{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "<!--NOTEBOOK_HEADER-->\n",
    "*This notebook contains course material from [CBE40455](https://jckantor.github.io/CBE40455) by\n",
    "Jeffrey Kantor (jeff at nd.edu); the content is available [on Github](https://github.com/jckantor/CBE40455.git).\n",
    "The text is released under the [CC-BY-NC-ND-4.0 license](https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode),\n",
    "and code is released under the [MIT license](https://opensource.org/licenses/MIT).*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "<!--NAVIGATION-->\n",
    "< [Extracting Information from Discrete-Event Simulations](http://nbviewer.jupyter.org/github/jckantor/CBE40455/blob/master/notebooks/02.05-Extracting_Information_from_Discrete-Event_Simulations.ipynb) | [Contents](toc.ipynb) | [Warehouse Fulfullment Operations](http://nbviewer.jupyter.org/github/jckantor/CBE40455/blob/master/notebooks/02.07-Warehouse-Fulfillment-Operations.ipynb) ><p><a href=\"https://colab.research.google.com/github/jckantor/CBE40455/blob/master/notebooks/02.06-Spankys-Pizzeria.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open in Google Colaboratory\"></a><p><a href=\"https://raw.githubusercontent.com/jckantor/CBE40455/master/notebooks/02.06-Spankys-Pizzeria.ipynb\"><img align=\"left\" src=\"https://img.shields.io/badge/Github-Download-blue.svg\" alt=\"Download\" title=\"Download Notebook\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "metadata": false,
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Spankys Pizzeria\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "pycharm": {}
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_context('talk')\n",
    "\n",
    "import simpy\n",
    "import itertools\n",
    "import random\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "## Menu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "pycharm": {}
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nuggets</th>\n",
       "      <th>pizza</th>\n",
       "      <th>rolls</th>\n",
       "      <th>sticks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cook time</th>\n",
       "      <td>10.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prep time</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>probability</th>\n",
       "      <td>0.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             nuggets  pizza  rolls  sticks\n",
       "cook time       10.0   13.0    8.0    10.0\n",
       "prep time        1.0    0.0    0.0     1.0\n",
       "probability      0.2    0.4    0.1     0.3"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "menu = {\n",
    "    'pizza': {'probability': 0.4, 'prep time': 0, 'cook time': 13},\n",
    "    'sticks': {'probability': 0.3, 'prep time': 1, 'cook time': 10},\n",
    "    'nuggets': {'probability': 0.2, 'prep time': 1, 'cook time': 10},\n",
    "    'rolls': {'probability': 0.1, 'prep time': 0, 'cook time': 8}\n",
    "}\n",
    "menu = pd.DataFrame(menu)\n",
    "\n",
    "items = menu.columns\n",
    "probs = menu.ix['probability',:]\n",
    "\n",
    "menu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "## Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "pycharm": {}
   },
   "outputs": [],
   "source": [
    "class Spanky(object):\n",
    "\n",
    "    def __init__(self,N_SERVERS=1,N_OVENS=2,T_ORDER=10):\n",
    "        self.env = simpy.Environment()\n",
    "        self.server = simpy.Resource(self.env,N_SERVERS)   # server resources\n",
    "        self.oven = simpy.Resource(self.env,N_OVENS)       # oven resources\n",
    "        self.T_ORDER = T_ORDER\n",
    "        self.data = dict()\n",
    "        self.orderQueue = pd.Series([0],[0])               # track orders in progress\n",
    "        self.env.process(self.orderGenerator())            # where to start the model\n",
    "        \n",
    "    def orderGenerator(self):\n",
    "        for orderId in itertools.count():\n",
    "            yield self.env.timeout(random.expovariate(1.0/self.T_ORDER))\n",
    "            item = np.random.choice(items,p = probs)\n",
    "            self.writeLog(orderId,item,'0 arrival')\n",
    "            self.env.process(self.order(orderId,item))\n",
    "\n",
    "    def order(self,orderId,item):\n",
    "        # take order from customer\n",
    "        with self.server.request() as serverreq:\n",
    "            yield serverreq\n",
    "            self.writeLog(orderId,item,'1 order')\n",
    "            self.writeOrderQueue(+1)\n",
    "            yield self.env.timeout(0.5)\n",
    "            # give receipt to customer\n",
    "            self.writeLog(orderId,item,'2 receipt')\n",
    "            # quote finish time to customer\n",
    "            tquote = self.env.now + menu[item]['prep time'] + menu[item]['cook time'] + 0.5\n",
    "            self.writeLog(orderId,item,'6 tquote',tquote)\n",
    "            \n",
    "        # request server to prep order\n",
    "        with self.server.request() as serverreq:\n",
    "            yield serverreq\n",
    "            self.writeLog(orderId,item,'3 prep')\n",
    "            yield self.env.timeout(menu[item]['prep time'])\n",
    "        \n",
    "        # request oven to cook order\n",
    "        with self.oven.request() as ovenreq:\n",
    "            yield ovenreq\n",
    "            # request server to load oven\n",
