxjWeBCcaYr0XEgq1ipT62DlPbGWPSRGQy8CgwM2slEQkEugMTrt6giAwEBgJUqVLFqXHSVeEZFzmKhzx6uDqMW1b4kw146PHiffx7zezj6hAcMuiZVq4OIU/jIp0Z6dw1inu5PC6yZLxteTlPFu+8qS4bF7mcSdM22j/VyTFdfAT5H6DPI48CMH7sFCz+zVwckSqoIH/n1vtj5a4CPzMU9T08K286oijzb87r5mp5Pz84W35fu964yOVO3V/HRY7Kc713nhnKns935/q3vJ7xe83sU6DzpdRf4UglyDwR2QskA/8QkfJAihNpHTbGbLNPRwG1gUBjTNawuDOA7/PZxnrgTRGpCvxojNkvIndgG073d3vljA9wJmsFEfEAvgU+NsYcunqDxpjpwHSA4OBg48R+KXXTcHbs96Kk48wrpVTxkbNMNhmXXBiJcpaz91VnnhmK8z28KPNvcT4Of5Wzz5I38zFRxU++lSDGmCEiEgnEGWMyRCQJuN+JtFJzTGcAgXksm87lV3W8c8TyjYhsBP4GLBSRpwABZhhjhl5nW9OB/caYj5yIWSmllFJKKaWUUjcJRzpG9QUGAVPss6oAEYWQ9iXgooh0sH/uB2S1ConG1roD4MEcsdQCDhljPgbmYhtGdynwoIhUsC9TVkRC7NPvAaXJ+xUbpZRSSimllFJK3QIc6Rj1C8AKtLV/Pg68V0jp9wfGiMgOoCkwwj5/LPCciKwDgnIs3wfYJSLbgDBgpjFmDzAMWGTfzmKgsv2VmTeBcGCLvdPUpwopbqWUUkoppZRSSpUwjvQJUtsY00dE+gIYY5LFyZ5Rsxhjxub42DqXv+/F1sojyzD7/JHAyFyWnw3Mvnq+iCwEKmPr3HU1tgodpZRSSimllFJK3YIcaQliFREfwACISG2u7N+jOHvIGNMEaAiUB3q7OB6llFJKKaWUUkq5iCOVIMOBBUA1EfkaWx8crzmRlruIfCoiu0VkkYj4iEhTEdkgIjtE5CcRKQMgIitEJMI+HSQi0fbpBiKyyf5qyw4RCbXPfyzH/Gki4g5gjImzp+0BWLBX5CillFJKKaWUUurWk28liDFmMdATGIBtqNkIY8wKJ9IKBSYZYxoAsUAvYCbwujGmMbATW4VLXp4FJhhjmmLrnPW4iNTH1ldIO/v8DCB7kGn7KzFngHjgv1dvUEQGishmEdnsxD4ppdQ1gqtVc3UI+SoJMULJiFNjVEopVRiKsqx2Ni1n14s7pENoq+Ij3z5BRKQdsM0Y86uIPAa8ISITjDFHCpjWYWPMNvt0FFAbCDTGZI0IMwP4Pp9trAfetHd6+qMxZr+I3IFtJJnf7V2V+GCr9ADAGHO3iHgDXwNdsHWcSo6/T8c2jC7BwcHaUqQYmJM+19Uh3LLGRY5ydQgOecijh6tDyNcPj1/TTVG+es3sQ59HbHW448dOweLfrLDDyqEOk6ZtvIHbLywlIc7LMQb5H8g+h8VJcYxJOWbQM60AeOeZoez5fLeLo7lW6/fbZpfJrd9vmx2vKt6uvsc4W87ePunNAi2fGrvc4bSs8Vtu8H3wWkWVf8dFLr/us0xezw85nxP+Kke2c71yJ68YW7/f9vrP8tOun1ZJeQZVNwdHXoeZAiSJSBPgX8ARbC04CipnPyIZQGAey6bniM07a6Yx5hugB5AMLBSRLoAAM4wxTe3/6hlj3sm5MWNMCrYhde93Im6llCoSJ44dy542GfqLSUmU8xwqVZgCapV2dQjqJlIS7jElIcaiVtT3GC131M3KkUqQdGOMwVaB8LExZgLgXwhpXwIuikgH++d+QFarkGhsrTsAHsxaQURqAYeMMR9jq9RojK2PkgdFpIJ9mbIiEiIipUSksn2eB9AN2FsIcSullFJKKaWUUqoEcmSI3HgRGQo8BnS0dzrqWUjp9wemiogvcAh4wj5/LDBHRPoBy3Is3wd4TETSgNPACGPMBREZBiwSETcgDRgEpABzRcQLcLdvZ2ohxa2UUkoppZRSSqkSxpFKkD7AI8CTxpjTIlIdGFOQRIwx0diGqc36PDbHn1vnsvxebK08sgyzzx8JjMxl+dlAbi+ntShInEoppZRSSimllLp5OTI6zGljzDhjzGr756PGmAL1CSIiNURkl7NB/lUiMteV6SullFJKKaWUUsr1HBkdJh7IGjXFgu1VmARjTInoKUdEegIJro5DKaWUUkoppZRSruVISxB/Y0yA/Z830AuY5ERa7iLyqYjsFpFFIuIjIk1FZIOI7BCRn0SkDICIrBCRCPt0kIhE26cbiMgmEdlmXyfUPv+xHPOn2fstQURKAYOB95yIV7lAUY6PrkomHWdeKXWrKAn3RC2TSz5xd+53TWfWczYtdfMrCeWdunk4MjrMFYwx/wO6OJFWKDDJGNMAiMVWmTITeN0Y0xjYCQzPZxvPAhOMMU2BCOC4iNTH1m9JO/v8DCBr4Ot/Ax8CSdfboIgMFJHNIrLZiX1Shaywxj5XN6/cxqtXSqmbUUm4J2qZXPJZ/JsV2XrOpqVufiWhvFM3D0deh+mZ46MbtsoHc53F83LYGLPNPh0F1AYCjTFZw+LOAL7PZxvrgTdFpCrwozFmv4jcgW043d9FBMAHOCMiTYE6xpiXRaTG9TZojJkOTAcIDg52Zr+UUkoppZRSSilVAjgyOkz3HNPpQDRwvxNppeaYzgAC81g2ncutVLyzZhpjvhGRjcDfgIUi8hQgwAxjzNCcGxCR54Dm9ldpPIAKIrLCGNPJidiVUkoppZRSSilVwuVbCWKMeeIGpX0JuCgiHewjz/QDslqFRGNr3bEJeDBrBRGpBRwyxnxsn24MLAJ+FpHxxpgzIlIW8DfGTAGm2NerAfyiFSBKKaWUUkoppdStK98+QUSkqr3T0jMiEiMiP9hfRykM/YExIrIDaAqMsM8fCzwnIuuAoBzL9wF2icg2IAyYaYzZAwwDFtm3sxioXEjxKaWUUkoppZRS6ibhyOswXwDfAL3tnx+zz7vL0USMMdFAwxyfx+b4c+tclt+LrZVHlmH2+SOBkbksPxuY7Wj6SimllFJKKaWUuvU4MjpMeWPMF8aYdPu/L4HyNzgupZRSSimllFJKqULlSCXIORF5TETc7f8eA87f6MCUUkoppZRSSimlCpMjr8P8HZgIjMc2NO46+7ybjq+vL97ujtQLlTwxMTFUrFjR1WHcELpvhadatWq8YX4pkrT0vF1r1qxZ2WVQtWrV6NevTWGH9pfpecvbrFkHiuV9RM9byZRz34qyfHZWzjIsP7fKeSuunL3HFOW+zZp1oEjvg//5zx9FVn47ez0X5BrLydnz5kyczsborJJwvTlL9+1KiYmJpworfTHGFNa2SiQRGQgMBKhTp07z/fv3uziiG0MvopJJ961k0n0rmXTfSibdt5JJ961k0n0rmXTfSibdtyuJSJQxJqIw0r9uJYiIfIKt5UeujDH/LIwAipOIiAizefNmV4ehlFJK/X979x8sV13ecfz9JCYYftMmOJACoRVGBTXYi+MM1qaWWrSlVAYrtioqM+hUq06dVnFs1Vo7daxSnYIUBwo6CrWI4y+qUiWlTqfAjUQBMXhBKFEkULgQJNyQ8PSPPYubePfu5rJnz35336+ZnXv37Nm7z+fuk3M233vO90iSJKkyyEGQhU6H6RwNeD/w3kG84CjbsGHDwxGxqek6NPJWAvc1XYRGmj2iftgn6sUeUS/2iPphn6iXEnrkiEH9oL5Oh4mIGzLzuEG96KiKiOlBjS5pfNkn6sUeUT/sE/Vij6gXe0T9sE/Uy6T1SL+z1kz2xCGSJEmSJKl4ozeFvSRJkiRJUg26zgkSEVv5+REge0fEQ+2HgMzM/esurgEXNF2AimCfqBd7RP2wT9SLPaJe7BH1wz5RLxPVIxN/iVxJkiRJkjQZPB1GkiRJkiRNBAdBJEmSJEnSRHAQRJIkSZIkTQQHQSRJkiRJ0kRwEESSJEmSJE2EnoMgEXFCROxTff/qiPhoRBxRf2mSJEmSJEmD08+RIJ8AHomI5wJ/CdwJfKrWqiRJkiRJkgasn0GQHZmZwCnAxzLzY8B+9ZYlSZIkSZI0WE/pY52tEXE28GrgRRGxFFhWb1mSJEmSJEmD1c+RIK8E5oAzM/OnwGrgw7VWJUmSJEmSNGDROtOly4Otoz6+npknDq8kSZIkSZKkwVvwSJDM3ElrUtQDhlSPJEmSJElSLfqZE+RR4MaIuAr4WXthZr4tCNTVAAAQw0lEQVS1tqokSZIkSZIGrJ9BkK9WN0mSJEmSpGItOCfIEytFrAAOz8xN9ZckSZIkSZI0eD2vDhMRJwMbga9V99dGxJfqLkySJEmSJGmQ+rlE7vuA5wOzAJm5ETiyxpokSZIkSZIGrp9BkB2Z+eBuy3qfQyNJkiRJkjRC+pkY9aaI+GNgaUQcBbwV+O96y5IkSZIkSRqsfo4E+TPgGGAO+CzwEPD2OouSJEmSJEkatJ5Xh4mINZl5x27Ljs/M6+ssrAkrV67MNWvWNF2GJEmSJEmqbNiw4b7MXDWIn9XP6TBXRMTJmfljgIh4EXAu8OxBFDBKDj/8cKanp5suoxbbtm1jxYoVTZdRC7OVyWxlMluZzFYms5XJbGUyW5nMVqbFZIuIOwf1+v0cCXI8cB5wMvA84O+AkzPzrkEVMSqmpqZyXAdBJEmSJEkqUURsyMypQfysnnOCVKe9vBX4Bq3L5f5OKQMgEXFYRFwdEbdExM0R8baF1t++ffuwShu6u+4q4i1bFLOVyWxlMluZzFYms5XJbGUyW5nMVqams3U9HSYivsyul8LdG3gQuDAiyMw/qLu4AdgBvCMzvxMR+wEbIuKqzPz+fCsvXbp0uNUN0X777dd0CbUxW5nMViazlclsZTJbmcxWJrOVyWxlajrbQnOC/MPQqqhJZt4N3F19vzUibgFWA/MOgjw4M8Nl69YNr0DtYnZmBoADn/70hiuZTCX8/se5xtmZGVasWsUZN9zAJccdx7Z7760tZwm/Ryijzs4aR7XeEmrULyrhfSuhRv2iQbxvi3nenjxnEP202Ndr74+BWvbF3erqVe8g36t+ftZinjfIbHVtU4b9+1/szxrk72TU3rduz2t/Dh6GroMgmfmfEbEU+HpmnjiUamoUEWuA44Brd1t+FnAWwBF7783s7OzQaxuGZcuW8dhjjzVdxoLm5uYA9vg9KCHbYg0z22J//4u1mGzDrnEx5ubmWLJkyR7X+MjsLHNzc6xfv57ZzZvZuW0b1JTzyfwe7cldddY4qv3Z2ZOjWuOTMa77gBLetyfT/+P6vsHoZxvE+7aYntyT5wyi5/f09dr/3h7p+L3UsS/uVlevehf7O5nvc0k/P2u+dRZb42Ke12/ePf33Nuzf/2J/1mLft8W83rDft/mydX4OHoYFrw6TmTsj4pGIOCAzHxxKRTWIiH2BzwNvz8yHOh/LzAuAC6A1MeqbnBhVUgPaR6GtW7eOnx5zDACnD2lHIEmSWnY/Ktx9sVS/zs/Bw9BzYlTgUeDGiLgwIj7evtVd2KBExDJaAyCfycwrFlp3586dwymqAaP216NBMluZzFYms5XJbGUyW5nMViazlclsZWo6Wz+DIF8F/gq4BtjQcRt5ERHAhcAtmfnRXuuP8yDI1q1bmy6hNmYrk9nKZLYyma1MZiuT2cpktjKZrUxNZ1vwdBiAzLwkIpYDR1eLNmXm6J7ouKsTgNfQOpJlY7Xs3Zl55XwrL1++fGiFDdthhx3WdAm1MVuZzFYms5XJbGUyW5nMViazlclsZWo6W88jQSJiHfBD4FzgPODWiHhRzXUNRGZ+OzMjM5+TmWur27wDIADbt28fZnlDdeeddzZdQm3MViazlclsZTJbmcxWJrOVyWxlMluZms7W80gQ4CPASzJzE0BEHA1cCvx6nYU1YenSpU2XUJuDDjqo6RJqY7Yyma1MZiuT2cpktjKZrUxmK5PZytR0tn7mBFnWHgAByMxbgWX1ldSccR4E2WeffZouoTZmK5PZymS2MpmtTGYrk9nKZLYyma1MTWfrZxBkuroyzLrq9kkKmRh1Tz366KNNl1CbTZs29V6pUGYrk9nKZLYyma1MZiuT2cpktjKZrUxNZ4vMXHiFiL2ANwMvBILWVWLOy8y5+ssbrqmpqZyenm66DEkTqH199NPXr9/le0mSNDztfXCb+2Kpfv189o2IDZk5NYjX6+dIkBOA8zPz1Mx8eWaeM44DIAA7duxouoTa3H///U2XUBuzlclsZTJbmcxWJrOVyWxlMluZzFamprP1MzHq64DzI+L/gP+qbt/OzAfqLKwJvY6KKdm2bduaLqE2ZiuT2cpktjKZrUxmK5PZymS2MpmtTE1n6zkIkpmvBYiIQ4HTaF0q99B+nluaZcvGcr5XAFavXt10CbUxW5nMViazlclsZTJbmcxWJrOVyWxlajpbz9NhIuLVEfHPwOXAicA/Ab9Rd2FNmJsby7N8ALj99tubLqE2ZiuT2cpktjKZrUxmK5PZymS2MpmtTE1n6+dojn8EbgPOB67OzDtqrahB43wkyMEHH9x0CbUxW5nMViazlclsZTJbmcxWJrOVyWxlajpbzyNBMnMl8AbgqcAHI+K6iPh07ZU1ICKaLqE2e+21V9Ml1MZsZTJbmcxWJrOVyWxlMluZzFYms5Wp6Wz9nA6zP3A4cASwBjgAeLzespoxzqfD3HbbbU2XUBuzlclsZTJbmcxWJrOVyWxlMluZzFamprNFryuiRMT3gG9Xt2syc/MwCmvC1NRUTk9PN12GpAnUeX30fq6VLkmSBq+9D25zXyzVr5/PvhGxITOnBvF6/ZwO85zM/NPM/Ow4D4AA7Nixo+kSanPfffc1XUJtzFYms5XJbGUyW5nMViazlclsZTJbmZrO1nMQZJL0OiqmZI899ljTJdTGbGUyW5nMViazlclsZTJbmcxWJrOVqelsYz0IEhEXRcSWiLipn/XH+eowhxxySNMl1MZsZTJbmcxWJrOVyWxlMluZzFYms5Wp6WxdB0Ei4kPV11cMr5yBuxg4qd+Vx3li1JmZmaZLqI3ZymS2MpmtTGYrk9nKZLYyma1MZitT09messBjL4uI9wBnA/82pHoGKjOviYg1/a4/zkeCrF69uukSamO2MpltV7MzM2x/+GEuW7eOLRs3snzffWuo7MnzfSuT2cpktjKZrUztbO39MTCy++I9NQnv2ziapGydn4OHYaFBkK8B9wH7RMRDQADZ/pqZ+w+hvtpFxFnAWQCHHnoo68d0BuglS5bw+ONjeWVjsxXKbLvauWIFOTfH7OwsuWwZO1esGMntke9bmcxWJrOVyWxlamdr74+Bkd0X76lJeN/G0SRl6/wcPAz9XCL3i5l5ylCqqUF1JMhXMvPYXusee+yxedNNfU0fUpxbb72Vo48+uukyamG2MpmtTGYrk9nKZLYyma1MZiuT2cq0mGyDvERuz0GQ6gWfBhxf3b02M+8dxIsPw54MgkxNTeX09HTtNUmSJEmSpP4MchCk59VhqolRrwNeAfwRcF1EnDaIFx81TV+qp0733HNP0yXUxmxlMluZzFYms5XJbGUyW5nMViazlanpbAvNCdL2HuD4zNwCEBGrgP8ALq+zsEGIiEuBdcDKiNgMvDczL2y2KkmSJEmS1IR+5gS5MTOf3XF/CfDdzmXjwtNhJEmSJEkaLUOdEyQiPgw8B7i0WvRK4HuZ+c5BFDBKImIrsKnpOjTyVtK6cpLUjT2iftgn6sUeUS/2iPphn6iXEnrkiMxcNYgf1O/EqKcCL6R1edxrMvMLg3jxURMR04MaXdL4sk/Uiz2iftgn6sUeUS/2iPphn6iXSeuRfuYEITOvAK6ouRZJkiRJkqTa9Lw6jCRJkiRJ0jhwEGRXFzRdgIpgn6gXe0T9sE/Uiz2iXuwR9cM+US8T1SP9zgmyHHgGkMCmzNxed2GSJEmSJEmD1M/VYX4POB+4jdbEqEcCb8zMf6+/PEmSJEmSpMHoZxDkB8DvZ+ZMdf/XgK9m5jOGUJ8kSZIkSdJA9DMnyJb2AEjldmBLTfU0JiJOiohNETETEe9quh6Nhoi4IyJujIiNETFdLfuliLgqIn5YfT2o6To1XBFxUURsiYibOpbN2xfR8vFq2/K9iHhec5VrWLr0yPsi4sfV9mRjRLys47Gzqx7ZFBG/20zVGqaIOCwiro6IWyLi5oh4W7XcbYmesECfuD0RABHx1Ii4LiK+W/XI+6vlR0bEtdW25F+r6Q2IiL2q+zPV42uarF/1W6BHLo6IH3VsR9ZWy8d+f9N1ECQiTo2IU4GbI+LKiHhdRJwBfBm4fmgVDkFELAXOBV4KPAt4VUQ8q9mqNEJ+KzPXdlw7+13ANzPzKOCb1X1NlouBk3Zb1q0vXgocVd3OAj4xpBrVrIv5xR4BOKfanqzNzCsBqv3N6cAx1XPOq/ZLGm87gHdk5jOBFwBvrnrBbYk6desTcHuiljngxZn5XGAtcFJEvAD4EK0eOQp4ADizWv9M4IHMfDpwTrWexlu3HgH4i47tyMZq2djvbxY6EuTk6vZU4B7gN4F1wL3AuP3l+/nATGbeXk36ehlwSsM1aXSdAlxSfX8J8IcN1qIGZOY1wP27Le7WF6cAn8qW/wEOjIhDhlOpmtKlR7o5BbgsM+cy80fADK39ksZYZt6dmd+pvt8K3AKsxm2JOizQJ924PZkw1Tbh4erusuqWwIuBy6vlu29L2tuYy4HfjogYUrlqwAI90s3Y72+6DoJk5usXuL1hmEUOwWrgro77m1l4B6PJkcA3ImJDRJxVLXtaZt4NrQ8nwMGNVadR0q0v3L6o01uqQ0sv6jiVzh6ZcNXh6McB1+K2RF3s1ifg9kSViFgaERtpTVlwFa0LWsxm5o5qlc4+eKJHqscfBH55uBVr2Hbvkcxsb0c+WG1HzomIvaplY78deUq3ByLirxd4XmbmB2qopynzjX72vnawJsEJmfmTiDgYuCpaEwVLe8Lti9o+AXyA1vv/AeAjwBuwRyZaROwLfB54e2Y+tMAfZO2TCTZPn7g90RMycyewNiIOBL4APHO+1aqv9sgE2r1HIuJY4Gzgp8By4ALgncDfMAE9stDpMD+b5wat88jeWXNdw7YZOKzj/q8AP2moFo2QzPxJ9XULrZ3K84F72oeEVV/HbqJgLUq3vnD7IgAy857M3JmZjwOf5OeHqNsjEyoiltH6j+1nMvOKarHbEu1ivj5xe6L5ZOYssJ7W/DEHRkT7D96dffBEj1SPH0D/p2+qcB09clJ1ul1m5hzwL0zQdmSh02E+0r7RGhlaAbye1nwZvzqk+obleuCoahbl5bQmlPpSwzWpYRGxT0Ts1/4eeAlwE63eOKNa7Qzgi81UqBHTrS++BLy2mmn7BcCD7UPdNVl2O5/25bS2J9DqkdOrGfuPpDUR2XXDrk/DVZ2DfyFwS2Z+tOMhtyV6Qrc+cXuitohYVf11n4hYAZxIa+6Yq4HTqtV235a0tzGnAd/KzLH6K7921aVHftAx4B605ozp3I6M9f6m6+kw0LpMG/DnwJ/QmkDneZn5wDAKG6bM3BERbwG+DiwFLsrMmxsuS817Gq3DxaD1b+Wzmfm1iLge+FxEnAn8L/CKBmtUAyLiUloTRa+MiM3Ae4G/Z/6+uBJ4Ga3J6R6hNZisMdelR9ZVl59L4A7gjQCZeXNEfA74Pq0rQby5OmxV4+0E4DXAjdV52gDvxm2JdtWtT17l9kSVQ4BLqqsALQE+l5lfiYjvA5dFxN8CN9AaTKP6+umImKF1BMjpTRStoerWI9+KiFW0Tn/ZCLypWn/s9zfRbeAvIj4MnErrKJBzO2aUlSRJkiRJKs5CgyCP07qm8A52nQglaE2Mun/95UmSJEmSJA1G10EQSZIkSZKkcbLQ1WEkSZIkSZLGhoMgkiRJkiRpIjgIIkmSJEmSJoKDIJIkSZIkaSI4CCJJkiRJkibC/wPvO6SARhDKvAAAAABJRU5ErkJggg==\n", + "image/png": 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", "text/plain": [ - "
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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -3308,6 +3450,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -3321,6 +3464,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -3332,6 +3476,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -3349,7 +3494,7 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python 3", + "display_name": ".venv", "language": "python", "name": "python3" }, @@ -3363,7 +3508,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.8" + "version": "3.8.1" } }, "nbformat": 4, diff --git a/examples/mp/jupyter/pasta_production.ipynb b/examples/mp/jupyter/pasta_production.ipynb index ada6cd1..2522d8e 100644 --- a/examples/mp/jupyter/pasta_production.ipynb +++ b/examples/mp/jupyter/pasta_production.ipynb @@ -1,6 +1,7 @@ { "cells": [ { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -42,6 +43,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -61,6 +63,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -82,6 +85,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -89,6 +93,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -111,6 +116,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -143,15 +149,17 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "### Step 3: Prepare the data\n", "\n", - "Data is very simple and is ready to use without any cleasning, massage, refactoring." + "Data is very simple and is ready to use without any cleaning, massage or refactoring." ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -159,13 +167,14 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "#### Create the DOcplex model\n", "The model contains all the business constraints and defines the objective.