A mixed-integer programming model for an integrated production planning problem with preventive maintenance in the pulp and paper industry.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "72e5f964",
   "metadata": {},
   "source": [
    "#  A mixed-integer programming model for an integrated production planning problem with preventive maintenance in the pulp and paper industry."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0546a95e",
   "metadata": {},
   "source": [
    "Production planning and scheduling in the pulp and paper industry can be very challenging. In most cases, practitioners address the production planning process manually, which is time-consuming and sub-optimal. This study deals with production planning encountered in a pulp mill company involving different wood species, parallel heterogeneous lines, inventory limits, sequence-independent setup times and preventive maintenance. To tackle the problem, an efficient mixed-integer formulation is proposed that optimizes when, where and how much to produce of different wood species and schedules preventive maintenance to minimize the total setup times. Several computational experiments are conducted to solve a case study in a pulp mill company in Chile. The results show the capability of the model to support the decision-making process in the pulp and paper industry, providing an efficient tool for practitioners to solve the problem in a reasonable amount of time."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e1085ec",
   "metadata": {},
   "source": [
    "#### Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4fe574ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "import gurobipy as gp\n",
    "from gurobipy import GRB\n",
    "import time\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea129299",
   "metadata": {},
   "source": [
    "#### Create the Gurobi Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cc324429",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Set parameter Username\n",
      "Academic license - for non-commercial use only - expires 2025-01-14\n"
     ]
    }
   ],
   "source": [
    "mdl = gp.Model(\"model\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2324d41",
   "metadata": {},
   "source": [
    "#### Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6f523926",
   "metadata": {},
   "outputs": [],
   "source": [
    "Lines = 3  # Number of Production Lines\n",
    "Species = 3  # Number of Species\n",
    "Days = 30  # Horizon (Days)\n",
    "Trimestral_day = [4, 11, 18, 24]  # Days Available for Quarterly Maintenance\n",
    "Maintenance_day = [0, 7, 14, 21, 28]  # Days Available for Monthly Maintenance\n",
    "\n",
    "T = 24  # Number of Hours in a Day\n",
    "Time_maintenance = 12  # Duration of the Maintenance (Hours)\n",
    "Time_trimestral = 24  # Duration of the Quarterly Maintenance (Hours)\n",
    "Dtri = 4  # Number of Consecutive Days for Quarterly Maintenance\n",
    "S = 4  # Extra Days to Index Quarterly Maintenance\n",
    "\n",
    "# Performance (m3/h)\n",
    "Re = [[150, 90, 90], [150, 90, 90], [150, 80, 80]]  # Performance per Line and Species\n",
    "\n",
    "# Demand\n",
    "Demand = [[6500, 700, 2300] for _ in range(Days)]  # Demand of Each Species per Day\n",
    "\n",
    "# External Supply\n",
    "External_supply = [\n",
    "    [2500, 0, 0] if day % 6 not in [5, 6] else [1000, 0, 0] for day in range(1, Days + 1)\n",
    "]  # External Supply of Each Species\n",
    "\n",
    "# Inventories\n",
    "Initial_inventory = [17000, 17000, 6000]  # Initial Inventory\n",
    "Minimum_inventory = [5700, 4600, 1000]  # Minimum Inventory\n",
    "Maximum_inventory = [25000, 20000, 7000]  # Maximum Inventory"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11d78976",
   "metadata": {},
   "source": [
