{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# State Task Network (Kondili and Sargent, 1993)\n",
    "\n",
    "A state-task network is a graphical representation of the activities in a multiproduct batch process. The representation includes the details needed for the short term scheduling of batch operations. A typical and well-studied example due to Kondili (1993) is shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SRI4ZlN5sSS4UaByFyY+DXw2WFnInUTKmpoQwdf5fEUyB1NRvFYofqTrIJyQR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\nWWTQfLEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image('../images/Kondili_1993.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each circular node in the diagram designates material in a particular state.  The materials are generally held in suitable vessels with a known capacity. The relevant information for each state is the initial inventory, storage capacity, and the unit price of the material in each state. The price of materials in intermediate states may be assigned penalities in order to minimize the amount of work in progress.\n",
    "\n",
    "The rectangular nodes denote process tasks. When scheduled for execution, each task is assigned an appropriate piece of equipment, and assigned a batch of material according to the incoming arcs. Each incoming arc begins at a state where the associated label indicates the mass fraction of the batch coming from that particular state. Outgoing arcs indicate the disposition of the batch to product states. The outgoing are labels indicate the fraction of the batch assigned to each product state, and the time necessary to produce that product. \n",
    "\n",
    "Not shown in the diagram is the process equipment used to execute the tasks. A separate list of process units is available, each characterized by a capacity and list of tasks which can be performed in that unit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# ==========================================================\n",
      "# = Solver Results                                         =\n",
      "# ==========================================================\n",
      "# ----------------------------------------------------------\n",
      "#   Problem Information\n",
      "# ----------------------------------------------------------\n",
      "Problem: \n",
      "- Name: unknown\n",
      "  Lower bound: 2744.375\n",
      "  Upper bound: 2744.375\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 410\n",
      "  Number of variables: 322\n",
      "  Number of nonzeros: 1158\n",
      "  Sense: maximize\n",
      "# ----------------------------------------------------------\n",
      "#   Solver Information\n",
      "# ----------------------------------------------------------\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Termination condition: optimal\n",
      "  Statistics: \n",
      "    Branch and bound: \n",
      "      Number of bounded subproblems: 597\n",
      "      Number of created subproblems: 597\n",
      "  Error rc: 0\n",
      "  Time: 0.19040894508361816\n",
      "# ----------------------------------------------------------\n",
      "#   Solution Information\n",
      "# ----------------------------------------------------------\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import sys\n",
    "sys.path.append('../STN')\n",
    "from STN import STN\n",
    "\n",
    "# create instance\n",
    "stn = STN()\n",
    "\n",
    "# states\n",
    "stn.state('FeedA',     init = 200)\n",
    "stn.state('FeedB',     init = 200)\n",
    "stn.state('FeedC',     init = 200)\n",
    "stn.state('HotA',      price = -1)\n",
    "stn.state('IntAB',     price = -1)\n",
    "stn.state('IntBC',     price = -1)\n",
    "stn.state('ImpureE',   price = -1)\n",
    "stn.state('Product_1', price = 10)\n",
    "stn.state('Product_2', price = 10)\n",
    "\n",
    "# state to task arcs\n",
    "stn.stArc('FeedA',   'Heating')\n",