\n", "\n", - "We now use CPLEX Modeling for Python to build a Mixed Integer Programming (MIP) model for this problem." + "We now use CPLEX Modeling for Python to build a Linear Programming (LP) model for this problem." ] }, { @@ -179,6 +188,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -196,6 +206,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -231,14 +242,14 @@ "# --- constraints ---\n", "# demand satisfaction\n", "mdl.add_constraints((inside_vars[prod] + outside_vars[prod] >= prod[1], 'ct_demand_%s' % prod[0]) for prod in products)\n", - "\n", - "# --- resource capacity ---\n", + "# resource capacity\n", "mdl.add_constraints((mdl.sum(inside_vars[p] * consumptions[p[0], res[0]] for p in products) <= res[1], 'ct_res_%s' % res[0]) for res in resources)\n", "\n", "mdl.print_information()" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -260,6 +271,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -289,6 +301,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -297,7 +310,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "metadata": { "scrolled": true }, @@ -307,11 +320,11 @@ "output_type": "stream", "text": [ "* Production model solved with objective: 372\n", - "* Total inside cost=24\n", + "* Total inside cost = 24\n", "Inside production of kluski: 40.0\n", "Inside production of capellini: 0\n", "Inside production of fettucine: 0\n", - "* Total outside cost=348\n", + "* Total outside cost = 348\n", "Outside production of kluski: 60.0\n", "Outside production of capellini: 200.0\n", "Outside production of fettucine: 300.0\n" @@ -322,26 +335,26 @@ "obj = mdl.objective_value\n", "\n", "print(\"* Production model solved with objective: {:g}\".format(obj))\n", - "print(\"* Total inside cost=%g\" % total_inside_cost.solution_value)\n", + "print(\"* Total inside cost = %g\" % total_inside_cost.solution_value)\n", "for p in products:\n", " print(\"Inside production of {product}: {ins_var}\".format(product=p[0], ins_var=inside_vars[p].solution_value))\n", - "print(\"* Total outside cost=%g\" % total_outside_cost.solution_value)\n", + "print(\"* Total outside cost = %g\" % total_outside_cost.solution_value)\n", "for p in products:\n", " print(\"Outside production of {product}: {out_var}\".format(product=p[0], out_var=outside_vars[p].solution_value))" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "In the above figure, all nurses but one are assigned the average of 7 shifts, which is what we expected.\n", - "\n", "## Summary\n", "\n", "You learned how to set up and use IBM Decision Optimization CPLEX Modeling for Python to formulate a Mathematical Programming model and solve it with IBM Decision Optimization on Cloud." ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -353,6 +366,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -383,7 +397,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.8" + "version": "3.9.12" } }, "nbformat": 4, From a8226fcecb06cf6d44025dac32f1be8f325372a8 Mon Sep 17 00:00:00 2001 From: Guglielmo-Sanchini1 Date: Thu, 25 May 2023 18:17:06 +0200 Subject: [PATCH 3/3] typos corrections --- examples/mp/jupyter/efficient.ipynb | 90 +++++++++++++++++++---------- 1 file changed, 59 insertions(+), 31 deletions(-) diff --git a/examples/mp/jupyter/efficient.ipynb b/examples/mp/jupyter/efficient.ipynb index 1323ed2..134da16 100644 --- a/examples/mp/jupyter/efficient.ipynb +++ b/examples/mp/jupyter/efficient.ipynb @@ -1,6 +1,7 @@ { "cells": [ { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -21,6 +22,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -78,6 +80,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -91,6 +94,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -102,19 +106,19 @@ "minimize \\sum_{k=0}^{k=N-1} (k+1) * y_{k}\\\\\n", "s.t.\\\\\n", "\\forall\\ \\ m\\ in \\{0..N-1\\}\\ \\ \\sum_{l=0}^{l=N-1} (y_{l} * (l+ (l+m) \\%3) \\ge l\\\\\n", - "y_{k} = 0, 1\\\\\n", - "\\\\\n", "\\sum_{l} y_{l} \\ge 2\\\\\n", + "y_{k} = 0, 1\\\\\n", "$$" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## A beginners's implementation of the model\n", "\n", - "In this section we show a Python/Docplex beginner's implementation of this model." + "In this section we show a Python/DOcplex beginner's implementation of this model." ] }, { @@ -139,7 +143,7 @@ " # create variables as a dictionary indexed by the range\n", " ys = m.binary_var_dict(rsize, name=\"my_yvar\")\n", " # create constraints\n", - " k = {(i,j) : (i + (i+j) %3) for i in rsize for j in rsize}\n", + " k = {(i,j): (i + (i+j) %3) for i in rsize for j in rsize}\n", " for i in rsize:\n", " m.add(m.sum(ys[i] * k[i,j] for j in rsize) >= i, \"ct_sum_yjs_%d\" %i)\n", " # for minimize, create a list of coefficients\n", @@ -149,6 +153,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -184,6 +189,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -206,20 +212,21 @@ } ], "source": [ - "N=3000\n", + "N = 3000\n", "with ContextTimer(\"bench1 size={0}\".format(N)):\n", " build_bench_model1(N)" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Pitfall #1 : use Model.sum(), not Python sum()\n", "\n", - "In the above code, we compute the sum of variables using `Model.sum()`, not Python builtin function `sum`. One could wonder why Docplex had to redefine a specific sum function?\n", + "In the above code, we compute the sum of variables using `Model.sum()`, not Python builtin function `sum`. One could wonder why DOcplex had to redefine a specific sum function.\n", "\n", - "Python's um function calls the `+` operator repeatedly, that is, sum([x,y,z, t]) is evaluated as ((x+y)+z)+t. Calling `sum` with `N` arguments then creates `N` intermediate expressions, but there's more. Each intermediate expression has to be copied for the next sum, and as the size of the intermediate expressions grow, each copy takes an O(n) time, and th efinal sum has time in O(N^2).\n", + "Python sum function calls the `+` operator repeatedly, that is, `sum([x,y,z,t])` is evaluated as `((x+y)+z)+t`. Calling `sum` with `N` arguments then creates `N` intermediate expressions, but there's more. Each intermediate expression has to be copied for the next sum, and as the size of the intermediate expressions grows, each copy takes an O(n) time, and the final sum has time in O(N^2).\n", "On the opposite, `Model.sum()` creates only _one_ expression and incrementally adds each argument to it.\n", "\n", "**Rule #1**: never use Python's builtin `sum` function to compute expressions,\n", @@ -251,7 +258,7 @@ " # create variables as a dictionary indexed by the range\n", " ys = m.binary_var_dict(rsize, name=\"my_yvar\")\n", " # create constraints\n", - " k = {(i,j) : (i + (i+j) %3) for i in rsize for j in rsize}\n", + " k = {(i,j): (i + (i+j) %3) for i in rsize for j in rsize}\n", " for i in rsize:\n", " m.add(sum(ys[i] * k[i,j] for j in rsize) >= i, \"ct_sum_yjs_%d\" %i)\n", " # for minimize, create a list of coefficients\n", @@ -268,6 +275,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -275,14 +283,15 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Pitfall #2 : create variables in batches, not one by one\n", "\n", - "In the above code we used DOcplex `Model.binary_var_dict` method to create opur dictionary of variables. This method creates variables in _one_ call. One could wonder what if we had created the variables one by one? The answer kis: it works, but much slower.