    "Disclaimer: This data is fictitious and created solely for illustrative purposes. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0f59863",
   "metadata": {},
   "source": [
    "#### Variables "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fbf1fe30",
   "metadata": {},
   "outputs": [],
   "source": [
    "# x[i,k,j]: integer hours of production of species i on line k, day j\n",
    "x = mdl.addVars(range(Species),range(Lines),range(Days),vtype=GRB.INTEGER,name=\"x\")\n",
    "\n",
    "# s[k,j]: integer number of setups performed on line k on day j\n",
    "s = mdl.addVars(range(Lines),range(Days+1),vtype=GRB.INTEGER,name=\"s\")\n",
    "\n",
    "# z[i,k,j]: binary, 1 if species i is processed on line k on day j, else 0\n",
    "z = mdl.addVars(range(Species),range(Lines),range(Days+2),vtype=GRB.BINARY,name=\"z\")\n",
    "\n",
    "# M[k,j]: binary, 1 if line k undergoes (regular) maintenance on day j\n",
    "M = mdl.addVars(range(Lines), range(Days), vtype=GRB.BINARY, name=\"M\")\n",
    "\n",
    "# I[i,j]: continuous, inventory of species i on day j\n",
    "I = mdl.addVars(range(Species),range(Days+2),vtype=GRB.CONTINUOUS,name=\"I\")\n",
    "\n",
    "# Mtri[k,j]: binary, 1 if line k undergoes trimestral maintenance on day j\n",
    "Mtri = mdl.addVars(range(Lines),range(Days+S),vtype=GRB.BINARY,name=\"Mtri\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40e1c568",
   "metadata": {},
   "source": [
    "#### Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bc5a9d60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<gurobi.Constr *Awaiting Model Update*>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ---------------------  Objective Function ---------------------\n",
    "# Minimize the sum of setups over all lines and days\n",
    "mdl.setObjective(gp.quicksum(s[k, j] for k in range(Lines) for j in range(Days)),GRB.MINIMIZE)\n",
    "\n",
    "# ---------------------  Constraints ---------------------\n",
    "\n",
    "# 1) Demand constraints:\n",
    "for i in range(Species):\n",
    "    for j in range(Days):\n",
    "        mdl.addConstr(\n",
    "            gp.quicksum(x[i, k, j] * Re[i][k] for k in range(Lines))+ I[i, j]+ External_supply[j][i] == Demand[j][i] + I[i, j+1])\n",
    "\n",
    "# 2) Daily time limit constraints on each line:\n",
    "for k in range(Lines):\n",
    "    for j in range(Days):\n",
    "        mdl.addConstr(\n",
    "            gp.quicksum(x[i, k, j] for i in range(Species)) + s[k, j] + Time_maintenance * M[k, j] + Time_trimestral * Mtri[k, j]== T )\n",
    "\n",
    "# 3) Setup / sequencing constraints:\n",
    "for k in range(Lines):\n",
    "    for j in range(Days):\n",
    "        mdl.addConstr(\n",
    "            s[k, j] + M[k, j] >= gp.quicksum(z[i, k, j] for i in range(Species)) - 1)\n",
    "\n",
    "# 4) Sequencing constraints across consecutive days:\n",
    "for i in range(Species):\n",
    "    for k in range(Lines):\n",
    "        for j in range(Days-1):\n",
    "            mdl.addConstr(\n",
    "                s[k, j] + z[i, k, j] + M[k, j+1] + Mtri[k, j+1] >= z[i, k, j+1])\n",
    "\n",
    "# 5) Symmetry sequencing constraint:\n",
    "for i in range(Species):\n",
    "    for k in range(Lines):\n",
    "        for j in range(Days-1):\n",
    "            mdl.addConstr(\n",
    "                z[i, k, j] <= s[k, j] + z[i, k, j+1] + M[k, j+1] + Mtri[k, j+1])\n",
    "\n",
    "# 6) Maximum number of setups per line/day:\n",
    "for k in range(Lines):\n",
    "    for j in range(Days):\n",
    "        mdl.addConstr(s[k, j] <= Species - 1)\n",
    "\n",
    "# 7) If a species is processed, x[i,k,j] <= T * z[i,k,j]\n",
    "for i in range(Species):\n",
    "    for k in range(Lines):\n",
    "        for j in range(Days):\n",
    "            mdl.addConstr(x[i, k, j] <= T * z[i, k, j])\n",
    "\n",
    "# 8) Initial inventory constraints:\n",
    "for i in range(Species):\n",
