House%2BBuilding%2BLocal.ipynb

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    "\n",
    "# House Building with worker skills\n",
    "\n",
    "This tutorial includes everything you need to set up decision optimization engines, and build constraint programming models to efficiently assign construction tasks to workers of different skill levels when you build houses in different locations. All data and instructions that you need to model and solve this problem are included.\n",
    "\n",
    "\n",
    "When you finish this tutorial, you'll have a foundational knowledge of _Prescriptive Analytics_.\n",
    "\n",
    ">This notebook is part of [Prescriptive Analytics for Python](https://rawgit.com/IBMDecisionOptimization/docplex-doc/master/docs/index.html).\n",
    "\n",
    ">This notebook requires a valid subscription to [Decision Optimization on Cloud](https://developer.ibm.com/docloud) or a local installation of CPLEX Optimizers.\n",
    "\n",
    "Some familiarity with Python is recommended. This notebook runs on Python 2."
   ]
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    " ## Table of contents \n",
    "\n",
    "-  [Describe the business problem](#Describe-the-business-problem)<br>\n",
    "-  [How decision optimization (prescriptive analytics) can help](#How--decision-optimization-can-help)<br>\n",
    "-  [Use decision optimization](#Use-decision-optimization)<br>\n",
    "-  [About detailed scheduling concepts](#About-detailed-scheduling-concepts)<br>\n",
    "    1.  [Download the library](#Download-library)<br>\n",
    "    2.  [Set up the engines](#Step-2:-Set-up-the-prescriptive-engine)<br>\n",
    "    3.  [Model the data](#Step-3:-Model-the-data)<br>\n",
    "    4.  [Set up the prescriptive model](#Step-4:-Set-up-the-prescriptive-model)<br>\n",
    "        4.1 [Define the decision variables](#Define-the-decision-variables)<br>\n",
    "        4.2 [Express the business constraints](#Express-the-business-constraints)<br>\n",
    "        4.3 [Express the objective](#Express-objective)<br>\n",
    "        4.4 [Solve with Decision Optimization solve service](#Solve-with-Decision-Optimization-solve-service)<br>\n",
    "    5. [Investigate the solution and run an example analysis](#Step-5:-Investigate-the-solution-and-then-run-an-example-analysis)<br>\n",
    "-  [Summary](#Summary)<br>\n",
    "****\n",
    "   "
   ]
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    "### Describe the business problem\n",
    "\n",
    "* This is a problem of building five houses in different locations; the masonry, roofing, painting, etc. must be scheduled. Some tasks must necessarily take place before others and these requirements are expressed through precedence constraints.\n",
    "\n",
    "* There are three workers, and each worker has a given skill level for each task. Each task requires one worker; the worker assigned must have a non-null skill level for the task. A worker can be assigned to only one task at a time.\n",
    "\n",
    "* Each house has a completion deadline. \n",
    "\n",
    "* The objective is to maximize the skill levels of the workers assigned to the tasks."
   ]
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    "## How  decision optimization can help\n",
    "\n",
    "Prescriptive analytics (decision optimization) technology recommends actions that are based on desired outcomes. It considers specific scenarios, resources, and knowledge of past and current events. With this insight, your organization can make better decisions and have greater control over business outcomes.\n",
    "\n",
    "Prescriptive analytics is the next step on the path to insight-based actions. It creates value through synergy with predictive analytics, which analyzes data to predict future outcomes. Prescriptive analytics takes that insight to the next level by suggesting the optimal way to handle a future situation. Organizations that act fast in dynamic conditions and make superior decisions in uncertain environments gain a strong competitive advantage.\n",
    "\n",
    "With prescriptive analytics, you can:\n",
    "\n",
    "* Automate the complex decisions and trade-offs to better manage your limited resources.\n",
    "    \n",
    "* Take advantage of a future opportunity or mitigate a future risk.\n",
    "    \n",
    "* Proactively update recommendations based on changing events.\n",
    "    \n",
    "* Meet operational goals, increase customer loyalty, prevent threats and fraud, and optimize business processes.\n"
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    "<h2>About detailed scheduling concepts</h2>\n",
    "<p>\n",
    "<ul>\n",
    "<li> Scheduling consists of assigning starting and completion times to a set of activities while satisfying different types of constraints (resource availability, precedence relationships, … ) and optimizing some criteria (minimizing tardiness, …)\n",
    "<!-- <img src = \"./house_building_utils/activity.png\" > -->\n",
    "<img src = \"https://github.com/IBMDecisionOptimization/docplex-examples/blob/master/examples/cp/jupyter/house_building_utils/activity.PNG?raw=true \" >\n",
    "<li> Time is considered as a continuous dimension: domain of possible start/completion times for an activity is potentially very large\n",
    "<li>Beside start and completion times of activities, other types of decision variables are often involved in real industrial scheduling problems (resource allocation, optional activities …)\n",
    "</ul>"
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   "source": [
    "## Use decision optimization"
   ]
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    "<a id=\"Download-library\"></a>\n",
    "## 1. Download the DOcplex library\n",
    "\n",
    "DOcplex is the Decision Optimization CPLEX Modeling library for Python. This library includes two modules, Mathematical Programming Modeling (DOcplex.MP ) and Constraint Programming Modeling (DOcplex.CP).\n",
    "\n",
    "Run the following cells to install the library. The `real.prefix` in the code is used to detect whether the script is running inside a virtual environment, and adjust the install procedure accordingly."