\n", + "In the above code we used DOcplex `Model.binary_var_dict` method to create our dictionary of variables. This method creates variables in _one_ call. One could wonder what if we had created the variables one by one. The answer is: it works, but much slower.\n", "\n", - "In the fnext cell, we define two functions to create large number of variables, either one by one or by batch, and compare times." + "In the next cell, we define two functions to create a large number of variables, either one by one or in batch, and compare times." ] }, { @@ -305,10 +314,11 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "Then we sample these two functions for various sizes, measure time and plot the result" + "Then we sample these two functions for various sizes, measure time and plot the results." ] }, { @@ -378,7 +388,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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obNi6j2Qqzf1r6zjQ1Mrkkf25ZdF0Fs0ZQ79Cq1BX5/9CkiRJknqMxpY2Hn5mK8lUmjWZvRTm5XDZzNFULUgwp2yQ173pRiyrkiRJkrq9V3ccYGkqw/LVtTQcbuGU4X35+8umcuVpYxhUXBB3PB0Hy6okSZKkbqm5NctjL9STTKV56tVd5OVEXDR9JFWVCU4/xbuHdHeWVUmSJEndSs3uQyxbmeGulbXsPNDEmEF9+PxFk7i6opSS/t5usaewrEqSJEnq8tqygSde3E4yleE3L24nAs6bXEJVZTnnTBxObo6zqD2NZVWSJElSl7V9XyN3razhzhUZ6hoaKelfyKfeO55r5icYM6hP3PF0AllWJUmSJHUp2WzgqVd3kUyleeyFelqzgbMnDOMfLp/K+VNGkJ+bE3dEnQSWVUmSJEldwp6DzSxfXcvSFRle23mQwcX5fPSscVw7P8G4YX3jjqeTzLIqSZIkKTYhBFan95BMZXj42a00t2aZN3Ywf3P+BC6ePpKi/Ny4IyomllVJkiRJJ93+xhbuX7uFZCrDxm376V+Yx7XzylhSWc6kkf3jjqcuwLIqSZIk6aR5bksDyVSaB9bVcai5jeljBvCdD87g8lmj6VtoPdH/5b8GSZIkSSfU4eY2fra+jmQqzfraBoryc1g4awxVCxLMLB0Udzx1UZZVSZIkSSfEy/X7SaYy3Lumlv2NrUwo6cfNl0/litNKGdgnP+546uIsq5IkSZI6TVNrG488t41kKsOK13ZTkJvDJTNGUlVZzryxg4miKO6I6iYsq5IkSZLetfSugyxdkeGeVbXsPthM+dBivnTJZK6aW8rQfoVxx1M3ZFmVJEmSdFxa27I8vnE7d1Sn+d3LO8nNiXjflBKqKss5a/wwcnKcRdXxs6xKkiRJOiZbGw6zbEUNy1ZmqN/XxMgBRXz2fRO5Zl4ZIwcWxR1PPYRlVZIkSdI7ymYDv3tlJ3dUp3l8Qz0BOGfCcL65MMF5k0vIy82JO6J6GMuqJEmSpLe180AT96yqZemKNDW7DzO0bwH/z3tO5dp5CRJDi+OOpx7MsipJkiTpT4QQSL22m2QqwyPPbaWlLbDglCH83UWTuWjaSArynEXViWdZlSRJkgRAw6EW/nttLclUhle2H2BAUR4fXlBOVWWC8SX9446nXsayKkmSJPViIQTW1zaQrE7zs2fqaGzJMrtsEN+9aiaXzRxNn4LcuCOql7KsSpIkSb3QwaZWHlhXRzKV5vm6fRQX5HLFnFKqKhNMHzMw7niSZVWSJEnqTTZu20eyOsN9a7dwoKmVySP7881F01k0ezT9i/Ljjie9zrIqSZIk9XCNLW38/NmtJFMZVqf3UJCXw2UzR1FVWc5piUFEURR3ROnPWFYlSZKkHmrTjgMsTWVYvqaWvYdaGDesL199/xSuPK2UwX0L4o4nHZFlVZIkSepBWtqyPPZCPclUmj+8sou8nIiLpo2kqjLB6acOdRZV3YZlVZIkSeoBtuw9zJ2pDHetqmHH/ibGDOrD5y+axNUVpZT0L4o7nnTMLKuSJElSN9WWDfz2pe0kqzP85sXtBOC8SSV8eEE550wcTm6Os6jqviyrkiRJUjezfX8jd6+s4c4VNWzZe5jh/Qv55HvHc828MkoHF8cdT+oUllVJkiSpGwgh8NSru0im0vzy+Xpas4Ezxw/lq++fwvumjiA/NyfuiFKnsqxKkiRJXdieg83cu6aWpakMm3YeZFBxPjeeOZZr5yc4ZXi/uONJJ4xlVZIkSepiQgisyewhWZ3hoWe30tyapaJ8MJ86fzyXTB9FUX5u3BGlE86yKkmSJHUR+xtbuH9dHcnqNBu37adfYR7XVJRRtSDB5JED4o4nnVSWVUmSJClmz21pIJnK8MC6LRxqbmPa6AH84wdn8IFZo+lb6Et29U7+y5ckSZJicLi5jZ89U0cylWF9zV6K8nO4fOZoPrygnJmlA4kibzuj3s2yKkmSJJ1Er2zfTzKV4d7VtexrbGV8ST++dvlUPjinlIHF+XHHk7oMy6okSZJ0gjW1tvHo8/Ukq9OkXttNfm7EJdNHUVWZYP64Ic6iSm/BsipJkiSdIJldh7hzZYa7V9aw62AziSHFfPGSyVw1t5Rh/Qrjjid1aZZVSZIkqRO1tmX59cbtJFMZnnx5BxHwvikjqFpQztnjh5GT4yyqdDQsq5IkSVIn2NbQyLKVGZatqGHbvkZGDijib86fwDXzyhg1sE/c8aRux7IqSZIkHadsNvD7V3aSTKX51YbttGUD50wcztcXTuP8ySXk5ebEHVHqtiyrkiRJ0jHadaCJe1bXsjSVIbP7EEP7FvDxs09hyfwEiaHFcceTegTLqiRJknQUQgiseG03yVSGR57bRnNblvnjhvC3F03iomkjKMzLjTui1KMcVVmNomgzsB9oA1pDCBVRFA0B7gLGApuBD4UQ9kTt193+HnApcAi4IYSwpuM41wNf7TjsLSGE2zvvVCRJkqTO13C4hfvW1JJMZXh5+wH6F+WxpDJBVWWCCSP6xx1P6rGOZWb1vSGEnW/4/ovA4yGE70RR9MWO778AXAJM6PioBH4EVHaU268BFUAAVkdR9GAIYU8nnIckSZLUqdbX7CWZSvPg+joaW7LMKhvE/7xqJpfPHE2fAmdRpRPt3SwDXgic2/H17cATtJfVhcBPQwgBqI6iaFAURaM6xj4WQtgNEEXRY8DFwJ3vIoMkSZLUaQ41t/LgujqSqQzPbmmguCCXK+aMoaqynOljBsYdT+pVjrasBuCXURQF4P8NIdwGjAghbAUIIWyNoqikY+wYoOYNj63t2PZ22/9EFEU3ATcBJBKJYzgVSZIk6fhs3LaPpakM963Zwv6mViaN6M83F05j4ZwxDCjKjzue1CsdbVk9M4RQ11FIH4uiaOMRxr7VXY7DEbb/6Yb2InwbQEVFxZ/tlyRJkjpDY0sbv3huK8nqDKvSeyjIy+GyGaOoWpDgtMRg2i/FIikuR1VWQwh1HZ+3R1F0HzAfqI+iaFTHrOooYHvH8Fqg7A0PLwXqOraf+6btT7yr9JIkSdIxem3nQe5ckeGeVTXsOdTC2KHFfOXSKVw5t5QhfQvijiepwzuW1SiK+gI5IYT9HV9fCHwDeBC4HvhOx+cHOh7yIPDXURQto/0CSw0dhfZR4NtRFA3uGHch8KVOPRtJkiTpLbS0ZfnVC/UkUxl+/8pO8nIiLpw2gqrKck4/ZSg5Oc6iSl3N0cysjgDu61gGkQcsDSE8EkXRSuDuKIr+AsgAV3eM/zntt615hfZb19wIEELYHUXRN4GVHeO+8ceLLUmSJEknwpa9h1m2IsOylTXs2N/EmEF9+NsLJ/KhijJKBhTFHU/SEUTtF+3tmioqKsKqVavijiFJkqRupC0bePKlHSRTaX69cTsBeO+kEqoqE5w7qYRcZ1GlLiOKotUhhIq32vdubl0jSZIkdRnb9zdyz6palqYybNl7mGH9CvnEueNZPL+M0sHFcceTdIwsq5IkSeq2Qgg8vWkXyVSGR5/bRms2cMapQ/nypVO4YOoICvJy4o4o6ThZViVJktTt7D3UzPLV7bOom3YeZGCffG44YyzXViY4dXi/uONJ6gSWVUmSJHULIQTWZPaSTKV5+JmtNLVmmVs+mFvPG8+lM0ZRlJ8bd0RJnciyKkmSpC7tQFMr96/dQjKVYcPWffQtyOXqilKWzC9n6ugBcceTdIJYViVJktQlPV/XQDKV4YG1WzjY3MbUUQP49hUz+MDs0fQr9GWs1NP5X7kkSZK6jMaWNh56Zit3VKdZV7OXwrwcPjBrNFULyplVOpAo8rYzUm9hWZUkSVLsXtl+gKWpDMtX17CvsZVTh/flHy6bypWnlTKwOD/ueJJiYFmVJElSLJpbszz6/DaSqTTVm3aTnxtx8fRRVFUmqBw3xFlUqZezrEqSJOmkqtl9iDtXZLh7VQ07DzRTOrgPf3fxJD5UUcawfoVxx5PURVhWJUmSdMK1tmX5zYs7SKbS/PalHUTA+VNGUFWZ4JwJw8nJcRZV0p+yrEqSJOmEqd/XyLIVNSxbmWFrQyMjBhTy6fMmcM28MkYP6hN3PEldmGVVkiRJnSqbDfzh1Z0kqzM8tqGetmzg7AnD+Nrl03jflBLycnPijiipG7CsSpIkqVPsOtDE8tW1LF2RIb3rEEP6FvCxs8exZH6C8qF9444nqZuxrEqSJOm4hRBYld7DHdVpfvHsNprbsswfO4TPXTCRi6ePpDAvN+6Ikropy6okSZKO2b7GFu5bs4VkKs1L9QfoX5THksoESyoTTBzRP+54knoAy6okSZKO2rO1DdxRnebB9XUcbmljVulA/ueVM7ls1iiKC3xpKanz+BtFkiRJR3SouZWfra8jmcrwTG0DffJzWTh7NFWV5cwoHRh3PEk9lGVVkiRJb+nFbftZmkrz32u2sL+plYkj+vGNhdNYNGcMA4ry444nqYezrEqSJOl1Ta1t/OLZbSRTaVZu3kNBbg6XzhjJhxeUM7d8MFEUxR1RUi9hWZUkSRKbdx7kzhUZ7lldy+6DzYwdWsyXL53MVXPLGNK3IO54knohy6okSVIv1dKW5fEN9SRTGX738k5ycyIunDqCqspyzjh1KDk5zqJKio9lVZIkqZep23uYZStruGtlhvp9TYwaWMTnLpjINfPKGDGgKO54kgRYViVJknqFtmzgyZd3kKzO8OuN9QTg3InD+daics6dNJy83Jy4I0rSn7CsSpIk9WA79jdx96oa7lyRoXbPYYb1K+Cvzj2VxfMSlA0pjjueJL0ty6okSVIPE0KgetNukqk0jz6/jZa2wOmnDOVLl0zhgqkjKMhzFlVS12dZlSRJ6iH2Hmrm3jVbSKbSbNpxkIF98rnu9LFcOz/B+JJ+cceTpGNiWZUkSerGQgisrdlLsjrDQ8/U0dSaZU5iEP/r6lm8f+YoivJz444oScfFsipJktQNHWhq5YF1W0hWZ3hh6z76FuRy1dxSllQmmDZ6YNzxJOlds6xKkiR1Iy/U7SOZSnP/2i0cbG5jyqgBfOuK6SycPYZ+hb60k9Rz+BtNkiSpi2tsaePhZ7aSTKVZk9lLYV4Ol80czYcXJJhdNogoiuKOKEmdzrIqSZLURb264wBLUxmWr66l4XALpwzvy99fNpUrTxvDoOKCuONJ0gllWZUkSepCmluz/PKFbSSrMzy9aRf5uREXTRtJVWU5C04Z4iyqpF7DsipJktQF1Ow+xLKVGe5aWcvOA02UDu7D5y+axIcqyhjevzDueJJ00llWJUmSYtKWDfxm43aSqTRPvLSDCDhv8giqFiQ4Z8JwcnOcRZXUe1lWJUmSTrLt+xq5a2UNd67IUNfQSEn/Qj513gQWzytj9KA+cceTpC7BsipJknQSZLOBp17dRTKV5pcv1NOWDZw9YRj/cPlUzp8ygvzcnLgjSlKXYlmVJEk6gXYfbGb56hqWpjJs3nWIwcX5fOyscVw7P8HYYX3jjidJXZZlVZIkqZOFEFid3kMyleHhZ7fS3Jpl3tjBfOZ9E7l4+kiK8nPjjihJXZ5lVZIkqZPsa2zh/rVbSFZneLF+P/0L87h2XhlLKsuZNLJ/3PEkqVuxrEqSJL1Lz9Y2kEyleWBdHYdb2pgxZiD/dOUMLp81muICX25J0vHwt6ckSdJxONTcykPrt5JMpVlf20BRfg4LZ42hakGCmaWD4o4nSd2eZVWSJOkYvFS/n6WpDPeuqWV/YysTSvrx9Q9MY9GcMQzskx93PEnqMSyrkiRJ76CptY1HnttGsjrDis27KcjN4dIZI6laUE5F+WCiKIo7oiT1OJZVSZKkt5HedZClKzLcs6qW3QebKR9azJcumcxVc0sZ2q8w7niS1KNZViVJkt6gtS3LrzZsJ5lK87uXd5KbE3HBlBFULUhw5qnDyMlxFlWSToajLqtRFOUCq4AtIYTLoigaBywDhgBrgI+EEJqjKCoEfgrMBXYB14QQNncc40vAXwBtwKdDCI925slIkiQdr60Nh1m2ooZlKzPU72ti1MAiPvu+iVwzr4yRA4vijidJvc6xzKz+DbABGNDx/T8B/zuEsCyKon+jvYT+qOPznhDC+CiKFneMuyaKotsjagAAACAASURBVKnAYmAaMBr4VRRFE0MIbZ10LpIkScckmw08+fIOkqkMj2+oJwDvmTicWxaV895Jw8nLzYk7oiT1WkdVVqMoKgXeD3wL+FzUfhWB84AlHUNuB26mvawu7PgaYDnwrx3jFwLLQghNwGtRFL0CzAee7pQzkSRJOko7DzRxz6palq5IU7P7MMP6FfCX7zmVa+cnKBtSHHc8SRJHP7P6f4C/A/p3fD8U2BtCaO34vhYY0/H1GKAGIITQGkVRQ8f4MUD1G475xse8Loqim4CbABKJxFGfiCRJ0pGEEEi9tptkKsMjz22lpS2w4JQh/N1Fk7lo2kgK8pxFlaSu5B3LahRFlwHbQwiroyg694+b32JoeId9R3rM/90Qwm3AbQAVFRV/tl+SJOlYNBxq4d41tSRTaV7dcZABRXl8ZMFYllQmGF/SL+54kqS3cTQzq2cCH4ii6FKgiPb3rP4fYFAURXkds6ulQF3H+FqgDKiNoigPGAjsfsP2P3rjYyRJkjpNCIH1tQ3cUZ3mZ+vraGrNMrtsEN+9aiaXzRxNn4LcuCNKkt7BO5bVEMKXgC8BdMys/m0IoSqKonuAq2i/IvD1wAMdD3mw4/unO/b/OoQQoih6EFgaRdGttF9gaQKwonNPR5Ik9WYHm1p5YF0dyVSa5+v2UVyQy5VzS1kyP8H0MQPjjidJOgbv5j6rXwCWRVF0C7AW+I+O7f8B/FfHBZR2034FYEIIz0dRdDfwAtAKfNIrAUuSpM6wYes+kqk096+t40BTK5NH9ueWRdNZOHs0/Yvy444nSToOUQhd922hFRUVYdWqVXHHkCRJXVBjSxs/f3YryVSG1ek9FOTlcNnMUVRVlnNaYhDtNyOQJHVlURStDiFUvNW+dzOzKkmSdNJt2nGApakMy9fUsvdQC6cM68tX3z+Fq+aWMqi4IO54kqROYlmVJEldXktblsdeqOeO6jRPvbqLvJyIi6aPpKoywemnDHUWVZJ6IMuqJEnqsmr3HGLZihruWlXDjv1NjBnUh89fNImrK0op6V8UdzxJ0glkWZUkSV1KWzbw25e2c0d1ht+8uJ0IOG9yCVWV5ZwzcTi5Oc6iSlJvYFmVJEldwvZ9jdy9qoY7V9SwZe9hhvcv5K/fO57F8xOMGdQn7niSpJPMsipJkmKTzQae3rSLZCrNL5+vpzUbOGv8ML76/im8b+oI8nNz4o4oSYqJZVWSJJ10ew42s3x1LUtXZHht50EGF+fz0bPGce38BOOG9Y07niSpC7CsSpKkkyKEwJrMHpLVGR56divNrVkqygfz6fPHc8n0URTl58YdUZLUhVhWJUnSCbW/sYX7124hmcqwcdt++hXmsXheGUsqE0weOSDueJKkLsqyKkmSTojntjSQTKV5YF0dh5rbmDZ6AP/4wRl8YNZo+hb6EkSSdGT+pZAkSZ3mcHMbP3umjmQqw/qavRTl5/CBWaOpqixnZulAosjbzkiSjo5lVZIkvWuvbN/PHdUZ7l1Ty/7GViaU9OPmy6dyxWmlDOyTH3c8SVI3ZFmVJEnHpam1jUefrydZnSb12m7ycyMumT6KDy8oZ97Ywc6iSpLeFcuqJEk6Jpldh1i6IsM9q2rYdbCZxJBivnjJZK6aW8qwfoVxx5Mk9RCWVUmS9I5a27I8vnE7yVSGJ1/aQW5OxPmTS/jwgnLOGj+MnBxnUSVJncuyKkmS3ta2hkaWrcywbEUN2/Y1MnJAEZ953wSumVfGqIF94o4nSerBLKuSJOlPZLOB372yk2R1msc3bicbAudMGM43Fk7jvMkl5OXmxB1RktQLWFYlSRIAuw40cc/qWpamMmR2H2Jo3wI+fvYpLJmfIDG0OO54kqRexrIqSVIvFkJgxWu7SaYyPPLcNprbslSOG8LfXjSJi6aNoDAvN+6IkqReyrIqSVIv1HC4hf9eU0syleGV7QcYUJRH1YIEVZUJxpf0jzueJEmWVUmSeosQAs/UNpBMpXlwfR2NLVlmlQ3iu1fN5LKZo+lT4CyqJKnrsKxKktTDHWxq5cH1dSRTaZ7bso/iglyumFNKVWWC6WMGxh1PkqS3ZFmVJKmH2rhtH0tTGe5bs4X9Ta1MHtmfby6azqLZo+lflB93PEmSjsiyKklSD9LY0sYvnttKsjrDqvQeCvJyuGzGKKoWJDgtMZgoiuKOKEnSUbGsSpLUA7y28yBLU2nuWV3L3kMtjBvWl6++fwpXnlbK4L4FcceTJOmYWVYlSeqmWtqy/OqFepKpDL9/ZSd5OREXThvBhyvLOf3Uoc6iSpK6NcuqJEndzJa9h1m2IsOylTXs2N/EmEF9+NsLJ/KhijJKBhTFHU+SpE5hWZUkqRtoywaefGkHyVSaX2/cTgDOm1RC1YIE75lYQm6Os6iSpJ7FsipJUhe2fX8j96yqZWkqw5a9hxnWr5BPnDuexfPLKB1cHHc8SZJOGMuqJEldTAiBp1/dRTKV4dHnt9GaDZw5fihfef8ULpg6gvzcnLgjSpJ0wllWJUnqIvYeamb56vZZ1E07DzKoOJ8bzxzLtfMTnDK8X9zxJEk6qSyrkiTFKITAmsxekqk0Dz2zlebWLHPLB3PreeO5dMYoivJz444oSVIsLKuSJMVgf2ML96+rI1mdZuO2/fQrzOOaijKWVCaYMmpA3PEkSYqdZVWSpJPo+boGkqkMD6zdwsHmNqaNHsC3r5jBwtmj6Vvon2VJkv7Iv4qSJJ1gh5vbeOiZOpKpDOtq9lKUn8PlM0dTtaCcWaUDiSJvOyNJ0ptZViVJOkFe2X6ApakMy1fXsK+xlfEl/fja5VP54JxSBhbnxx1PkqQuzbIqSVInam7N8ujz20im0lRv2k1+bsTF00dRVZmgctwQZ1ElSTpKllVJkjpBze5DLF2R4Z5VNew80EzZkD584eLJXF1RyrB+hXHHkySp27GsSpJ0nFrbsvzmxR3cUZ3myZd3EAHvmzKCqgXlnD1+GDk5zqJKknS8LKuSJB2jbQ2N3LWyhmUrM2xtaGTEgEI+fd4EFs8vY9TAPnHHkySpR7CsSpJ0FLLZwB9e3ckd1Wl+tWE7bdnAOROHc/MHpnH+5BLycnPijihJUo9iWZUk6Qh2HWhi+epalq7IkN51iCF9C/jY2eNYMj9B+dC+cceTJKnHsqxKkvQmIQRWbt5DMpXmF89uo7kty/xxQ/jcBRO5ePpICvNy444oSVKPZ1mVJKnDvsYW7luzhWQqzUv1B+hflMeSygRVlQkmjOgfdzxJknoVy6okqdd7pnYvyeoMD66v43BLG7NKB/I/r5zJ5bNG06fAWVRJkuJgWZUk9UqHmlt5cF0dyVSGZ7c00Cc/l0VzRrNkfjkzSgfGHU+SpF7vHctqFEVFwJNAYcf45SGEr0VRNA5YBgwB1gAfCSE0R1FUCPwUmAvsAq4JIWzuONaXgL8A2oBPhxAe7fxTkiTp7b24bT/JVJr71mxhf1Mrk0b05xsLp7FozhgGFOXHHU+SJHU4mpnVJuC8EMKBKIrygd9HUfQL4HPA/w4hLIui6N9oL6E/6vi8J4QwPoqixcA/AddEUTQVWAxMA0YDv4qiaGIIoe0EnJckSa9rbGnjkee2kUylWbl5DwV5Obx/xiiqKhPMLR9MFEVxR5QkSW/yjmU1hBCAAx3f5nd8BOA8YEnH9tuBm2kvqws7vgZYDvxr1P4qYCGwLITQBLwWRdErwHzg6c44EUmS3mzzzoMsXZHhnlU17DnUwtihxXzl0ilcObeUIX0L4o4nSZKO4KjesxpFUS6wGhgP/AB4FdgbQmjtGFILjOn4egxQAxBCaI2iqAEY2rG9+g2HfeNj3vhcNwE3ASQSiWM8HUlSb9fSluXxDfUkUxl+9/JOcnMiLpw6gqrKcs44dSg5Oc6iSpLUHRxVWe1Yqjs7iqJBwH3AlLca1vH5rV4FhCNsf/Nz3QbcBlBRUfFn+yVJeit1ew+zbEWGZStr2L6/idEDi/gfF0zkQ/PKGDGgKO54kiTpGB3T1YBDCHujKHoCWAAMiqIor2N2tRSo6xhWC5QBtVEU5QEDgd1v2P5Hb3yMJEnHrC0bePLlHSSr0/x643YC8N5JJVRVJjh3Ugm5zqJKktRtHc3VgIcDLR1FtQ/wPtovmvQb4Crarwh8PfBAx0Me7Pj+6Y79vw4hhCiKHgSWRlF0K+0XWJoArOjk85Ek9QI79jdx96oa7lyRoXbPYYb1K+Svzj2VxfMSlA0pjjueJEnqBEczszoKuL3jfas5wN0hhIeiKHoBWBZF0S3AWuA/Osb/B/BfHRdQ2k37FYAJITwfRdHdwAtAK/BJrwQsSTpaIQSqN+3mjlSaXz6/jZa2wBmnDuVLl0zhgqkjKMjLiTuiJEnqRFH7xX67poqKirBq1aq4Y0iSYrT3UDP3rtlCMpVm046DDOyTz1VzS1lSmeDU4f3ijidJkt6FKIpWhxAq3mrfMb1nVZKkkyGEwNqavSSrMzz0TB1NrVlOSwzif109i/fPHEVRfm7cESVJ0glmWZUkdRkHmlq5f+0WkqkMG7buo29BLldXlLJkfjlTRw+IO54kSTqJLKuSpNi9ULePZCrN/Wu3cLC5jSmjBvCtK6azcPYY+hX6p0qSpN7IVwCSpFg0trTx0DNbSabSrM3spTAvh8tnjaaqMsHsskFEkbedkSSpN7OsSpJOqld3HGBpKsPy1bU0HG7hlOF9+fvLpnLVaaUMLM6PO54kSeoiLKuSpBOuuTXLL1/YRrI6w9ObdpGfG3HRtJFUVZaz4JQhzqJKkqQ/Y1mVJJ0wNbsPceeKDHevqmHngWZKB/fh7y6exNVzyxjevzDueJIkqQuzrEqSOlVbNvCbjdtJptI88dIOIuC8ySP48IIE50wYTk6Os6iSJOmdWVYlSZ2ifl8jd62sYdmKDHUNjZT0L+RT501g8bwyRg/qE3c8SZLUzVhWJUnHLZsNPPXqLpKpNL98oZ62bODsCcP4h8uncf6UEvJzc+KOKEmSuinLqiTpmO0+2Mzy1TUsTWXYvOsQg4vz+dhZ47h2foKxw/rGHU+SJPUAllVJ0lEJIbAqvYdkdZqfP7uN5rYs88cO4bMXTOTi6SMpzMuNO6IkSepBLKuSpCPa19jC/Wu3kKzO8GL9fvoX5nHt/DKqFpQzcUT/uONJkqQeyrIqSXpLz9Y2kEyleWBdHYdb2phZOpB/unIGl88aTXGBfz4kSdKJ5asNSdLrDjW38rP1dSRTGZ6pbaBPfi4LZ49mSWWCmaWD4o4nSZJ6EcuqJImX6vezNJXh3jW17G9sZeKIfnxj4TQWzRnDgKL8uONJkqReyLIqSb1UU2sbjzy3jWR1hhWbd1OQm8OlM0ZStaCcivLBRFEUd0RJktSLWVYlqZdJ7zrI0hUZ7llVy+6DzZQPLebLl07mqrllDOlbEHc8SZIkwLIqSb1Ca1uWX23YTjKV5ncv7yQ3J+KCKSOoWpDgzFOHkZPjLKokSepaLKuS1IPV7T3MspU13LUyQ/2+JkYNLOJzF0zkmnlljBhQFHc8SZKkt2VZlaQeJpsNPPnyDpKpDI9vqCcA75k4nG8tKufcScPJy82JO6IkSdI7sqxKUg+x80ATd6+q4c4VGWp2H2ZYvwL+8j2ncu38BGVDiuOOJ0mSdEwsq5LUjYUQSL22m2QqwyPPbaWlLXD6KUP5wsWTuXDqSArynEWVJEndk2VVkrqhhkMt3LumlmQqzas7DjKgKI+PLBjLksoE40v6xR1PkiTpXbOsSlI3EUJgXc1ekqkMP1tfR1NrljmJQfzz1bO4bOYoivJz444oSZLUaSyrktTFHWxq5YF1dSRTaZ6v20ffglyumlvKksoE00YPjDueJEnSCWFZlaQuasPWfSRTae5fW8eBplYmj+zPLYums2jOGPoV+utbkiT1bL7akaQupLGljYef2UoylWZNZi+FeTlcNnM0VQsSzCkbRBRFcUeUJEk6KSyrktQFbNpxgKWpDMvX1LL3UAunDO/L3182lStPG8Og4oK440mSJJ10llVJiklza5bHXqgnmUrz1Ku7yMuJuGj6SKoqE5x+ylBnUSVJUq9mWZWkk6x2zyGWrahh2coadh5oYsygPnz+oklcXVFKSf+iuONJkiR1CZZVSToJ2rKBJ17cTjKV4TcvbicCzptcQlVlOedMHE5ujrOokiRJb2RZlaQTaPu+Ru5a2T6LumXvYUr6F/Kp947nmvkJxgzqE3c8SZKkLsuyKkmdLJsNPL1pF3dUp3nshXpas4GzJwzj7y+bwvlTRpCfmxN3REmSpC7PsipJnWTPwWaWr65l6YoMr+08yODifD561jiunZ9g3LC+cceTJEnqViyrkvQuhBBYk9nDHdUZHn52K82tWeaNHczfnD+Bi6ePpCg/N+6IkiRJ3ZJlVZKOw/7GFu5fu4VkKsPGbfvpV5jH4nllLKlMMHnkgLjjSZIkdXuWVUk6Bs9taSCZSvPAujoONbcxfcwAvvPBGVw+azR9C/2VKkmS1Fl8ZSVJ76CxpY0H19eRrE6zvraBovwcFs4aQ9WCBDNLB8UdT5IkqUeyrErS26jbe5g7qtMsW1nD7oPNTCjpx82XT+WK00oZ2Cc/7niSJEk9mmVVkt4ghMCq9B5+8ofNPPL8NkIIXDB1BNefMZbTTxlKFEVxR5QkSeoVLKuSRPtS35+tr+MnT23m+bp9DCjK42NnjePDC8opG1IcdzxJkqRex7IqqVfb1tDIHdVplq7IsPtgMxNH9OPbV8xg0ZzRFBf4K1KSJCkuvhKT1OuEEFid3sNPntrML57bRjYE3jdlBDeeMZbTT3WpryRJUldgWZXUazS2tPHQM1v5yVOv8dyW9qW+f3HWOD7iUl9JkqQux7Iqqcfb1tBIMpVmaSrDro6r+n7riulcMWeMS30lSZK6qHd8lRZFURnwU2AkkAVuCyF8L4qiIcBdwFhgM/ChEMKeqH393PeAS4FDwA0hhDUdx7oe+GrHoW8JIdzeuacjSe1CCKzJ7G1f6vvsVtpC4PzJI7jxzLGc4VJfSZKkLu9ophRagf8RQlgTRVF/YHUURY8BNwCPhxC+E0XRF4EvAl8ALgEmdHxUAj8CKjvK7deACiB0HOfBEMKezj4pSb1XU2sbD63fyu1Pb+aZ2gb6F+Vxwxljue70sSSGutRXkiSpu3jHshpC2Aps7fh6fxRFG4AxwELg3I5htwNP0F5WFwI/DSEEoDqKokFRFI3qGPtYCGE3QEfhvRi4sxPPR1IvVb+vkWTHVX13HmhmfEk/blnUvtS3b6FLfSVJkrqbY3oFF0XRWGAOkAJGdBRZQghboygq6Rg2Bqh5w8NqO7a93fY3P8dNwE0AiUTiWOJJ6mVCCKyt2ctP/rCZn7++1LeEG84Yx5njXeorSZLUnR11WY2iqB9wL/CZEMK+I7wIfKsd4Qjb/3RDCLcBtwFUVFT82X5Jampt4+fPbuUnf9jM+toG+hfmcf0ZY7nu9HLKh/aNO54kSZI6wVGV1SiK8mkvqskQwn93bK6PomhUx6zqKGB7x/ZaoOwNDy8F6jq2n/um7U8cf3RJvc32fY3ckcqwNJVh54EmTh3el28unMYHTyt1qa8kSVIPczRXA46A/wA2hBBufcOuB4Hrge90fH7gDdv/OoqiZbRfYKmho9A+Cnw7iqLBHeMuBL7UOachqSdbm9nDT55qX+rbmg2cN6mE688Yy1njh5GT41JfSZKknuhopiLOBD4CPBtF0bqObV+mvaTeHUXRXwAZ4OqOfT+n/bY1r9B+65obAUIIu6Mo+iawsmPcN/54sSVJerPm1iw/f3YrP35qM+tr9tK/MI+PLGhf6jt2mEt9JUmSerqo/aK9XVNFRUVYtWpV3DEknUTb9zeyNJUhmcqwY38Tpwzvyw1njOWDp5XSz6W+kiRJPUoURatDCBVvtc9XfpK6hHU1e/nJH17j4We30tIWeO+k4dxw5jjOdqmvJElSr2RZlRSb5tYsv3huKz/+w2bW1eylX2EeVZXlXH/GWMa51FeSJKlXs6xKOul27G9iaSrDHal0+1LfYX35+gemceVcl/pKkiSpna8KJZ0062v2cvtTm3noma00t2U5d9JwbjhjLOdMGO5SX0mSJP0Jy6qkE+qPS31/8tRm1mb20rcglyWVCa47vfz/b+/Og6M+7zyPfx7dUutCgAS6MWAQh8G2zOVJfMSJMWMHO76IbUJS2fJsVby1WzWVjXN6djLJOLs1k5mtnUlVZtcBBAYzTnxkxhPGzjHZtSQuQ2ww9hgbtSQOYRDoAqnV3c/+0T+BLLWkbh39a3W/X1Wq7t+vG/cX+eGHPjzf5/nputm5bpcHAACAOEVYBTAlPu7q0679zdrR6NW5rj5Vz8zRM/ct0UM3lysvK93t8gAAABDnCKsAJtU7rR36Wf1J/dMfQq2+t10/Wz96qFq30eoLAACAKBBWAUxYfyCoXx09q631TTrkvShPRqq+uKpCX1pXrfm0+gIAAGAcCKsAxu1Cd6jVt67Rq7bOPlXNzNH37l2ih2rLlU+rLwAAACaAsAogakdPdehnbzbpl2+fls8f1KcWztJffmG5br++mFZfAAAATArCKoCI9AeC2nvsrLa+2aSD3ovKyUjVo7UV2rKuSguK89wuDwAAAAmGsApgVBe6+7T7QIvqGrw629mryqIcfffe0K6+Bdm0+gIAAGBqEFYBhHX0VIe21TfplT9ca/X9wQPLdPuiYqXS6gsAAIApRlgFcJU/ENTeY23aWn9SB5ouKjs9VY/UlmvL2motLKHVFwAAALFDWAWg9h6fdu1v1o5Gr8509KqiKFvf+eMaPVxbQasvAAAAXEFYBZLYsdOhVt+Xj4Raff9owSx9f+My3bGYVl8AAAC4i7AKJBl/IKjX323Tz+qbtP9ku7LTU/XwzeXasq5a19PqCwAAgDhBWAWSRHuPT7sPNGtHg1enO3pVPiNb395Qo0dqK1SQQ6svAAAA4gthFUhw757udFp9T6nPH9S6+TP1Z59fqs/UlNDqCwAAgLhFWAUSkD8Q1BvH2/SzN5u072S7stJT9ODNoV19F82h1RcAAADxj7AKJJCLPT7tPtCiHY1enbp0RWWF2frWhsV6pLZChTkZbpcHAAAARIywCiSA42dCrb4vHQ61+q69bqa+d98S3UWrLwAAAKYpwiowTQWCVq+/26at9SfV+FGo1fcLN5Vpy7pqLZ6T73Z5AAAAwIQQVoFp5tJln1440KLtDddafZ++Z7Eera3QDA+tvgAAAEgMhFVgmnj/bJe21jfppcOt6u0Pas11RfruvTW6q6ZEaakpbpcHAAAATCrCKhDHAkGrN463aeubTWr46IIy01L0wI2hVt+aubT6AgAAIHERVoE41HG5Xy8cbNb2Bq9aL15RaUGWvrF+sTbdQqsvAAAAkgNhFYgj/97mtPq+dUpX+gNaNa9I395Qo88uodUXAAAAyYWwCrgsELT6zXvntLX+pN48EWr1vX9lqNV3SSmtvgAAAEhOhFXAJR2X+7XnYIu2Nzappf2K5hZk6b+uX6RNt1SqiFZfAAAAJDnCKhBjHzitvr8YaPWtLtI376nR52j1BQAAAK4irAIxMNDqu62+Sf/vxHllpKVo44pSbVlXrWVlBW6XBwAAAMQdwiowhc539+mFAy16fl+zTl26ojn5Wfr63Yu06ZYKzczNdLs8AAAAIG4RVoFJZq3VW80XVdfg1WvvnJUvENS6+TP1nT+u0V1LSpROqy8AAAAwJsIqMEku+/x6+fBp1TV6dfxMp/Iy0/TY6ko9saZSC4rz3C4PAAAAmFYIq8AEnTjXrR2NXv38UKu6+vxaPCdPP3xguTauLJUnkz9iAAAAwHjwkzQwDv5AUG8cb9P2Bq/qP7yg9FSjDcvnavOaKt1cNUPGGLdLBAAAAKY1wioQhXOdvdq1v0W79jfrbGevSgtCGyY9Uluh2XlsmAQAAABMFsIqMAZrrfadbFddo1d7j56VP2j16etn6/v3L9Odi4uVmsIsKgAAADDZCKvACLp6+/XS4VOqa/Dqg3PdKshO15fXVevxNVWaN8vjdnkAAABAQiOsAkO8f7ZLdY1NeumtU+rxBbS8rED//aEbdN8NpcrOSHW7PAAAACApEFYBST5/UHuPnVVdg1f7m9qVkZai+24o1ea1VVpZUeh2eQAAAEDSIawiqZ2+dEW79jdr1/4Wne/uU0VRtr55z2I9UluhGZ4Mt8sDAAAAkhZhFUknGLSq//CC6hqb9Pq7bbKS7lxUrCfWVum2hbOVwoZJAAAAgOsIq0gaHVf69eKhVu1s9Oqj8z0q8mToyU/P1+OrK1VRlON2eQAAAAAGIawi4R091aEdjV69fOSUevuDuqmyUD9+dIXuWTZXWelsmAQAAADEozHDqjHmOUn3SjpnrV3mnCuS9IKkaklNkh6x1l40xhhJfytpg6TLkr5srX3L+TVbJH3H+c/+hbV22+T+VoBrevsD+pejZ7S9wavDzZeUlZ6i+1eW6Yk1VVpWVuB2eQAAAADGEMnM6lZJ/0vS9kHnnpb0a2vts8aYp53jb0i6R9JC52u1pJ9IWu2E22ck1Uqykg4ZY1611l6crN8IIEkt7Ze1c1+z9hxsUXuPT9fN8uh79y7RgzeXqyA73e3yAAAAAERozLBqrf29MaZ6yOmNkm53nm+T9DuFwupGSduttVZSozGm0Bgz13nv69badkkyxrwuab2kXRP+HSDpBYNW//bBx9rR4NVv3j8nI+mzS0q0eU211s2fyYZJAAAAwDQ03jWrJdbaM5JkrT1jjCl2zpdJahn0vlbn3EjnhzHGPCnpSUmqrKwcZ3lIBhd7fNpzsEU79zWruf2yZuVm6qk7FuiLqypVWpjtdnkAAAAAJmCyN1gKN4VlRzk//KS1P5X0U0mqra0N+x4ktyMtl1TX4NUv3z4tnz+oVfOK9PW7KVIQmwAAEypJREFUF+nupXOUkZbidnkAAAAAJsF4w2qbMWauM6s6V9I553yrpIpB7yuXdNo5f/uQ878b52cjCV3xBfTLt09rR6NXb7d2yJORqkdqy/XEmiotnpPvdnkAAAAAJtl4w+qrkrZIetZ5fGXQ+aeMMbsV2mCpwwm0eyX90Bgzw3nf5yR9c/xlI1mcPN+jnY1e/eOhVnVc6dfC4lx9f+NS3X9jmfKy2DAJAAAASFSR3Lpml0KzorOMMa0K7er7rKQ9xpivSmqW9LDz9tcUum3NCYVuXfMVSbLWthtjvi/pgPO+Px/YbAkYKhC0+s1751TX6NXv//1jpaUY3b1sjjavqdLqeUUK3SEJAAAAQCIzoY1741Ntba09ePCg22UgRs539+mFAy16fl+zTl26opL8TD22qkqbVlWoJD/L7fIAAAAATDJjzCFrbW241yZ7gyUgKtZaHfJeVF2jV6+9c0b9AatbF8zUd++t0WdqSpSeyoZJAAAAQDIirMIVPX1+vXLktOoavTp+plN5mWl6fHWVnlhTpQXFuW6XBwAAAMBlhFXE1IlzXdrR2KyfH2pVV59fNXPz9ZdfWK6NK0uVk8FwBAAAABBCOsCU6w8E9ca7bapr9Kr+wwvKSE3RhuVztHltlW6qnMGGSQAAAACGIaxiypzr7NXz+5u1a3+z2jr7VFaYra/fvUiP3lKhWbmZbpcHAAAAII4RVjGprLVq/KhdOxq92nvsrPxBq9uun60f3F+lOxYXKzWFWVQAAABgwvx9Ul+35HO++rolX9e1cwPHyx6Uiq5zu9pxIaxiUnT19uulw6dU1+DVB+e6VZCdrq/cWq3HV1epepbH7fIAAAAAdwX8Q4Jlt9TXFeXxoEAa7I/sc4uXElaRnN4726m6Bq9eOnxKl30B3VBeoP/x0A26b0WpstJT3