    "stn.stArc('FeedB',   'Reaction_1', rho = 0.5)\n",
    "stn.stArc('FeedC',   'Reaction_1', rho = 0.5)\n",
    "stn.stArc('FeedC',   'Reaction_3', rho = 0.2)\n",
    "stn.stArc('HotA',    'Reaction_2', rho = 0.4)\n",
    "stn.stArc('IntAB',   'Reaction_3', rho = 0.8)\n",
    "stn.stArc('IntBC',   'Reaction_2', rho = 0.6)\n",
    "stn.stArc('ImpureE', 'Separation')\n",
    "\n",
    "# task to state arcs\n",
    "stn.tsArc('Heating',    'HotA',      rho = 1.0, dur = 1)\n",
    "stn.tsArc('Reaction_2', 'IntAB',     rho = 0.6, dur = 2)\n",
    "stn.tsArc('Reaction_2', 'Product_1', rho = 0.4, dur = 2)\n",
    "stn.tsArc('Reaction_1', 'IntBC',     dur = 2)\n",
    "stn.tsArc('Reaction_3', 'ImpureE',   dur = 1)\n",
    "stn.tsArc('Separation', 'IntAB',     rho = 0.1, dur = 2)\n",
    "stn.tsArc('Separation', 'Product_2', rho = 0.9, dur = 1)\n",
    "\n",
    "# unit-task data\n",
    "stn.unit('Heater',    'Heating',    Bmin = 0, Bmax = 100)\n",
    "stn.unit('Reactor_1', 'Reaction_1', Bmin = 0, Bmax =  80)\n",
    "stn.unit('Reactor_1', 'Reaction_2', Bmin = 0, Bmax =  80)\n",
    "stn.unit('Reactor_1', 'Reaction_3', Bmin = 0, Bmax =  80)\n",
    "stn.unit('Reactor_2', 'Reaction_1', Bmin = 0, Bmax =  50)\n",
    "stn.unit('Reactor_2', 'Reaction_2', Bmin = 0, Bmax =  50)\n",
    "stn.unit('Reactor_2', 'Reaction_3', Bmin = 0, Bmax =  50)\n",
    "stn.unit('Still',     'Separation', Bmin = 0, Bmax = 200)\n",
    "\n",
    "H = 10\n",
    "stn.build(range(0,H+1))\n",
    "stn.solve('glpk')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5ndq1R6nScWHunG7Dl78MH/oQXHNNetRmsyFD4G9/S0+l2WGHNAnzL39ZN/+iRWnf7beH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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1069e96a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "stn.gantt()\n",
    "plt.savefig('../images/Kondili_gantt.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Starting Conditions\n",
      "\n",
      "    Initial State Inventories are:\n",
      "        FeedB        200.0 kg\n",
      "        IntAB          0.0 kg\n",
      "        FeedA        200.0 kg\n",
      "        HotA           0.0 kg\n",
      "        FeedC        200.0 kg\n",
      "        ImpureE        0.0 kg\n",
      "        Product_2      0.0 kg\n",
      "        IntBC          0.0 kg\n",
      "        Product_1      0.0 kg\n",
      "\n",
      "Time = 0 hr\n",
      "\n",
      "    1. Assign Heater to Heating for 84.00 kg batch for 1.0 hours\n",
      "    2. Transfer 84.00 from FeedA to Heater\n",
      "    3. Assign Reactor_1 to Reaction_1 for 76.00 kg batch for 2.0 hours\n",
      "    4. Transfer 38.00 from FeedC to Reactor_1\n",
      "    5. Transfer 38.00 from FeedB to Reactor_1\n",
      "    6. Assign Reactor_2 to Reaction_1 for 50.00 kg batch for 2.0 hours\n",
      "    7. Transfer 25.00 from FeedC to Reactor_2\n",
      "    8. Transfer 25.00 from FeedB to Reactor_2\n",
      "\n",
      "    State Inventories are now:\n",
      "        FeedB        137.0 kg\n",
      "        IntAB          0.0 kg\n",
      "        FeedA        116.0 kg\n",
      "        HotA           0.0 kg\n",
      "        FeedC        137.0 kg\n",
      "        ImpureE        0.0 kg\n",
      "        Product_2      0.0 kg\n",
      "        IntBC          0.0 kg\n",
      "        Product_1      0.0 kg\n",
      "\n",
      "    Unit Assignments are now:\n",
      "        Heater on Heating with 84.00 kg batch for hour 1 of 1\n",
      "        Reactor_1 on Reaction_1 with 76.00 kg batch for hour 1 of 2\n",
      "        Still is unassigned\n",
      "        Reactor_2 on Reaction_1 with 50.00 kg batch for hour 1 of 2\n",
      "\n",
      "Time = 1 hr\n",
      "\n",
      "    1. Transfer 84.00 kg from Heater to HotA\n",
      "    2. Release Heater from Heating\n",
      "\n",
      "    State Inventories are now:\n",
      "        FeedB        137.0 kg\n",
      "        IntAB          0.0 kg\n",
      "        FeedA        116.0 kg\n",
      "        HotA          84.0 kg\n",