   ]
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     "text": [
      "Requirement already satisfied: docplex in /user-home/1001/.local/lib/python2.7/site-packages\n",
      "Requirement already satisfied: futures in /user-home/1001/.local/lib/python2.7/site-packages (from docplex)\n",
      "Requirement already satisfied: requests in /user-home/1001/.local/lib/python2.7/site-packages (from docplex)\n",
      "Requirement already satisfied: docloud>=1.0.257 in /opt/conda/lib/python2.7/site-packages (from docplex)\n",
      "Requirement already satisfied: six in /opt/conda/lib/python2.7/site-packages (from docplex)\n",
      "Requirement already satisfied: idna<2.7,>=2.5 in /user-home/1001/.local/lib/python2.7/site-packages (from requests->docplex)\n",
      "Requirement already satisfied: urllib3<1.23,>=1.21.1 in /user-home/1001/.local/lib/python2.7/site-packages (from requests->docplex)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /user-home/1001/.local/lib/python2.7/site-packages (from requests->docplex)\n",
      "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /user-home/1001/.local/lib/python2.7/site-packages (from requests->docplex)\n",
      "Requirement already satisfied: enum34 in /opt/conda/lib/python2.7/site-packages (from docloud>=1.0.257->docplex)\n"
     ]
    }
   ],
   "source": [
    "!pip install docplex"
   ]
  },
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   "source": [
    "import docplex.cp"
   ]
  },
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   "source": [
    "Note that the more global package <i>docplex</i> contains another subpackage <i>docplex.mp</i> that is dedicated to Mathematical Programming, another branch of optimization."
   ]
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    "<a id=\"Step-2:-Set-up-the-prescriptive-engine\"></a>\n",
    "## 2. Set up the prescriptive engine\n",
    "\n",
    "To access the DOcplexcloud solve service, perform the following steps:\n",
    "\n",
    "* Subscribe to the [Decision Optimization on Cloud](https://developer.ibm.com/docloud) (DOcplexcloud) service.\n",
    "* Get the service base URL and personal API key.\n",
    "* Enter the URL and API key in the cell below, enclosed in quotation marks (\"\"), and run the cell. \n",
    "\n",
    "__Note:__ For a persistent setting, create a Python configuration file *docloud_config.py* in a location that is visible to PYTHONPATH."
   ]
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   "source": [
    "# Initialize IBM Decision Optimization credentials\n",
    "SVC_URL = \"ENTER YOUR URL HERE\" \n",
    "SVC_KEY = \"ENTER YOUR CREDENTIAL ID HERE\""
   ]
  },
  {
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   "source": [
    "And for display of the solution, ensure latest version of `matplotlib` is available:"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "try:\n",
    "   import matplotlib\n",
    "   if matplotlib.__version__ < \"1.4.3\":\n",
    "        !pip install --upgrade matplotlib\n",
    "except:\n",
    "   !pip install --user matplotlib"
   ]
  },
  {
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   "source": [
    "Now, we need to import all required modeling functions that are provided by the <i>docplex.cp</i> package:"
   ]
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   "source": [
    "from docplex.cp.model import *\n",
    "from sys import stdout\n",
    "from collections import namedtuple"
   ]
  },
  {
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   "metadata": {
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   "source": [
    "<div class=\"alert alert-block alert-info\"> Note: This notebook requires a full subscription to CPLEX 12.7.1 or above, that is with a valid docplexcloud url and key.</div> "
   ]
  },
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   "source": [
    "<a id=\"Step-3:-Model-the-data\"></a>\n",
    "## 3. Model the data"
   ]
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   "source": [
    "Planning contains the number of houses and the max amount of periods (<i>days</i>) for our schedule."