S4PAAAAGJ9gUOrviTxIjhQqB479VyL7XJMiZeRJmblSRu61x9ziTx5n5obel+G59nzoa5m5Utr0XX5HWEXUfP6gfnXsrHY0eLW/qV2ZaSm6b0WpNq+p0oqKQrfLAwAAQDKyVvL3jtwOO+pxuPDZI8lG9tkZg0OiJxQU88uGhMu8yI7TsyU2IJVEWEUUTl+6ouf3NWv3gWad7/apsihH39qwWA/fXKEZngy3ywMAAMB04/eFCYrjCJYDjzYQ2eemZQ2fgfTMlormRRcsM3OldI+UkjK136ckRVjFqIJBqzc/PK+6Bq/eON4mK+kzi4v1xJoqfXrhbKWwYRIAAEDyCAYiXGfZE1n4DPgi+9yU9OGtrlkFodnLUYOlJ3x7bCoxaDrg/xLC6rjSrxcPtWpno1cfne9RkSdDf3LbfD22qlIVRTlulwcAAIBIWBt5cIzkeELrLj1Szqzw6yrHOp7G6y4xfoRVfML7Z7u0vaHp6oZJN1UW6m8eXal7ls9RZhobJgEAAEypSV136YTUSNddpnuGB0XWXcJFhFXIHwjqjeNt2lbvVcNHF5SRlqKNK0q1ZV21lpUVuF0eAABAfBtr3aWvJ7pbksRq3eXQ56y7RJwhrCax9h6fdu1v1s5Gr0539KqsMFvfWL9Yj95SoSI2TAIAAIkqGBjjNiOjBMlJX3eZL+XPjbwddnDATE2f2u8T4DLCahJ6p7VDW+ub9Mu3T8vnD+rWBTP1zOeX6q6aEqWyYRIAAIg3k7bu0vlv9F+O7HNNyvAZyMxc1l0CMUJYTRI+f1D/cvSMttY36XDzJeVkpOrR2gp9aW2VFpbkuV0eAABIJKOuuxzrFiQj3JIklusur96SJId1l4CLCKsJrq2zVzv3Nev5fc06392nebM8eua+JXrw5nLlZ9E6AgAAHIH+CG9JEoN1lzmzpBnVI992ZKRj1l0CCYWwmoCstTrovaht9U361dGzClirOxYVa8u6an1qwSzujQoAQCKY6LrLoZv+BPoi+9yUtOGzkZl5rLsEMOkIqwmktz+gV46c0rZ6r94906n8rDR9eV21Nq+tUtVMj9vlAQCQ3MKtu/T1jH8WM9J1lzJh2l7Hue4ywxNad0lrLIAYIKwmgJb2y9rR6NULB1t06XK/FpXk6YcPLNf9N5YqJ4P/xQAAjIu1kr9vnO2wU7HusnTk246MtgaTdZcApimSzDRlrdWbJy5oa32Tfv1em1KM0d1LS/SltdVaPa9Ihr+UAADJaNzrLnvCvyfoj+xzUzOHB8WcmVJhVXS7xWbkhmYvU1Kn9vsEANMAYXWa6e7z6xdvtWpbfZM+/LhHMz0Z+trtC/TY6kqVFma7XR4AANEJu+4ymluSDAmfE1l3mZEr5c1h3SUAxAnC6jTx4cfdqmvw6sVDreru82tFeYH++pEV2rB8rrLS+ddXAECMWBtaKznudtjJXndZNUKQ9IzeHsu6SwCIe4TVOBYIWv32vXPa1tCk//vBeaWnGt17Q6m2rKvWyopCt8sDAEwH41532TPy/TFtMLLPTveEQuPgoJg3N/J7XbLuEgCSGmE1Dl267NOegy2qa/Sqpf2KSvIz9aefvV6bVlVqdl6m2+UBAKba4HWXV3eLHasddpT22AmtuyySCitZdwkAiDnCahw5fqZT2+qb9PKRU+rtD2rVvCI9vb5Gn1taovRUbnANAHErGIxynaWb6y5HaY9l3SUAII4QVl3WHwjqX4+1aVtDk/afbFdWeooeuLFMm9dUa0lpvtvlAUBiinbd5bANgIaEzf6eCD946LpLJzgWVkbeDsu6SwBAkiCsuuR8d5927WvWzn3NOtvZq4qibH17Q40eri1XYU6G2+UBQHy5uu4y3DrKccxiRrXuMmd4UMydI81k3SUAAFOJsBpjR1ouaVt9k/757TPyBYL61MJZ+sEDy3T7omKlpvADDIAEEvBPTrCM2bpLT/hbkrDuEgAAVxBWY6DPH9A/v31G2+qb9IfWDuVmpumx1ZXavLZK82fnul0eAIREs+7S1xOmdXbIe/y9kX1u2HWXHim3JLpZS9ZdAgCQUAirU+hMxxXtbGzWrv3NutDj0/zZHv35xqX6wk3lys3kWw9ggqyV+q+M816XE1x3OfRelxm5UmEF6y4BAMCkITFNMmut9p1s1/aGJu091qagtbqrpkRb1lbr1gUzZfiBDEhu/r5xtsO6vO7yE889hEsAADDlCKuT5LLPr5cPn9b2hia9d7ZLBdnp+g9/NE9PrKlSRVGO2+UBGK9g0AmKA1+RtMOOMosZ7I/sc0dbdxluVvMT4ZJ1lwAAYPojrE5Q84XL2t7QpD0HW9TZ61fN3Hz96MHl+vyKMmVn8MMh4JqBFtm+zlB47O289nzYucHHQ173dUX2eSY1fFDMLWbdJQAAwDgQVscpELT6jzsO6Y3jbUo1RuuXzdGWddWqrZpBqy8wUX6fExo7hoTIrmvhMlywHPxr+roi2z023RMKh1n5ocfMfClvTuhx8LnMvGtf4dpj07JojQUAAJhEhNVxSk0xKs7L1H+6c6EeX12pkvwst0sC3BcMDAmVo81ojhI8I9lFNiXdCZNOkMwqCG3wk7l0ePgMFzyz8kOzoKlcBgEAAOIRP6VNwA8eWO52CcDksFbqvzxKiBwcNDtGDp6+7rE/y6R8MkRm5oVaZWfO/2SQHPx6uOCZljn13xcAAAC4hrAKTHf+vrFD5GhrMgee28DYn5XuGT5DmV/qHBcMCZYjzGqykywAAAAiQFgF3BIMjBIiO8LMaI6wGVCgb+zPSs0cPkNZWBVm1tJppx3aLpuZR8ssAAAAYoqfPIFIDOws6+u5druScM/7L4/82sAtTgaCZn/P2J97tWV20Kxlbok0c+GQoDl0VnNI8KRlFgAAANMMYRWJJ+AfJUSOFDTDvTYkeMpGXkO6J9TumuFxdoz1hIJjQfnoG/4MXqeZmUfLLAAAAJIWYRXusTa06+tYs5XDAmW44DnoOJK22AEpadduP5IxKGDmlw8JmzmfDJ5Dg+jg52nZUkrK1H3fAAAAgCRAWEVkAv5Q2+qogfJyBLOVg17r75FsMPIaRpqtzC8dFBpzwgfIcMfpHiktY+q+ZwAAAADGjbCaiPy+UUJjd5StsM7zSO57OWDE2cqyIcExgkCZkSul54S+mK0EAAAAkgZh1U3Whm474uuRfF1RtMH2XNusJ9xrwf7Ia0jPGR4SM/OkvDmhmcfM3PABcmi4TB/UJstsJQAAAIAJinlYNcasl/S3klIl/W9r7bOxrmHSnDokXbk4PDD2dY8dNAeOI7m35YBwITGnSCqsCDOTOcbzTCdgpqRO3fcHAAAAAMYppmHVGJMq6e8kfVZSq6QDxphXrbXvxrKOSfOLJ6ULJ4afNynhg2Vu8aDAGEmw9FzbEZZNewAAAAAkkVjPrK6SdMJa+5EkGWN2S9ooaXqG1ft/EmrlHRYss7jdCAAAAABMQKzDapmklkHHrZJWD36DMeZJSU9KUmVlZewqG4+KVW5XAAAAAAAJKdZ9peGmG+0nDqz9qbW21lpbO3v27BiVBQAAAACIJ7EOq62SKgYdl0s6HeMaAAAAAABxLtZh9YCkhcaYecaYDEmbJL0a4xoAAAAAAHEupmtWrbV+Y8xTkvYqdOua56y1x2JZAwAAAAAg/sX8PqvW2tckvRbrzwUAAAAATB/cuBMAAAAAEHcIqwAAAACAuENYBQAAAADEHcIqAAAAACDuEFYBAAAAAHGHsAoAAAAAiDuEVQAAAABA3CGsAgAAAADiDmEVAAAAABB3CKsAAAAAgLhjrLVu1zAiY8zHkrxR/rJZks5PQTlITIwXRIPxgmgwXhANxguiwXhBNOJ9vFRZa2eHeyGuw+p4GGMOWmtr3a4D0wPjBdFgvCAajBdEg/GCaDBeEI3pPF5oAwYAAAAAxB3CKgAAAAAg7iRiWP2p2wVgWmG8IBqMF0SD8YJoMF4QDcYLojFtx0vCrVkFAAAAAEx/iTizCgAAAACY5girAAAAAIC4kzBh1Riz3hjzvjHmhDHmabfrQfwxxjQZY94xxhwxxhx0zhUZY143xnzgPM5wu064wxjznDHmnDHm6KBzYceHCfmfzvXmbWPMTe5VDjeMMF7+zBhzyrnGHDHGbBj02jed8fK+MeZud6qGW4wxFcaY3xpjjhtjjhlj/rNznmsMhhllvHCNwTDGmCxjzH5jzB+c8fLfnPPzjDH7nOvLC8aYDOd8pnN8wnm92s36x5IQYdUYkyrp7yTdI2mJpC8aY5a4WxXi1B3W2pWD7jX1tKRfW2sXSvq1c4zktFXS+iHnRhof90ha6Hw9KeknMaoR8WOrho8XSfqxc41Zaa19TZKcv482SVrq/Jq/d/7eQvLwS/pTa22NpDWSvuaMC64xCGek8SJxjcFwfZLutNaukLRS0npjzBpJP1JovCyUdFHSV533f1XSRWvtAkk/dt4XtxIirEpaJemEtfYja61P0m5JG12uCdPDRknbnOfbJN3vYi1wkbX295Lah5weaXxslLTdhjRKKjTGzI1NpYgHI4yXkWyUtNta22etPSnphEJ/byFJWGvPWGvfcp53STouqUxcYxDGKONlJFxjkphzneh2DtOdLyvpTkkvOueHXl8GrjsvSvqMMcbEqNyoJUpYLZPUMui4VaP/oUZyspL+1RhzyBjzpHOuxFp7Rgr95SCp2LXqEI9GGh9cczCSp5y2zecGLStgvOAqp+XuRkn7xDUGYxgyXiSuMQjDGJNqjDki6Zyk1yV9KOmStdbvvGXwmLg6XpzXOyTNjG3FkUuUsBruXwO4Jw+GutVae5NC7VVfM8Z82u2CMG1xzUE4P5E0X6E2rDOS/so5z3iBJMkYkyvp55L+i7W2c7S3hjnHmEkyYcYL1xiEZa0NWGtXSipXaFa9JtzbnMdpNV4SJay2SqoYdFwu6bRLtSBOWWtPO4/nJL2k0B/mtoHWKufxnHsVIg6NND645mAYa22b8wNDUNI/6FobHuMFMsakKxQ8dlprf+Gc5hqDsMKNF64xGIu19pKk3ym01rnQGJPmvDR4TFwdL87rBYp8WUvMJUpYPSBpobPrVYZCi8xfdbkmxBFjjMcYkzfwXNLnJB1VaJxscd62RdIr7lSIODXS+HhV0pecHTvXSOoYaOVD8hqypvABha4xUmi8bHJ2YJyn0KY5+2NdH9zjrAf7P5KOW2v/etBLXGMwzEjjhWsMwjHGzDbGFDrPsyXdpdA6599Kesh529Dry8B15yFJv7HWxu3MatrYb4l/1lq/MeYpSXslpUp6zlp7zOWyEF9KJL3krB9Pk/S8tfZXxpgDkvYYY74qqVnSwy7WCBcZY3ZJul3SLGNMq6RnJD2r8OPjNUkbFNrE4rKkr8S8YLhqhPFyuzFmpULtVE2S/kSSrLXHjDF7JL2r0C6fX7PWBtyoG665VdJmSe8468ok6VviGoPwRhovX+QagzDmStrm7ACdImmPtfafjDHvStptjPkLSYcV+gcQOY91xpgTCs2obnKj6EiZOA7SAAAAAIAklShtwAAAAACABEJYBQAAAADEHcIqAAAAACDuEFYBAAAAAHGHsAoAAAAAiDuEVQAAAABA3CGsAgAAAADizv8H17IWPMsnTp4AAAAASUVORK5CYII=", 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" ] @@ -390,24 +400,29 @@ } ], "source": [ - "plt.figure(figsize=(16,7))\n", + "plt.figure(figsize=(16, 7))\n", + "plt.title(\"Number of variables vs creation time for a model\")\n", "plt.plot(sizes, tt_ones, label=\"one_by_one\")\n", "plt.plot(sizes, tt_batches, label=\"var_list\")\n", "plt.legend()\n", + "plt.xlabel(\"size (number of variables)\")\n", + "plt.ylabel(\"model creation time (s)\")\n", "plt.show()" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "The conclusion is clear: for large number of variables (say, above 1000) prefer creating variables by batches (list or dict, ) using Docplex's prefedined routines (e.g. `Model.binary_var_list` or `Model.binary_var_dict`).