      "        FeedC        137.0 kg\n",
      "        ImpureE        0.0 kg\n",
      "        Product_2      0.0 kg\n",
      "        IntBC          0.0 kg\n",
      "        Product_1      0.0 kg\n",
      "\n",
      "    Unit Assignments are now:\n",
      "        Heater is unassigned\n",
      "        Reactor_1 on Reaction_1 with 76.00 kg batch for hour 2 of 2\n",
      "        Still is unassigned\n",
      "        Reactor_2 on Reaction_1 with 50.00 kg batch for hour 2 of 2\n",
      "\n",
      "Time = 2 hr\n",
      "\n",
      "    1. Transfer 76.00 kg from Reactor_1 to IntBC\n",
      "    2. Transfer 50.00 kg from Reactor_2 to IntBC\n",
      "    3. Release Reactor_1 from Reaction_1\n",
      "    4. Assign Reactor_1 to Reaction_2 for 80.00 kg batch for 2.0 hours\n",
      "    5. Transfer 48.00 from IntBC to Reactor_1\n",
      "    6. Transfer 32.00 from HotA to Reactor_1\n",
      "    7. Release Reactor_2 from Reaction_1\n",
      "    8. Assign Reactor_2 to Reaction_2 for 50.00 kg batch for 2.0 hours\n",
      "    9. Transfer 30.00 from IntBC to Reactor_2\n",
      "   10. Transfer 20.00 from HotA to Reactor_2\n",
      "\n",
      "    State Inventories are now:\n",
      "        FeedB        137.0 kg\n",
      "        IntAB          0.0 kg\n",
      "        FeedA        116.0 kg\n",
      "        HotA          32.0 kg\n",
      "        FeedC        137.0 kg\n",
      "        ImpureE        0.0 kg\n",
      "        Product_2      0.0 kg\n",
      "        IntBC         48.0 kg\n",
      "        Product_1      0.0 kg\n",
      "\n",
      "    Unit Assignments are now:\n",
      "        Heater is unassigned\n",
      "        Reactor_1 on Reaction_2 with 80.00 kg batch for hour 1 of 2\n",
      "        Still is unassigned\n",
      "        Reactor_2 on Reaction_2 with 50.00 kg batch for hour 1 of 2\n",
      "\n",
      "Time = 3 hr\n",
      "\n",
      "    State Inventories are now:\n",
      "        FeedB        137.0 kg\n",
      "        IntAB          0.0 kg\n",
      "        FeedA        116.0 kg\n",
      "        HotA          32.0 kg\n",
      "        FeedC        137.0 kg\n",
      "        ImpureE        0.0 kg\n",
      "        Product_2      0.0 kg\n",
      "        IntBC         48.0 kg\n",
      "        Product_1      0.0 kg\n",
      "\n",
      "    Unit Assignments are now:\n",
      "        Heater is unassigned\n",
      "        Reactor_1 on Reaction_2 with 80.00 kg batch for hour 2 of 2\n",
      "        Still is unassigned\n",
      "        Reactor_2 on Reaction_2 with 50.00 kg batch for hour 2 of 2\n",
      "\n",
      "Time = 4 hr\n",
      "\n",
      "    1. Transfer 48.00 kg from Reactor_1 to IntAB\n",
      "    2. Transfer 32.00 kg from Reactor_1 to Product_1\n",
      "    3. Transfer 30.00 kg from Reactor_2 to IntAB\n",
      "    4. Transfer 20.00 kg from Reactor_2 to Product_1\n",
      "    5. Assign Heater to Heating for 52.00 kg batch for 1.0 hours\n",
      "    6. Transfer 52.00 from FeedA to Heater\n",
      "    7. Release Reactor_1 from Reaction_2\n",
      "    8. Assign Reactor_1 to Reaction_2 for 80.00 kg batch for 2.0 hours\n",
      "    9. Transfer 48.00 from IntBC to Reactor_1\n",
      "   10. Transfer 32.00 from HotA to Reactor_1\n",
      "   11. Release Reactor_2 from Reaction_2\n",
      "   12. Assign Reactor_2 to Reaction_3 for 50.00 kg batch for 1.0 hours\n",
      "   13. Transfer 10.00 from FeedC to Reactor_2\n",
      "   14. Transfer 40.00 from IntAB to Reactor_2\n",
      "\n",
      "    State Inventories are now:\n",
      "        FeedB        137.0 kg\n",
      "        IntAB         38.0 kg\n",
      "        FeedA         64.0 kg\n",
      "        HotA           0.0 kg\n",
      "        FeedC        127.0 kg\n",
      "        ImpureE        0.0 kg\n",
      "        Product_2      0.0 kg\n",
      "        IntBC          0.0 kg\n",
      "        Product_1     52.0 kg\n",
      "\n",
      "    Unit Assignments are now:\n",