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "NB_HOUSES = 5\n",
    "MAX_AMOUNT_OF_PERIODS = 318\n",
    "HOUSES = range(1, NB_HOUSES + 1)"
   ]
  },
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    "All tasks must start and end between 0 and the max amount of periods."
   ]
  },
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   "source": [
    "period_domain = (0, MAX_AMOUNT_OF_PERIODS)"
   ]
  },
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   "source": [
    "For each task type in the house building project, the following table shows the duration of the task in days along with the tasks that must be finished before the task can start. A worker can only work on one task at a time; each task, once started, may not be interrupted.\n",
    "<p>\n",
    "\n",
    "| *Task* | *Duration (Days)* | *Preceding tasks* |\n",
    "|---|---|---|\n",
    "| masonry \t| 35 |\t|\n",
    "| carpentry | 15 | masonry |\n",
    "| plumbing \t| 40 | masonry |\n",
    "| ceiling \t| 15 | masonry |\n",
    "| roofing \t| 5  | carpentry |\n",
    "| painting \t| 10 | ceiling |\n",
    "| windows \t| 5  | roofing |\n",
    "| facade \t| 10 | roofing, plumbing |\n",
    "| garden \t| 5  | roofing, plumbing |\n",
    "| moving \t| 5  | windows, facade, garden, painting | "
   ]
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   "source": [
    "##### Tasks' durations"
   ]
  },
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   "cell_type": "code",
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   "source": [
    "Task = (namedtuple(\"Task\", [\"name\", \"duration\"]))\n",
    "TASKS = {Task(\"masonry\",   35),\n",
    "         Task(\"carpentry\", 15),\n",
    "         Task(\"plumbing\",  40),\n",
    "         Task(\"ceiling\",   15),\n",
    "         Task(\"roofing\",    5),\n",
    "         Task(\"painting\",  10),\n",
    "         Task(\"windows\",    5),\n",
    "         Task(\"facade\",    10),\n",
    "         Task(\"garden\",     5),\n",
    "         Task(\"moving\",     5),\n",
    "        }"
   ]
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   "source": [
    "##### Tasks' precedences"
   ]
  },
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   "source": [
    "TaskPrecedence = (namedtuple(\"TaskPrecedence\", [\"beforeTask\", \"afterTask\"]))\n",
    "TASK_PRECEDENCES = {TaskPrecedence(\"masonry\",   \"carpentry\"),\n",
    "                    TaskPrecedence(\"masonry\",   \"plumbing\"),\n",
    "                    TaskPrecedence(\"masonry\",   \"ceiling\"),\n",
    "                    TaskPrecedence(\"carpentry\", \"roofing\"),\n",
    "                    TaskPrecedence(\"ceiling\",   \"painting\"),\n",
    "                    TaskPrecedence(\"roofing\",   \"windows\"),\n",
    "                    TaskPrecedence(\"roofing\",   \"facade\"),\n",
    "                    TaskPrecedence(\"plumbing\",  \"facade\"),\n",
    "                    TaskPrecedence(\"roofing\",   \"garden\"),\n",
    "                    TaskPrecedence(\"plumbing\",  \"garden\"),\n",
    "                    TaskPrecedence(\"windows\",   \"moving\"),\n",
    "                    TaskPrecedence(\"facade\",    \"moving\"),\n",
    "                    TaskPrecedence(\"garden\",    \"moving\"),\n",
    "                    TaskPrecedence(\"painting\",  \"moving\"),\n",
    "                   }"
   ]
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   "source": [
    "There are three workers with varying skill levels in regard to the ten construction tasks. If a worker has a skill level of zero for a task, he may not be assigned to the task.\n",
    "<p>\n",
    "\n",
    "| *Task* | *Joe* | *Jack* | *Jim* |\n",
    "|---|---|---|---|\n",
    "|masonry   |9 |\t5 |\t0|\n",
    "|carpentry |7 |\t0 |\t5|\n",
    "|plumbing  |0 |\t7 |\t0|\n",
    "|ceiling   |5 |\t8 |\t0|\n",
    "|roofing   |6 |\t7 |\t0|\n",
    "|painting  |0 |\t9 |\t6|\n",
    "|windows   |8 |\t0 |\t5|\n",
    "|façade    |5 |\t5 |\t0|\n",
    "|garden    |5 |\t5 |\t9|\n",
    "|moving    |6 |\t0 |\t8|"
   ]
  },
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   "source": [
    "##### Workers' names"
   ]
  },
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   "cell_type": "code",
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   "source": [
    "WORKERS = {\"Joe\", \"Jack\", \"Jim\"}"
   ]
  },
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   "source": [
    "##### Workers' name and level for each of their skill"