    "    mdl.addConstr(I[i, 0] == Initial_inventory[i])\n",
    "\n",
    "# 9) Minimum inventory:\n",
    "for i in range(Species):\n",
    "    for j in range(Days+1):\n",
    "        mdl.addConstr(I[i, j] >= Minimum_inventory[i])\n",
    "\n",
    "# 10) Maximum inventory:\n",
    "for i in range(Species):\n",
    "    for j in range(Days):\n",
    "        mdl.addConstr(I[i, j] <= Maximum_inventory[i])\n",
    "\n",
    "# 11) Each machine (line) must have exactly ONE regular maintenance in the horizon:\n",
    "for k in range(Lines):\n",
    "    mdl.addConstr(gp.quicksum(M[k, j] for j in range(Days)) == 1)\n",
    "\n",
    "# 12) Each machine has maintenance on the specified day(s) in Maintenance_day:\n",
    "for k in range(Lines):\n",
    "    mdl.addConstr(gp.quicksum(M[k, j] for j in Maintenance_day) == 1)\n",
    "\n",
    "# 13) At most one maintenance (regular or trimestral) can happen per day across all lines:\n",
    "for j in range(Days):\n",
    "    mdl.addConstr(gp.quicksum(M[k, j] + Mtri[k, j] for k in range(Lines)) <= 1)\n",
    "\n",
    "# 14) Exactly ONE trimestral maintenance in the horizon, but it must start on a 'Trimestral_day' (e.g. Monday):\n",
    "mdl.addConstr(gp.quicksum(Mtri[k, j] for j in Trimestral_day for k in range(Lines)) == 1)\n",
    "\n",
    "# 15) If trimestral maintenance starts on day j, it lasts for Dtri consecutive days:\n",
    "for k in range(Lines):\n",
    "    for j in Trimestral_day:\n",
    "        for l in range(1, Dtri):\n",
    "            mdl.addConstr(Mtri[k, j] == Mtri[k, j + l])\n",
    "\n",
    "# 16) The total sum of Mtri across the horizon = Dtri\n",
    "mdl.addConstr(gp.quicksum(Mtri[k, j] for j in range(Days) for k in range(Lines)) == Dtri)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd24d2e8",
   "metadata": {},
   "source": [
    "#### Solve the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f7a5a119",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Set parameter TimeLimit to value 120\n",
      "Gurobi Optimizer version 11.0.0 build v11.0.0rc2 (win64 - Windows 11+.0 (26100.2))\n",
      "\n",
      "CPU model: AMD Ryzen 7 4800H with Radeon Graphics, instruction set [SSE2|AVX|AVX2]\n",
      "Thread count: 8 physical cores, 16 logical processors, using up to 16 threads\n",
      "\n",
      "Optimize a model with 1412 rows, 939 columns and 5325 nonzeros\n",
      "Model fingerprint: 0x464c0d45\n",
      "Variable types: 96 continuous, 843 integer (480 binary)\n",
      "Coefficient statistics:\n",
      "  Matrix range     [1e+00, 2e+02]\n",
      "  Objective range  [1e+00, 1e+00]\n",
      "  Bounds range     [1e+00, 1e+00]\n",
      "  RHS range        [1e+00, 3e+04]\n",
      "Presolve removed 343 rows and 197 columns\n",
      "Presolve time: 0.01s\n",
      "Presolved: 1069 rows, 742 columns, 3755 nonzeros\n",
      "Variable types: 0 continuous, 742 integer (297 binary)\n",
      "\n",
      "Root relaxation: objective 0.000000e+00, 787 iterations, 0.01 seconds (0.01 work units)\n",
      "\n",
      "    Nodes    |    Current Node    |     Objective Bounds      |     Work\n",
      " Expl Unexpl |  Obj  Depth IntInf | Incumbent    BestBd   Gap | It/Node Time\n",
      "\n",
      "     0     0    0.00000    0  328          -    0.00000      -     -    0s\n",
      "H    0     0                     104.0000000    0.00000   100%     -    0s\n",
      "H    0     0                      97.0000000    0.00000   100%     -    0s\n",
      "H    0     0                      96.0000000    0.00000   100%     -    0s\n",
      "H    0     0                      87.0000000    0.00000   100%     -    0s\n",
      "H    0     0                      79.0000000    0.00000   100%     -    0s\n",
      "     0     0    0.00000    0  339   79.00000    0.00000   100%     -    0s\n",
      "H    0     0                      57.0000000    0.00000   100%     -    0s\n",