      "        Heater on Heating with 52.00 kg batch for hour 1 of 1\n",
      "        Reactor_1 on Reaction_2 with 80.00 kg batch for hour 1 of 2\n",
      "        Still is unassigned\n",
      "        Reactor_2 on Reaction_3 with 50.00 kg batch for hour 1 of 1\n",
      "\n",
      "Time = 5 hr\n",
      "\n",
      "    1. Transfer 52.00 kg from Heater to HotA\n",
      "    2. Transfer 50.00 kg from Reactor_2 to ImpureE\n",
      "    3. Release Heater from Heating\n",
      "    4. Assign Still to Separation for 50.00 kg batch for 2.0 hours\n",
      "    5. Transfer 50.00 from ImpureE to Still\n",
      "    6. Release Reactor_2 from Reaction_3\n",
      "    7. Assign Reactor_2 to Reaction_3 for 47.50 kg batch for 1.0 hours\n",
      "    8. Transfer 9.50 from FeedC to Reactor_2\n",
      "    9. Transfer 38.00 from IntAB to Reactor_2\n",
      "\n",
      "    State Inventories are now:\n",
      "        FeedB        137.0 kg\n",
      "        IntAB          0.0 kg\n",
      "        FeedA         64.0 kg\n",
      "        HotA          52.0 kg\n",
      "        FeedC        117.5 kg\n",
      "        ImpureE        0.0 kg\n",
      "        Product_2      0.0 kg\n",
      "        IntBC          0.0 kg\n",
      "        Product_1     52.0 kg\n",
      "\n",
      "    Unit Assignments are now:\n",
      "        Heater is unassigned\n",
      "        Reactor_1 on Reaction_2 with 80.00 kg batch for hour 2 of 2\n",
      "        Still on Separation with 50.00 kg batch for hour 1 of 2\n",
      "        Reactor_2 on Reaction_3 with 47.50 kg batch for hour 1 of 1\n",
      "\n",
      "Time = 6 hr\n",
      "\n",
      "    1. Transfer 48.00 kg from Reactor_1 to IntAB\n",
      "    2. Transfer 32.00 kg from Reactor_1 to Product_1\n",
      "    3. Transfer 45.00 kg from Still to Product_2\n",
      "    4. Transfer 47.50 kg from Reactor_2 to ImpureE\n",
      "    5. Release Reactor_1 from Reaction_2\n",
      "    6. Assign Reactor_1 to Reaction_1 for 78.00 kg batch for 2.0 hours\n",
      "    7. Transfer 39.00 from FeedC to Reactor_1\n",
      "    8. Transfer 39.00 from FeedB to Reactor_1\n",
      "    9. Release Reactor_2 from Reaction_3\n",
      "   10. Assign Reactor_2 to Reaction_3 for 16.25 kg batch for 1.0 hours\n",
      "   11. Transfer 3.25 from FeedC to Reactor_2\n",
      "   12. Transfer 13.00 from IntAB to Reactor_2\n",
      "\n",
      "    State Inventories are now:\n",
      "        FeedB         98.0 kg\n",
      "        IntAB         35.0 kg\n",
      "        FeedA         64.0 kg\n",
      "        HotA          52.0 kg\n",
      "        FeedC         75.2 kg\n",
      "        ImpureE       47.5 kg\n",
      "        Product_2     45.0 kg\n",
      "        IntBC          0.0 kg\n",
      "        Product_1     84.0 kg\n",
      "\n",
      "    Unit Assignments are now:\n",
      "        Heater is unassigned\n",
      "        Reactor_1 on Reaction_1 with 78.00 kg batch for hour 1 of 2\n",
      "        Still on Separation with 5.00 kg batch for hour 2 of 2\n",
      "        Reactor_2 on Reaction_3 with 16.25 kg batch for hour 1 of 1\n",
      "\n",
      "Time = 7 hr\n",
      "\n",
      "    1. Transfer 5.00 kg from Still to IntAB\n",
      "    2. Transfer 16.25 kg from Reactor_2 to ImpureE\n",
      "    3. Release Still from Separation\n",
      "    4. Release Reactor_2 from Reaction_3\n",
      "    5. Assign Reactor_2 to Reaction_3 for 50.00 kg batch for 1.0 hours\n",
      "    6. Transfer 10.00 from FeedC to Reactor_2\n",
      "    7. Transfer 40.00 from IntAB to Reactor_2\n",
      "\n",
      "    State Inventories are now:\n",
      "        FeedB         98.0 kg\n",
      "        IntAB          0.0 kg\n",
      "        FeedA         64.0 kg\n",
      "        HotA          52.0 kg\n",
      "        FeedC         65.2 kg\n",
      "        ImpureE       63.8 kg\n",
      "        Product_2     45.0 kg\n",
      "        IntBC          0.0 kg\n",
      "        Product_1     84.0 kg\n",