   ]
  },
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   "cell_type": "code",
   "execution_count": 11,
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   "source": [
    "Skill = (namedtuple(\"Skill\", [\"worker\", \"task\", \"level\"]))\n",
    "SKILLS = {Skill(\"Joe\",  \"masonry\",   9),\n",
    "          Skill(\"Joe\",  \"carpentry\", 7),\n",
    "          Skill(\"Joe\",  \"ceiling\",   5),\n",
    "          Skill(\"Joe\",  \"roofing\",   6),\n",
    "          Skill(\"Joe\",  \"windows\",   8),\n",
    "          Skill(\"Joe\",  \"facade\",    5),\n",
    "          Skill(\"Joe\",  \"garden\",    5),\n",
    "          Skill(\"Joe\",  \"moving\",    6),\n",
    "          Skill(\"Jack\", \"masonry\",   5),\n",
    "          Skill(\"Jack\", \"plumbing\",  7),\n",
    "          Skill(\"Jack\", \"ceiling\",   8),\n",
    "          Skill(\"Jack\", \"roofing\",   7),\n",
    "          Skill(\"Jack\", \"painting\",  9),\n",
    "          Skill(\"Jack\", \"facade\",    5),\n",
    "          Skill(\"Jack\", \"garden\",    5),\n",
    "          Skill(\"Jim\",  \"carpentry\", 5),\n",
    "          Skill(\"Jim\",  \"painting\",  6),\n",
    "          Skill(\"Jim\",  \"windows\",   5),\n",
    "          Skill(\"Jim\",  \"garden\",    9),\n",
    "          Skill(\"Jim\",  \"moving\",    8)\n",
    "          }"
   ]
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   "source": [
    "##### Utility functions"
   ]
  },
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   "source": [
    "**find_tasks**: returns the task it refers to in the TASKS vector."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
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   "source": [
    "def find_tasks(name):\n",
    "    return next(t for t in TASKS if t.name == name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "**find_skills**: returns the skill it refers to in the SKILLS vector."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true,
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   },
   "outputs": [],
   "source": [
    "def find_skills(worker, task):\n",
    "    return next(s for s in SKILLS if (s.worker == worker) and (s.task == task))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "**find_max_level_skill**: returns the tuple \"skill\" where a worker's level is the maximum for a given task."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
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   "source": [
    "def find_max_level_skill(task):\n",
    "    st = [s for s in SKILLS if s.task == task]\n",
    "    return next(sk for sk in st if sk.level == max([s.level for s in st]))"
   ]
  },
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   "source": [
    "<a id=\"Step-4:-Set-up-the-prescriptive-model\"></a>\n",
    "## 4. Set up the prescriptive model"
   ]
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   "source": [
    "<h3>Create the model container</h3>\n",
    "<p>\n",
    "The model is represented by a Python object that is filled with the available model elements (variables, constraints, objective function, etc). The first thing to do is then to create such an object:"
   ]
  },
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   "source": [
    "mdl = CpoModel(name=\"HouseBuilding\")"
   ]
  },
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   "source": [
    "<a id=\"Define-the-decision-variables\"></a>\n",
    "### 4.1 Define the decision variables"
   ]
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   "source": [
    "<h5><i><font color=blue>Concept: interval variable</font></i></h5>\n",
    "<p>\n",
    "<ul>\n",
    "<li> What for?<br>\n",
    "<blockquote> Modeling an interval of time during which a particular property holds <br>\n",
    "(an activity executes, a resource is idle, a tank must be non-empty, …)</blockquote> \n",
    "<li> Example:<br>\n",
    "<blockquote><code><font color=green>interval_var(start=(0,1000), end=(0,1000), size=(10,20))</font></code>\n",
    "</blockquote> \n",
    "<!-- <img src = \"./house_building_utils/intervalVar.png\" > -->\n",
    "<img src = \"https://github.com/IBMDecisionOptimization/docplex-examples/blob/master/examples/cp/jupyter/house_building_utils/intervalVar.PNG?raw=true\" >\n",
    "<li>Properties:\n",
    "<ul>\n",
    "<li>The **value** of an interval variable is an integer interval [start,end) \n",
    "<li>**Domain**  of possible values: [0,10), [1,11), [2,12),... [990,1000), [0,11), [1,12),...\n",