      "H    0     0                      13.0000000    0.00000   100%     -    0s\n",
      "     0     0    0.00000    0  340   13.00000    0.00000   100%     -    0s\n",
      "H    0     0                       7.0000000    0.00000   100%     -    0s\n",
      "     0     0    0.00000    0  180    7.00000    0.00000   100%     -    0s\n",
      "     0     0    0.00000    0  245    7.00000    0.00000   100%     -    0s\n",
      "H    0     0                       4.0000000    0.00000   100%     -    0s\n",
      "     0     0    0.00000    0  168    4.00000    0.00000   100%     -    0s\n",
      "     0     0    0.00000    0  132    4.00000    0.00000   100%     -    0s\n",
      "     0     0    0.00000    0  100    4.00000    0.00000   100%     -    0s\n",
      "     0     0    0.00000    0   97    4.00000    0.00000   100%     -    0s\n",
      "     0     0    0.00000    0   99    4.00000    0.00000   100%     -    0s\n",
      "     0     0    0.00000    0  108    4.00000    0.00000   100%     -    0s\n",
      "     0     0    0.00000    0  113    4.00000    0.00000   100%     -    0s\n",
      "     0     0    0.00000    0  110    4.00000    0.00000   100%     -    0s\n",
      "     0     2    0.00000    0   68    4.00000    0.00000   100%     -    0s\n",
      "H  468   402                       3.0000000    0.00000   100%   128    1s\n",
      "  2231  1307    1.00000   10  166    3.00000    0.00000   100%   104    5s\n",
      "H 3090  1547                       2.0000000    0.00000   100%   123    7s\n",
      "  5209   828     cutoff   26         2.00000    0.01133  99.4%   121   10s\n",
      "\n",
      "Cutting planes:\n",
      "  Gomory: 1\n",
      "  MIR: 16\n",
      "  StrongCG: 3\n",
      "  Flow cover: 27\n",
      "  Inf proof: 1\n",
      "  Zero half: 6\n",
      "  RLT: 48\n",
      "\n",
      "Explored 6499 nodes (758020 simplex iterations) in 10.42 seconds (12.14 work units)\n",
      "Thread count was 16 (of 16 available processors)\n",
      "\n",
      "Solution count 10: 2 3 4 ... 97\n",
      "\n",
      "Optimal solution found (tolerance 1.00e-04)\n",
      "Best objective 2.000000000000e+00, best bound 2.000000000000e+00, gap 0.0000%\n"
     ]
    }
   ],
   "source": [
    "Time_limit= 120 \n",
    "\n",
    "# Set the time limit (seconds)\n",
    "mdl.setParam(GRB.Param.TimeLimit, Time_limit)\n",
    "\n",
    "# Optimize\n",
    "mdl.optimize()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a12b1c97",
   "metadata": {},
   "source": [
    "#### Display Solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3c155697",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal solution found (status = 2)\n",
      "Objective value (Sum of Setups) = 2.0\n",
      "\n",
      "Non-zero Decision Variables:\n",
      "x[0,0,0] = 24.0\n",
      "x[0,0,1] = 24.0\n",
      "x[0,0,2] = 24.0\n",
      "x[0,0,3] = 24.0\n",
      "x[0,0,8] = 24.0\n",
      "x[0,0,9] = 24.0\n",
      "x[0,0,10] = 24.0\n",
      "x[0,0,11] = 24.0\n",
      "x[0,0,12] = 24.0\n",
      "x[0,0,13] = 24.0\n",
      "x[0,0,14] = 24.0\n",
      "x[0,0,15] = 24.0\n",
      "x[0,0,16] = 24.0\n",
      "x[0,0,17] = 24.0\n",
      "x[0,0,18] = 24.0\n",
      "x[0,0,19] = 24.0\n",
      "x[0,0,20] = 24.0\n",
      "x[0,0,21] = 24.0\n",
      "x[0,0,22] = 24.0\n",
      "x[0,0,23] = 24.0\n",
      "x[0,0,24] = 24.0\n",
      "x[0,0,25] = 24.0\n",
      "x[0,0,26] = 24.0\n",
      "x[0,0,27] = 24.0\n",
      "x[0,1,21] = 12.0\n",
      "x[0,1,22] = 24.0\n",
      "x[0,1,23] = 24.0\n",
      "x[0,1,24] = 24.0\n",
      "x[0,1,25] = 24.0\n",
      "x[0,1,26] = 24.0\n",
      "x[0,1,27] = 24.0\n",
      "x[0,1,28] = 24.0\n",
      "x[0,1,29] = 24.0\n",
      "x[0,2,0] = 12.0\n",
      "x[0,2,1] = 24.0\n",
      "x[0,2,2] = 24.0\n",
      "x[0,2,3] = 24.0\n",
      "x[0,2,4] = 24.0\n",
      "x[0,2,5] = 24.0\n",
      "x[0,2,6] = 24.0\n",
      "x[0,2,7] = 24.0\n",
      "x[0,2,8] = 24.0\n",
      "x[0,2,9] = 24.0\n",
      "x[0,2,10] = 23.0\n",
      "x[1,1,14] = 24.0\n",
      "x[1,1,15] = 24.0\n",
      "x[1,1,16] = 24.0\n",
      "x[1,1,17] = 24.0\n",
      "x[1,1,18] = 24.0\n",
      "x[1,1,19] = 24.0\n",
      "x[1,1,20] = 24.0\n",
      "x[2,0,28] = 12.0\n",
      "x[2,0,29] = 24.0\n",
      "x[2,1,0] = 24.0\n",
      "x[2,1,1] = 24.0\n",
      "x[2,1,2] = 24.0\n",
      "x[2,1,3] = 24.0\n",
      "x[2,1,4] = 24.0\n",
      "x[2,1,5] = 24.0\n",
      "x[2,1,6] = 24.0\n",
      "x[2,1,7] = 24.0\n",
      "x[2,1,8] = 24.0\n",
      "x[2,1,9] = 24.0\n",
      "x[2,1,10] = 24.0\n",
      "x[2,1,11] = 24.0\n",
      "x[2,1,12] = 24.0\n",
      "x[2,1,13] = 23.0\n",
      "x[2,2,11] = 24.0\n",
      "x[2,2,12] = 24.0\n",
      "x[2,2,13] = 24.0\n",
      "x[2,2,14] = 24.0\n",
      "x[2,2,15] = 24.0\n",
      "x[2,2,16] = 24.0\n",
      "x[2,2,17] = 24.0\n",
      "x[2,2,18] = 24.0\n",
      "x[2,2,19] = 24.0\n",
      "x[2,2,20] = 24.0\n",
      "x[2,2,21] = 24.0\n",
      "x[2,2,22] = 24.0\n",
      "x[2,2,23] = 24.0\n",
      "x[2,2,24] = 24.0\n",
      "x[2,2,25] = 24.0\n",
      "x[2,2,26] = 24.0\n",
      "x[2,2,27] = 24.0\n",
      "x[2,2,28] = 24.0\n",
      "x[2,2,29] = 24.0\n",
      "s[1,13] = 1.0\n",
      "s[2,10] = 1.0\n",
      "z[0,0,0] = 1.0\n",
      "z[0,0,1] = 1.0\n",
      "z[0,0,2] = 1.0\n",
      "z[0,0,3] = 1.0\n",
      "z[0,0,6] = 1.0\n",
      "z[0,0,7] = 1.0\n",
      "z[0,0,8] = 1.0\n",
      "z[0,0,9] = 1.0\n",
      "z[0,0,10] = 1.0\n",
      "z[0,0,11] = 1.0\n",
      "z[0,0,12] = 1.0\n",
      "z[0,0,13] = 1.0\n",
      "z[0,0,14] = 1.0\n",
      "z[0,0,15] = 1.0\n",
      "z[0,0,16] = 1.0\n",
      "z[0,0,17] = 1.0\n",
      "z[0,0,18] = 1.0\n",
      "z[0,0,19] = 1.0\n",
      "z[0,0,20] = 1.0\n",
      "z[0,0,21] = 1.0\n",
      "z[0,0,22] = 1.0\n",
      "z[0,0,23] = 1.0\n",
      "z[0,0,24] = 1.0\n",
      "z[0,0,25] = 1.0\n",
      "z[0,0,26] = 1.0\n",
      "z[0,0,27] = 1.0\n",
      "z[0,1,21] = 1.0\n",
      "z[0,1,22] = 1.0\n",
      "z[0,1,23] = 1.0\n",
      "z[0,1,24] = 1.0\n",
      "z[0,1,25] = 1.0\n",
      "z[0,1,26] = 1.0\n",
      "z[0,1,27] = 1.0\n",
      "z[0,1,28] = 1.0\n",
      "z[0,1,29] = 1.0\n",
      "z[0,2,0] = 1.0\n",
      "z[0,2,1] = 1.0\n",
      "z[0,2,2] = 1.0\n",
      "z[0,2,3] = 1.0\n",
      "z[0,2,4] = 1.0\n",
      "z[0,2,5] = 1.0\n",
      "z[0,2,6] = 1.0\n",
      "z[0,2,7] = 1.0\n",
      "z[0,2,8] = 1.0\n",
      "z[0,2,9] = 1.0\n",
      "z[0,2,10] = 1.0\n",
      "z[1,0,5] = 1.0\n",
      "z[1,1,14] = 1.0\n",
      "z[1,1,15] = 1.0\n",
      "z[1,1,16] = 1.0\n",
      "z[1,1,17] = 1.0\n",
      "z[1,1,18] = 1.0\n",
      "z[1,1,19] = 1.0\n",
      "z[1,1,20] = 1.0\n",
      "z[2,0,4] = 1.0\n",
      "z[2,0,28] = 1.0\n",
      "z[2,0,29] = 1.0\n",
      "z[2,1,0] = 1.0\n",
      "z[2,1,1] = 1.0\n",
      "z[2,1,2] = 1.0\n",
      "z[2,1,3] = 1.0\n",
      "z[2,1,4] = 1.0\n",
      "z[2,1,5] = 1.0\n",
      "z[2,1,6] = 1.0\n",
      "z[2,1,7] = 1.0\n",
      "z[2,1,8] = 1.0\n",
      "z[2,1,9] = 1.0\n",
      "z[2,1,10] = 1.0\n",
      "z[2,1,11] = 1.0\n",
      "z[2,1,12] = 1.0\n",
      "z[2,1,13] = 1.0\n",
      "z[2,2,11] = 1.0\n",
      "z[2,2,12] = 1.0\n",
      "z[2,2,13] = 1.0\n",
      "z[2,2,14] = 1.0\n",
      "z[2,2,15] = 1.0\n",
      "z[2,2,16] = 1.0\n",
      "z[2,2,17] = 1.0\n",
      "z[2,2,18] = 1.0\n",
      "z[2,2,19] = 1.0\n",
      "z[2,2,20] = 1.0\n",
      "z[2,2,21] = 1.0\n",
      "z[2,2,22] = 1.0\n",
      "z[2,2,23] = 1.0\n",
      "z[2,2,24] = 1.0\n",
      "z[2,2,25] = 1.0\n",
      "z[2,2,26] = 1.0\n",
      "z[2,2,27] = 1.0\n",
      "z[2,2,28] = 1.0\n",
      "z[2,2,29] = 1.0\n",
      "M[0,28] = 1.0\n",
      "M[1,21] = 1.0\n",
      "M[2,0] = 1.0\n",
      "I[0,0] = 17000.0\n",
      "I[0,1] = 17680.0\n",
      "I[0,2] = 19440.0\n",
      "I[0,3] = 21200.0\n",