      "\n",
      "    Unit Assignments are now:\n",
      "        Heater is unassigned\n",
      "        Reactor_1 on Reaction_1 with 78.00 kg batch for hour 2 of 2\n",
      "        Still is unassigned\n",
      "        Reactor_2 on Reaction_3 with 50.00 kg batch for hour 1 of 1\n",
      "\n",
      "Time = 8 hr\n",
      "\n",
      "    1. Transfer 78.00 kg from Reactor_1 to IntBC\n",
      "    2. Transfer 50.00 kg from Reactor_2 to ImpureE\n",
      "    3. Release Reactor_1 from Reaction_1\n",
      "    4. Assign Reactor_1 to Reaction_2 for 80.00 kg batch for 2.0 hours\n",
      "    5. Transfer 48.00 from IntBC to Reactor_1\n",
      "    6. Transfer 32.00 from HotA to Reactor_1\n",
      "    7. Assign Still to Separation for 113.75 kg batch for 2.0 hours\n",
      "    8. Transfer 113.75 from ImpureE to Still\n",
      "    9. Release Reactor_2 from Reaction_3\n",
      "   10. Assign Reactor_2 to Reaction_2 for 50.00 kg batch for 2.0 hours\n",
      "   11. Transfer 30.00 from IntBC to Reactor_2\n",
      "   12. Transfer 20.00 from HotA to Reactor_2\n",
      "\n",
      "    State Inventories are now:\n",
      "        FeedB         98.0 kg\n",
      "        IntAB          0.0 kg\n",
      "        FeedA         64.0 kg\n",
      "        HotA           0.0 kg\n",
      "        FeedC         65.2 kg\n",
      "        ImpureE        0.0 kg\n",
      "        Product_2     45.0 kg\n",
      "        IntBC          0.0 kg\n",
      "        Product_1     84.0 kg\n",
      "\n",
      "    Unit Assignments are now:\n",
      "        Heater is unassigned\n",
      "        Reactor_1 on Reaction_2 with 80.00 kg batch for hour 1 of 2\n",
      "        Still on Separation with 113.75 kg batch for hour 1 of 2\n",
      "        Reactor_2 on Reaction_2 with 50.00 kg batch for hour 1 of 2\n",
      "\n",
      "Time = 9 hr\n",
      "\n",
      "    1. Transfer 102.38 kg from Still to Product_2\n",
      "\n",
      "    State Inventories are now:\n",
      "        FeedB         98.0 kg\n",
      "        IntAB          0.0 kg\n",
      "        FeedA         64.0 kg\n",
      "        HotA           0.0 kg\n",
      "        FeedC         65.2 kg\n",
      "        ImpureE        0.0 kg\n",
      "        Product_2    147.4 kg\n",
      "        IntBC          0.0 kg\n",
      "        Product_1     84.0 kg\n",
      "\n",
      "    Unit Assignments are now:\n",
      "        Heater is unassigned\n",
      "        Reactor_1 on Reaction_2 with 80.00 kg batch for hour 2 of 2\n",
      "        Still on Separation with 11.38 kg batch for hour 2 of 2\n",
      "        Reactor_2 on Reaction_2 with 50.00 kg batch for hour 2 of 2\n",
      "\n",
      "Time = 10 hr\n",
      "\n",
      "    1. Transfer 48.00 kg from Reactor_1 to IntAB\n",
      "    2. Transfer 32.00 kg from Reactor_1 to Product_1\n",
      "    3. Transfer 11.38 kg from Still to IntAB\n",
      "    4. Transfer 30.00 kg from Reactor_2 to IntAB\n",
      "    5. Transfer 20.00 kg from Reactor_2 to Product_1\n",
      "    6. Release Reactor_1 from Reaction_2\n",
      "    7. Release Still from Separation\n",
      "    8. Release Reactor_2 from Reaction_2\n",
      "\n",
      "    State Inventories are now:\n",
      "        FeedB         98.0 kg\n",
      "        IntAB         89.4 kg\n",
      "        FeedA         64.0 kg\n",
      "        HotA           0.0 kg\n",
      "        FeedC         65.2 kg\n",
      "        ImpureE        0.0 kg\n",
      "        Product_2    147.4 kg\n",
      "        IntBC          0.0 kg\n",
      "        Product_1    136.0 kg\n",
      "\n",
      "    Unit Assignments are now:\n",
      "        Heater is unassigned\n",
      "        Reactor_1 is unassigned\n",
      "        Still is unassigned\n",
      "        Reactor_2 is unassigned\n"
     ]
    }
   ],
   "source": [
    "stn.trace()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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