    "<li>Domain of interval variables is represented **compactly** in CP Optimizer (a few bounds: smin, smax, emin, emax, szmin, szmax)\n",
    "</ul>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "For each `house`, an interval variable is created for each task.<br>\n",
    "This interval must start and end inside the `period_domain` and its duration is set as the value stated in TASKS definition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "tasks = {}   # dict of interval variable for each house and task\n",
    "for house in HOUSES:\n",
    "    for task in TASKS:\n",
    "        tasks[(house, task)] = interval_var(start=period_domain,\n",
    "                                            end=period_domain,\n",
    "                                            size=task.duration,\n",
    "                                            name=\"house {} task {}\".format(house, task))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "<h5><i><font color=blue>Concept: optional interval variable</font></i></h5>\n",
    "<p>Interval variables can be defined as being **optional**; that is, it is part of the decisions of the problem to decide whether the interval will be **present** or **absent** in the solution.\n",
    "<ul>\n",
    "<br>\n",
    "<li> What for?<br>\n",
    "<blockquote> Modeling optional activities, alternative execution modes for activities, and … most of the discrete decisions in a schedule</blockquote> \n",
    "<li> Example:<br>\n",
    "<blockquote><code><font color=green>interval_var(</font><font color=red>optional=True</font><font color=green>, start=(0,1000), end=(0,1000), size=(10,20))</font></code>\n",
    "</blockquote> \n",
    "<li>Properties:\n",
    "<ul>\n",
    "<li>An optional interval variable has an additional possible value in its domain (absence value)\n",
    "<li>**Optionality** is a powerful property that you must learn to leverage in your models\n",
    "</ul>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "For each house, an __optional__ interval variable is created for each skill.<br>\n",
    "With skill being a tuple (worker, task, level), this means that for each house, an __optional__ interval variable is created for each couple worker-task such that the skill level of this worker for this task is > 0.<p>\n",
    "The \"**set_optional()**\" specifier allows a choice between different variables, thus between different couples house-skill.\n",
    "This means that the engine decides if the interval will be present or absent in the solution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "wtasks = {}  # dict of interval variable for each house and skill\n",
    "for house in HOUSES:\n",
    "    for skill in SKILLS:\n",
    "        iv = interval_var(name='H' + str(house) + '-' + skill.task + '(' + skill.worker + ')')\n",
    "        iv.set_optional()\n",
    "        wtasks[(house, skill)] = iv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "<a id=\"Express-the-business-constraints\"></a>\n",
    "### 4.2 Express the business constraints"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "<h5>Temporal constraints</h5>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "<h5><i><font color=blue>Concept: precedence constraint</font></i></h5>\n",
    "<p>\n",
    "<ul>\n",
    "<li> What for?<br>\n",
    "<ul>\n",
    "<li>Modeling temporal constraints between interval variables\n",
    "<li>Modeling constant or variable minimal delays\n",
    "</ul>\n",
    "<li>Properties\n",
    "<blockquote>Semantic of the constraints handles optionality (as for all constraints in CP Optimizer).<br>\n",
    "Example of **endBeforeStart**:<br>\n",
    "<code><font color=green>end_before_start(a,b,z)</font></code><br>\n",
    "present(a) <font color=red>AND</font> present(b)  &Implies;  end(a)+z &LessSlantEqual; start(b) \n",
    "</blockquote>\n",
    "<ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "The tasks in the model have precedence constraints that are added to the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "for h in HOUSES:\n",
    "    for p in TASK_PRECEDENCES:\n",
    "        mdl.add(end_before_start(tasks[(h, find_tasks(p.beforeTask))], tasks[(h, find_tasks(p.afterTask))]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "<h5>Alternative workers</h5>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "<h5><i><font color=blue>Concept: alternative constraint</font></i></h5>\n",
    "<p>\n",
    "<ul>\n",
    "<li> What for?<br>\n",