      "I[0,4] = 22960.0\n",
      "I[0,5] = 19620.0\n",
      "I[0,6] = 17780.0\n",
      "I[0,7] = 15940.0\n",
      "I[0,8] = 14100.0\n",
      "I[0,9] = 15860.0\n",
      "I[0,10] = 17620.0\n",
      "I[0,11] = 17790.0\n",
      "I[0,12] = 17390.0\n",
      "I[0,13] = 16990.0\n",
      "I[0,14] = 16590.0\n",
      "I[0,15] = 16190.0\n",
      "I[0,16] = 15790.0\n",
      "I[0,17] = 13890.0\n",
      "I[0,18] = 13490.0\n",
      "I[0,19] = 13090.0\n",
      "I[0,20] = 12690.0\n",
      "I[0,21] = 12290.0\n",
      "I[0,22] = 12970.0\n",
      "I[0,23] = 13230.0\n",
      "I[0,24] = 14990.0\n",
      "I[0,25] = 16750.0\n",
      "I[0,26] = 18510.0\n",
      "I[0,27] = 20270.0\n",
      "I[0,28] = 22030.0\n",
      "I[0,29] = 18690.0\n",
      "I[0,30] = 16850.0\n",
      "I[1,0] = 17000.0\n",
      "I[1,1] = 16300.0\n",
      "I[1,2] = 15600.0\n",
      "I[1,3] = 14900.0\n",
      "I[1,4] = 14200.0\n",
      "I[1,5] = 13500.0\n",
      "I[1,6] = 12800.0\n",
      "I[1,7] = 12100.0\n",
      "I[1,8] = 11400.0\n",
      "I[1,9] = 10700.0\n",
      "I[1,10] = 10000.0\n",
      "I[1,11] = 9300.0\n",
      "I[1,12] = 8600.0\n",
      "I[1,13] = 7900.0\n",
      "I[1,14] = 7200.0\n",
      "I[1,15] = 8660.0\n",
      "I[1,16] = 10120.0\n",
      "I[1,17] = 11580.0\n",
      "I[1,18] = 13040.0\n",
      "I[1,19] = 14500.0\n",
      "I[1,20] = 15960.0\n",
      "I[1,21] = 17420.0\n",
      "I[1,22] = 16720.0\n",
      "I[1,23] = 16020.0\n",
      "I[1,24] = 15320.0\n",
      "I[1,25] = 14620.0\n",
      "I[1,26] = 13920.0\n",
      "I[1,27] = 13220.0\n",
      "I[1,28] = 12520.0\n",
      "I[1,29] = 11820.0\n",
      "I[1,30] = 11120.0\n",
      "I[2,0] = 6000.0\n",
      "I[2,1] = 5620.0\n",
      "I[2,2] = 5240.0\n",
      "I[2,3] = 4860.0\n",
      "I[2,4] = 4480.0\n",
      "I[2,5] = 4100.0\n",
      "I[2,6] = 3720.0\n",
      "I[2,7] = 3340.0\n",
      "I[2,8] = 2960.0\n",
      "I[2,9] = 2580.0\n",
      "I[2,10] = 2200.0\n",
      "I[2,11] = 1820.0\n",
      "I[2,12] = 3360.0\n",
      "I[2,13] = 4900.0\n",
      "I[2,14] = 6360.0\n",
      "I[2,15] = 5980.0\n",
      "I[2,16] = 5600.0\n",
      "I[2,17] = 5220.0\n",
      "I[2,18] = 4840.0\n",
      "I[2,19] = 4460.0\n",
      "I[2,20] = 4080.0\n",
      "I[2,21] = 3700.0\n",
      "I[2,22] = 3320.0\n",
      "I[2,23] = 2940.0\n",
      "I[2,24] = 2560.0\n",
      "I[2,25] = 2180.0\n",
      "I[2,26] = 1800.0\n",
      "I[2,27] = 1420.0\n",
      "I[2,28] = 1040.0\n",
      "I[2,29] = 2460.0\n",
      "I[2,30] = 5680.0\n",
      "Mtri[0,4] = 1.0\n",
      "Mtri[0,5] = 1.0\n",
      "Mtri[0,6] = 1.0\n",
      "Mtri[0,7] = 1.0\n"
     ]
    }
   ],
   "source": [
    "if mdl.SolCount > 0:\n",
    "    print(\"Optimal solution found (status = {})\".format(mdl.Status))\n",
    "    # Display objective value\n",
    "    print(f\"Objective value (Sum of Setups) = {mdl.ObjVal}\")\n",
    "    print(\"\\nNon-zero Decision Variables:\")\n",
    "    for var in mdl.getVars():\n",
    "        if abs(var.X) > 1e-6:\n",
    "            print(f\"{var.VarName} = {var.X}\")\n",
    "else:\n",
    "    print(\"No feasible solution found (status = {})\".format(mdl.Status))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4481aa6",
   "metadata": {},
   "source": [
    "#### Inventory Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3aa0f1e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Inventory Plots\n",
    "fig, axes = plt.subplots(1, Species, figsize=(18, 5), sharey=True)\n",
    "\n",
    "for specie in range(Species):\n",
    "    inventory_per_day = [I[specie, day].X for day in range(Days + 1)]\n",
    "    \n",
    "    axes[specie].plot(range(Days + 1), inventory_per_day, color=\"blue\")\n",
    "    \n",
    "    axes[specie].axhline(Minimum_inventory[specie], color='red', linestyle='--', label=\"Minimum Inventory\")\n",