    "            with self.server.request() as serverreq:\n",
    "                yield serverreq\n",
    "                yield self.env.timeout(0.25)\n",
    "                self.writeLog(orderId,item,'4 oven') \n",
    "            # cook order\n",
    "            yield self.env.timeout(menu[item]['cook time'])\n",
    "            # request server to unload oven\n",
    "            with self.server.request() as serverreq:\n",
    "                yield serverreq\n",
    "                yield self.env.timeout(0.25)\n",
    "                self.writeLog(orderId,item,'5 finish')\n",
    "                self.writeOrderQueue(-1)\n",
    "\n",
    "    def writeLog(self,orderId,item,event,t=None):\n",
    "        if t==None:\n",
    "            t = self.env.now\n",
    "        self.data[orderId,item,event] = t\n",
    "        \n",
    "    @property\n",
    "    def log(self):\n",
    "        df = pd.DataFrame([[a,b,c,self.data[a,b,c]] for (a,b,c) in self.data.keys()])\n",
    "        df.columns = ['OrderID','Item','Event','Time']\n",
    "        return df.pivot_table(values='Time',index=['OrderID','Item'],columns=['Event'])\n",
    "    \n",
    "    def stats(self):\n",
    "        df = w.log\n",
    "        late = df['5 finish']-df['6 tquote']\n",
    "        late = late[late > 0]\n",
    "        \n",
    "        N = {}\n",
    "        N['orders'] = df[df['1 order']>0]['1 order'].count()\n",
    "        N['finished'] = df[df['5 finish']>0]['5 finish'].count()\n",
    "        N['late'] = late.count()\n",
    "        N['latefrac'] = N['late']/N['orders']\n",
    "        N['meanlate'] = late.mean()\n",
    "        N['maxlate'] = late.max()\n",
    "        \n",
    "        print(\" Orders Received = {0:6d}\".format(N['orders']))\n",
    "        print(\" Orders Finished = {0:6d}\".format(N['finished']))\n",
    "        print(\"     Late Orders = {0:6d}\".format(N['late']))\n",
    "        print(\"   Fraction late = {0:6.3f}\".format(N['latefrac']))\n",
    "        print(\"Average lateness = {0:6.3f}\".format(N['meanlate']))\n",
    "        print(\"Maximum lateness = {0:6.3f}\".format(N['maxlate']))\n",
    "        \n",
    "        plt.subplot(2,1,1)\n",
    "        late.hist()\n",
    "        plt.title('Distribution of Late Orders')\n",
    "        plt.ylabel('Number of orders')\n",
    "        plt.xlabel('Minutes late')\n",
    "        plt.subplot(2,1,2)\n",
    "        w.orderQueue.plot()\n",
    "        plt.xlabel('Time [min]')\n",
    "        plt.ylabel('Orders')\n",
    "        plt.title('Orders in Process')\n",
    "        plt.tight_layout()\n",
    "\n",
    "        return N\n",
    "\n",
    "    def writeOrderQueue(self,inc):                         # track orders in progress\n",
    "        self.orderQueue[self.env.now] = self.orderQueue.iloc[-1] + inc\n",
    "                    \n",
    "    def run(self,t_sim):\n",
    "        self.env.run(until=t_sim)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "## Simulation of Existing Operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "pycharm": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Orders Received =     17\n",
      " Orders Finished =     17\n",
      "     Late Orders =      8\n",
      "   Fraction late =  0.471\n",
      "Average lateness =  1.635\n",
      "Maximum lateness =  5.738\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'finished': 17,\n",
       " 'late': 8,\n",
       " 'latefrac': 0.47058823529411764,\n",
       " 'maxlate': 5.7380732432047523,\n",
       " 'meanlate': 1.635408416738107,\n",
       " 'orders': 17}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1ahXu3r2Lmzdvori4GC1bthT0TY9ERERERLWNoJANlI7LvnHjBm7evAkdHR3o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WkYFP7sK3IR3ivbTjPo6R/dK5tmGNxuZuUiqTL2xx6LtfM9b+d39FK98F7fWZ9FrMkIomVpXgzu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\nPESv1qgCymsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114f16780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create Spanky's with one server, two ovens, with orders arriving at an average of 10 minutes\n",
    "w = Spanky(1,2,10)\n",
    "\n",
    "# run a simulation for 180 minutes\n",
    "w.run(180)\n",
    "\n",
    "# summarize data\n",
    "w.stats()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "## Should Spanky's add additional ovens?"
   ]
  },
  {
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    "## Should Spanky's add additional servers?"
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    "<!--NAVIGATION-->\n",
    "< [Extracting Information from Discrete-Event Simulations](http://nbviewer.jupyter.org/github/jckantor/CBE40455/blob/master/notebooks/02.05-Extracting_Information_from_Discrete-Event_Simulations.ipynb) | [Contents](toc.ipynb) | [Warehouse Fulfullment Operations](http://nbviewer.jupyter.org/github/jckantor/CBE40455/blob/master/notebooks/02.07-Warehouse-Fulfillment-Operations.ipynb) ><p><a href=\"https://colab.research.google.com/github/jckantor/CBE40455/blob/master/notebooks/02.06-Spankys-Pizzeria.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open in Google Colaboratory\"></a><p><a href=\"https://raw.githubusercontent.com/jckantor/CBE40455/master/notebooks/02.06-Spankys-Pizzeria.ipynb\"><img align=\"left\" src=\"https://img.shields.io/badge/Github-Download-blue.svg\" alt=\"Download\" title=\"Download Notebook\"></a>"
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