    "<ul>\n",
    "<li>Modeling alternative resource/modes/recipes \n",
    "<li>In general modeling a discrete selection in the schedule \n",
    "</ul><br>\n",
    "<li> Example:<br>\n",
    "<blockquote><code><font color=green>alternative(a,[b1,...,bn])</font></code>\n",
    "</blockquote> \n",
    "<!-- <img src = \"./house_building_utils/alternative.png\" > -->\n",
    "<img src = \"https://github.com/IBMDecisionOptimization/docplex-examples/blob/master/examples/cp/jupyter/house_building_utils/alternative.PNG?raw=true\" >\n",
    "</ul>\n",
    "<p>Note: Master interval variable **a** can be optional.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "To constrain the solution so that exactly one of the interval variables wtasks associated with a given task of a given house is to be present in the solution, an \"**alternative**\" constraint is used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "for h in HOUSES:\n",
    "    for t in TASKS:\n",
    "        mdl.add(alternative(tasks[(h, t)], [wtasks[(h, s)] for s in SKILLS if (s.task == t.name)], 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "<h5>No overlap constraint</h5>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "<h5><i><font color=blue>Concept: No-overlap constraint</font></i></h5>\n",
    "<p>\n",
    "<ul>\n",
    "<li> Constraint **noOverlap** schedules a group of interval variables in such a way that they do not overlap in time.\n",
    "<li> Absent interval variables are ignored.\n",
    "<li>It is possible to constrain minimum delays between intervals using transition matrix.\n",
    "<li>It is possible to constrain the first, last in the sequence or next or preceding interval.\n",
    "</ul>\n",
    "<!-- <img src = \"./house_building_utils/noOverlap.png\" > -->\n",
    "<img src = \"https://github.com/IBMDecisionOptimization/docplex-examples/blob/master/examples/cp/jupyter/house_building_utils/noOverlap.PNG?raw=true\" >"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "To add the constraints that a given worker can be assigned only one task at a given moment in time, a  **noOverlap** constraint is used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "for w in WORKERS:\n",
    "    mdl.add(no_overlap([wtasks[(h, s)] for h in HOUSES for s in SKILLS if s.worker == w]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "<a id=\"Express-objective\"></a>\n",
    "### 4.3 Express the objective"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "The presence of an interval variable in wtasks in the solution must be accounted for in the objective. Thus for each of these given tasks, the cost is incremented by the product of the skill level and the expression representing the presence of the interval variable in the solution.<p>\n",
    "The objective of this problem is to maximize the skill level used for all the tasks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "obj = sum([s.level * presence_of(wtasks[(h, s)]) for s in SKILLS for h in HOUSES])\n",
    "mdl.add(maximize(obj))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "The model is now completely defined. It is time to solve it !\n",
    "<p>\n",
    "__Note:__ To use the CP Optimizer solver available on the IBM Decision Optimization on Cloud service:\n",
    "<ul>\n",
    "<li> Register for the DOcplexcloud free trial and use it free for 30 days by using: https://developer.ibm.com/docloud/try-docloud-free\n",
    "<li> Get your access credentials (base URL and access key) by going this page: http://developer.ibm.com/docloud/docs/api-key/\n",
    "</ul>\n",
    "<p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "<a id=\"Solve-with-Decision-Optimization-solve-service\"></a>\n",
    "### 4.4 Solve with Decision Optimization solve service"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Solving model....\n"
     ]
    }
   ],
   "source": [
    "# Solve the model\n",
    "print(\"\\nSolving model....\")\n",
    "msol = mdl.solve(url=SVC_URL, key=SVC_KEY, TimeLimit=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "<a id =\"Step-5:-Investigate-the-solution-and-then-run-an-example-analysis\"></a>\n",
    "## 5. Investigate the solution and then run an example analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Solve status: Feasible\n",
      "Solve time: 10.0087778568\n",
      "\n",
      "List of tasks in increasing start order:\n",
      "From 0 to 35, masonry in house 4\n",
      "From 0 to 35, masonry in house 5\n",
      "From 35 to 70, masonry in house 1\n",