\n", + "The conclusion is clear: for large number of variables (say, above 1000) prefer creating variables in batches (list or dict) using DOcplex predefined routines (e.g. `Model.binary_var_list` or `Model.binary_var_dict`).\n", "\n", "\n", - "**Rule #2**: use Docplex's specia;ized routines to create large number of variables.\n" + "**Rule #2**: use DOcplex's specialized routines to create a large number of variables.\n" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -415,6 +430,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -447,7 +463,7 @@ " # create variables as a dictionary indexed by the range\n", " ys = m.binary_var_dict(rsize, name=\"y\")\n", " # create a matrix of coefficients\n", - " k = {(i,j) : (i + (i+j) %3) for i in rsize for j in rsize}\n", + " k = {(i,j): (i + (i+j) %3) for i in rsize for j in rsize}\n", " for i in rsize:\n", " m.add(m.scal_prod([ys[i1] for i1 in rsize], [k[i,j] for j in rsize]) >= i, \"ct_%d\" % i)\n", " # for minimize, create a list of coefficients\n", @@ -460,6 +476,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -467,16 +484,16 @@ "\n", "Since DOcplex 2.9, Scalar product has a _functional_ variant, in which coefficients are computed on the fly, with no need to prepare a (possibly large) container of numbers. In some cases, this can save a significant time.\n", "\n", - "The method is `Model.dotf` takes two arguments:\n", + "The method is `Model.dotf` and it takes two arguments:\n", "\n", - " - a dictionary of variables, as create by the Model._var_dict methods\n", + " - a dictionary of variables, as created by the `Model._var_dict` methods\n", " - a Python function that takes a variable key and returns a float, the coefficient\n", " \n", - "In our example, keys are the integer from 0 to `size-1` .\n", - "The coefficient for y_j in the i_th constraint is i+(i+j)%3: here we do not need\n", + "In our example, the keys are integers from `0` to `size-1` .\n", + "The coefficient for `y_j` in the `i_th` constraint is `i+(i+j)%3`: here we do not need\n", "to precompute a list or a comprehension, but only use a lambda function to compute this.\n", "\n", - "We also leverage `dotf` for the objective, where the cost coefficient for y_j is (j+1)" + "We also leverage `dotf` for the objective, where the cost coefficient for `y_j` is `(j+1)`." ] }, { @@ -513,12 +530,13 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Tip #3: Add constraints in batches\n", "\n", - "Adding constraints to the model by batches using `Model.add_constraints()`\n", + "Adding constraints to the model in batches using `Model.add_constraints()`\n", "is usually more efficient.\n", "Try grouping constraints in lists or comprehensions (both work).\n", "\n", @@ -558,6 +576,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -601,6 +620,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -608,7 +628,7 @@ "\n", "DOcplex usually checks the argumens passed to methods. As this can be useful when writing the model to avoid errors, this checking comes with a runtime cost. When running a deployed model that has been thoroughly tested and tuned, you can remove all checks by adding the `checker=\"off\"` keyword argument to the model constructor.\n", "\n", - "Again, the next version is identical to the previous one , except that type-checking has been disabled." + "Again, the next version is identical to the previous one, except that type-checking has been disabled." ] }, { @@ -643,6 +663,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -683,12 +704,13 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# Summary\n", "\n", - "From version 1 to version 6 , model build time has decreased from 35s to 4s (on our platform). Results may well differ on other platforms, but still, this demonstrates that the way the model is built can greatly influence the performance.\n", + "From version 1 to version 6, model build time has decreased from 35s to 4s (on our platform). Results may well differ on other platforms, but still, this demonstrates that the way the model is built can greatly influence the performance.\n", "\n", "Here is a list of tricks to try to improve model building time:\n", "\n", @@ -696,10 +718,11 @@ " - Try using Model.dotf when applicable.\n", " - Add constraints in batches, not one by one\n", " - Try ignoring name generation (for large models)\n", - " - Try disabling argument checking\n" + " - Try disabling argument checking" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -850,7 +873,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] @@ -869,24 +892,27 @@ " print(\"try install matplotlib: pip install matplotlib\")\n", " raise\n", "\n", - "\n", - "labels = [ bl for (_, bl) in builders]\n", - "plt.figure(figsize=(16,7))\n", + "labels = [bl for (_, bl) in builders]\n", + "plt.figure(figsize=(16, 7))\n", + "plt.title(\"Number of variables vs creation time for a model\")\n", "for b in range(len(builders)):\n", " bts = [res[b,s]/1000 for s in sizes]\n", " plt.plot(sizes, bts, label=labels[b])\n", " plt.legend()\n", "\n", + "plt.xlabel(\"size (number of variables)\")\n", + "plt.ylabel(\"model creation time (s)\")\n", "plt.show()" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Average improvement\n", "\n", - "In the next cell, we compute the geometric mean of improvment between the first and last versions. Of course, results may differ depending on platform (and the model, too) but the idea is, applying the above rules may yield a significant improvement" + "In the next cell, we compute the geometric mean of improvement between the first and last versions. Of course, results may differ depending on platform (and the model, too) but the idea is, applying the above rules may yield a significant improvement." ] }, { @@ -903,7 +929,7 @@ } ], "source": [ - "# compute geomerical mean for all sizes\n", + "# compute geometrical mean for all sizes\n", "nb_builders = len(builders)\n", "ratios = {}\n", "for s in sizes:\n", @@ -912,12 +938,14 @@ " best = min(res[b, s] for b in range(nb_builders))\n", " r = (initial/best)\n", " ratios[s] = r\n", + "\n", "import math\n", "rgm = math.exp(sum(math.log(r) for r in ratios.values()) / float(nb_builders))\n", "print(\"* geometric mean of time improvement is {0:.1f}\".format(rgm))" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": true