    "    axes[specie].axhline(Maximum_inventory[specie], color='green', linestyle='--', label=\"Maximum Inventory\")\n",
    "    \n",
    "    axes[specie].set_title(f\"Inventory for Species {specie + 1}\")\n",
    "    axes[specie].set_xlabel(\"Days\")\n",
    "    axes[specie].set_ylabel(\"Inventory (m3)\")\n",
    "    axes[specie].legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95df57aa",
   "metadata": {},
   "source": [
    "#### Total Inventory Accumulated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "24a73dbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "total_inventory = [sum(I[specie, day].X for specie in range(Species)) for day in range(Days + 1)]\n",
    "\n",
    "# Plot total inventory accumulated over all species\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(range(Days + 1), total_inventory, label=\"Total Inventory\", color=\"blue\")\n",
    "plt.axhline(np.sum(Minimum_inventory), color='red', linestyle='--', label=\"Min Total Inventory\")\n",
    "plt.axhline(np.sum(Maximum_inventory), color='green', linestyle='--', label=\"Max Total Inventory\")\n",
    "plt.title(\"Total Inventory Accumulated (All Species)\")\n",
    "plt.xlabel(\"Days\")\n",
    "plt.ylabel(\"Inventory (m3)\")\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed5a79f1",
   "metadata": {},
   "source": [
    "#### Plot Production per Line in Hours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3f838b63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, Lines, figsize=(18, 5), sharey=True)\n",
    "colors = ['blue', 'orange', 'purple']  \n",
    "\n",
    "for line in range(Lines):\n",
    "    # Initialize the bottom for stacking bars\n",
    "    bottom = np.zeros(Days)\n",
    "    for specie in range(Species):\n",
    "        # Extract production data from the Gurobi variables\n",
    "        production_data = [x[specie, line, day].X for day in range(Days)]\n",
    "        \n",
    "        # Plot the production data for this line and species\n",
    "        axes[line].bar(\n",
    "            range(Days),\n",
    "            production_data,\n",
    "            color=colors[specie],\n",
    "            label=f\"Species {specie + 1}\",\n",
    "            alpha=0.7,\n",
    "            bottom=bottom\n",
    "        )\n",
    "        # Update the bottom for the next species\n",
    "        bottom += production_data\n",
    "        \n",
    "    axes[line].set_title(f\"Line {line + 1} Production\")\n",
    "    axes[line].set_xlabel(\"Days\")\n",
    "    axes[line].set_ylabel(\"Production (hours)\")\n",
    "    axes[line].legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99615119",
   "metadata": {},
   "source": [
    "#### Plot Production per Line in m3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e3669156",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, Lines, figsize=(18, 5), sharey=True)\n",
    "colors = ['blue', 'orange', 'purple']  # Colors for different species\n",
    "\n",
    "for line in range(Lines):\n",
    "    # Initialize the bottom for stacking bars\n",
    "    bottom = np.zeros(Days)\n",
    "    for specie in range(Species):\n",
    "        # Extract production data from the Gurobi variables\n",
    "        production_data = [(x[specie, line, day].X*Re[line][specie]) for day in range(Days)]\n",
    "        \n",
    "        # Plot the production data for this line and species\n",
    "        axes[line].bar(\n",
    "            range(Days),\n",
    "            production_data,\n",
    "            color=colors[specie],\n",
    "            label=f\"Species {specie + 1}\",\n",
    "            alpha=0.7,\n",
    "            bottom=bottom\n",
    "        )\n",
    "        # Update the bottom for the next species\n",
    "        bottom += production_data\n",
    "    \n",
    "    axes[line].set_title(f\"Line {line + 1} Production\")\n",
    "    axes[line].set_xlabel(\"Days\")\n",