      "From 35 to 50, ceiling in house 5\n",
      "From 50 to 90, plumbing in house 4\n",
      "From 50 to 60, painting in house 5\n",
      "From 70 to 105, masonry in house 3\n",
      "From 90 to 130, plumbing in house 5\n",
      "From 105 to 140, masonry in house 2\n",
      "From 105 to 120, carpentry in house 3\n",
      "From 130 to 145, ceiling in house 3\n",
      "From 140 to 155, ceiling in house 4\n",
      "From 145 to 160, ceiling in house 2\n",
      "From 145 to 155, painting in house 3\n",
      "From 155 to 170, carpentry in house 1\n",
      "From 155 to 165, painting in house 4\n",
      "From 160 to 200, plumbing in house 3\n",
      "From 170 to 185, ceiling in house 1\n",
      "From 185 to 195, painting in house 1\n",
      "From 185 to 200, carpentry in house 5\n",
      "From 200 to 240, plumbing in house 1\n",
      "From 200 to 215, carpentry in house 4\n",
      "From 215 to 230, carpentry in house 2\n",
      "From 230 to 235, roofing in house 5\n",
      "From 235 to 240, garden in house 5\n",
      "From 235 to 240, windows in house 5\n",
      "From 240 to 245, roofing in house 2\n",
      "From 240 to 250, facade in house 5\n",
      "From 245 to 250, roofing in house 1\n",
      "From 250 to 255, garden in house 1\n",
      "From 250 to 255, windows in house 2\n",
      "From 250 to 255, roofing in house 4\n",
      "From 255 to 260, roofing in house 3\n",
      "From 255 to 260, garden in house 4\n",
      "From 255 to 260, windows in house 4\n",
      "From 260 to 265, windows in house 1\n",
      "From 260 to 300, plumbing in house 2\n",
      "From 260 to 265, garden in house 3\n",
      "From 265 to 270, windows in house 3\n",
      "From 265 to 270, moving in house 5\n",
      "From 270 to 280, facade in house 1\n",
      "From 280 to 285, moving in house 1\n",
      "From 280 to 290, facade in house 4\n",
      "From 290 to 300, facade in house 3\n",
      "From 290 to 295, moving in house 4\n",
      "From 300 to 305, garden in house 2\n",
      "From 300 to 310, painting in house 2\n",
      "From 300 to 310, facade in house 2\n",
      "From 305 to 310, moving in house 3\n",
      "From 310 to 315, moving in house 2\n"
     ]
    }
   ],
   "source": [
    "print(\"Solve status: \" + msol.get_solve_status())\n",
    "if msol.is_solution():\n",
    "    stdout.write(\"Solve time: \" + str(msol.get_solve_time()) + \"\\n\")\n",
    "    # Sort tasks in increasing begin order\n",
    "    ltasks = []\n",
    "    for hs in HOUSES:\n",
    "        for tsk in TASKS:\n",
    "            (beg, end, dur) = msol[tasks[(hs, tsk)]]\n",
    "            ltasks.append((hs, tsk, beg, end, dur))\n",
    "    ltasks = sorted(ltasks, key = lambda x : x[2])\n",
    "    # Print solution\n",
    "    print(\"\\nList of tasks in increasing start order:\")\n",
    "    for tsk in ltasks:\n",
    "        print(\"From \" + str(tsk[2]) + \" to \" + str(tsk[3]) + \", \" + tsk[1].name + \" in house \" + str(tsk[0]))\n",
    "else:\n",
    "    stdout.write(\"No solution found\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "#### Import graphical tools"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "*You can set __POP\\_UP\\_GRAPHIC=True__ if you prefer a pop up graphic window instead of an inline one.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "POP_UP_GRAPHIC=False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import docplex.cp.utils_visu as visu\n",
    "import matplotlib.pyplot as plt\n",
    "if not POP_UP_GRAPHIC:\n",
    "    %matplotlib inline\n",
    "#Change the plot size\n",
    "from pylab import rcParams\n",
    "rcParams['figure.figsize'] = 15, 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Drawing the solution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "#### Useful functions "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "With the aim to facilitate the display of tasks names, we keep only the ***n*** first characters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def compact_name(name,n): return name[:n]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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/IP986lFuf3g8zcKbc6ao0NImrSy6zau/rnFcbPtWO58q18k6cANc3e5qpqyb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\nQohK8n/k8x7qff+NlgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f93d8162310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if msol and visu.is_visu_enabled():\n",
    "    workers_colors = {}\n",
    "    workers_colors[\"Joe\"] = 'lightblue'\n",
    "    workers_colors[\"Jack\"] = 'violet'\n",
    "    workers_colors[\"Jim\"] = 'lightgreen'\n",
    "    visu.timeline('Solution per houses', 0, MAX_AMOUNT_OF_PERIODS)\n",
    "    for h in HOUSES:\n",
    "        visu.sequence(name=\"house \" + str(h))\n",
    "        for s in SKILLS:\n",
    "            wt = msol.get_var_solution(wtasks[(h,s)])\n",
    "            if wt.is_present():\n",
    "                color = workers_colors[s.worker]\n",
    "                wtname = compact_name(s.task,2)\n",
    "                visu.interval(wt, color, wtname)\n",
    "    visu.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "The purpose of this function is to compact the names of the different tasks with the aim of making the graphical display readable. </p>\n",
    "For example \"H3-garden\" becomes \"G3\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def compact_house_task(name):\n",
    "    loc, task = name[1:].split('-', 1)\n",
    "    return task[0].upper() + loc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "If the task is assigned to the most skilled user, the attribute `color` is light-green.\n",
    "Otherwise, `color` is salmon."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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s+uMza9YsUl9PrT8+t7xyC+26tgOgeH8xjwx/hHEzx3FO1Dnc0/6e+nNU55Yf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\n+Xr0GuicBXIbO7eyz5lR5yuU189wANZ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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f93c6c9e810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if msol and visu.is_visu_enabled():\n",
    "    visu.timeline('Solution per workers', 0, MAX_AMOUNT_OF_PERIODS)\n",
    "    for w in WORKERS:\n",
    "        visu.sequence(name=w)\n",
    "        for h in HOUSES:\n",
    "            for s in SKILLS:\n",
    "                if s.worker == w:\n",
    "                    wt = msol.get_var_solution(wtasks[(h,s)])\n",
    "                    if wt.is_present():\n",
    "                        ml = find_max_level_skill(s.task).level\n",
    "                        if s.level == ml:\n",
    "                            color = 'lightgreen'\n",
    "                        else:\n",
    "                            color = 'salmon'\n",
    "                        wtname = compact_house_task(wt.get_name())\n",
    "                        visu.interval(wt, color, wtname)\n",
    "    visu.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "<h4>Going further with Constraint Programming</h4>\n",
    "<ul>\n",
    "<li>The last available installable package is available on Pypi here: https://pypi.python.org/pypi/docplex\n",
    "\n",
    "<li>A complete set of modeling examples can be downloaded here: https://github.com/IBMDecisionOptimization/docplex-examples  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Summary\n",
    "\n",
    "You learned how to set up and use the IBM Decision Optimization CPLEX Modeling for Python to formulate a Constraint Programming model and solve it with IBM Decision Optimization on the cloud."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "##  References\n",
    "* [CPLEX Modeling for Python documentation](https://rawgit.com/IBMDecisionOptimization/docplex-doc/master/docs/index.html)\n",
    "* [Decision Optimization on Cloud](https://developer.ibm.com/docloud/)\n",
    "* [Decision Optimization documentation](https://datascience.ibm.com/docs/content/DO/DOinDSX.html)\n",
    "* For help with DOcplex, or to report a defect, go [here](https://developer.ibm.com/answers/smartspace/docloud).\n",
    "* Contact us at dofeedback@wwpdl.vnet.ibm.com"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "<div class=\"alert alert-block alert-info\"> Note: To save resources and get the best performance please use the code below to stop the kernel before exiting your notebook.</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "%%javascript\n",
    "Jupyter.notebook.session.delete();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Copyright © 2017 IBM. IPLA licensed Sample Materials."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
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  "language_info": {
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    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
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 },
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}