    "    axes[line].set_ylabel(\"Production (m3)\")\n",
    "    axes[line].legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9116c42e",
   "metadata": {},
   "source": [
    "#### Setup Changes Per Line Per Day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "395199c7",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Matrix to store the setups\n",
    "Setup_counts_corrected = np.zeros((Lines, Days))\n",
    "\n",
    "# Extract the setup values from the model\n",
    "for line in range(Lines):\n",
    "    for day in range(Days):\n",
    "        Setup_counts_corrected[line, day] = s[line, day].X\n",
    "\n",
    "# Plot setup changes\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "for line in range(Lines):\n",
    "    ax.bar(range(Days), Setup_counts_corrected[line, :], label=f\"Line {line + 1}\")\n",
    "\n",
    "ax.set_title(\"Setup Changes Per Line Per Day\")\n",
    "ax.set_xlabel(\"Days\")\n",
    "ax.set_ylabel(\"Number of Setups\")\n",
    "ax.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c76e0a0c",
   "metadata": {},
   "source": [
    "#### Time Usage Per Line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "88edb6c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Production_hours = np.zeros((Lines, Days))\n",
    "Maintenance_hours = np.zeros((Lines, Days))\n",
    "Trimestral_hours = np.zeros((Lines, Days))\n",
    "Idle_hours = np.zeros((Lines, Days))\n",
    "\n",
    "# Extract data from Gurobi variables\n",
    "for line in range(Lines):\n",
    "    for day in range(Days):\n",
    "        # Sum production hours across all species for this line and day\n",
    "        Production_hours[line, day] = sum(x[specie, line, day].X for specie in range(Species))\n",
    "        # Maintenance hours\n",
    "        Maintenance_hours[line, day] = Time_maintenance * M[line, day].X\n",
    "        # Trimestral maintenance hours\n",
    "        Trimestral_hours[line, day] = Time_trimestral * Mtri[line, day].X\n",
    "        # Calculate idle hours\n",
    "        Idle_hours[line, day] = T - (Production_hours[line, day] + Maintenance_hours[line, day] + Trimestral_hours[line, day])\n",
    "\n",
    "# Plot time usage for each line\n",
    "fig, axes = plt.subplots(1, Lines, figsize=(18, 5), sharey=True)\n",
    "for line in range(Lines):\n",
    "    axes[line].bar(range(Days), Production_hours[line, :], label=\"Production Hours\", color=\"blue\", alpha=0.7)\n",
    "    axes[line].bar(range(Days), Maintenance_hours[line, :], label=\"Maintenance Hours\", color=\"orange\", alpha=0.7,\n",
    "                   bottom=Production_hours[line, :])\n",
    "    axes[line].bar(range(Days), Trimestral_hours[line, :], label=\"Trimestral Maintenance Hours\", color=\"purple\", alpha=0.7,\n",
    "                   bottom=Production_hours[line, :] + Maintenance_hours[line, :])\n",
    "    axes[line].bar(range(Days), Idle_hours[line, :], label=\"Setup Time\", color=\"gray\", alpha=0.7,\n",
    "                   bottom=Production_hours[line, :] + Maintenance_hours[line, :] + Trimestral_hours[line, :])\n",
    "    axes[line].set_title(f\"Time Usage for Line {line + 1}\")\n",
    "    axes[line].set_xlabel(\"Days\")\n",
    "    axes[line].set_ylabel(\"Hours\")\n",
    "    axes[line].legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a17cdd84",
   "metadata": {},
   "source": [
    "#### References"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b2d9681",
   "metadata": {},
   "source": [
    "Avilés, F. N., Etchepare, R. M., Aguayo, M. M., & Valenzuela, M. (2022). A mixed-integer programming model for an integrated production planning problem with preventive maintenance in the pulp and paper industry. Engineering Optimization, 55(8), 1352–1369. https://doi.org/10.1080/0305215X.2022.2086237"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eeff09af",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}