BCP_project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Bin covering problem</h3>\n",
    "The goal is to fill multiple bins with items of the highest value so as not to exceed the size of the bin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gurobipy import *\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setting parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summed size: 6660\n",
      "Summed value: 1134\n"
     ]
    }
   ],
   "source": [
    "Items_size = {}\n",
    "Items_value = {}\n",
    "bins = [100,50,40,120]\n",
    "n = 100\n",
    "for i in range(n):\n",
    "    item = f'P{i}'\n",
    "    Items_size[item] = np.random.randint(30,100)\n",
    "    Items_value[item] = np.random.randint(2,20)\n",
    "print(f'Summed size: {np.sum(list(Items_size.values()))}')\n",
    "print(f'Summed value: {np.sum(list(Items_value.values()))}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "m = Model('BCP')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adding a decision variable and defining temporary parameters (for better visibility)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = m.addMVar((len(bins),len(Items_size.keys())), vtype=GRB.BINARY,name='items')\n",
    "y_values = np.array(list(Items_value.values()))\n",
    "y_size = np.array(list(Items_size.values()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setting objective.<br>\n",
    "The objective is maximising value of items used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "m.setObjective(sum(x[i,:]@y_values for i in range(len(bins))),GRB.MAXIMIZE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setting constraints. <br>\n",
    "First constraints limits use of items due to the volume of bin. <br>\n",
    "Second one forbids to use one item more then once."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "m.addConstrs((x[i,:]@y_size) <= np.array(bins[i]) for i in range(len(bins)))\n",
    "m.addConstrs((sum(x[:,i]) <= 1 for i in range(n)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (win64)\n",
      "Thread count: 4 physical cores, 8 logical processors, using up to 8 threads\n",
      "Optimize a model with 104 rows, 400 columns and 800 nonzeros\n",
      "Model fingerprint: 0x958ff9dc\n",
      "Variable types: 0 continuous, 400 integer (400 binary)\n",
      "Coefficient statistics:\n",
      "  Matrix range     [1e+00, 1e+02]\n",
      "  Objective range  [2e+00, 2e+01]\n",
      "  Bounds range     [1e+00, 1e+00]\n",
      "  RHS range        [1e+00, 1e+02]\n",
      "Found heuristic solution: objective 56.0000000\n",
      "Presolve removed 0 rows and 167 columns\n",
      "Presolve time: 0.00s\n",
      "Presolved: 104 rows, 233 columns, 466 nonzeros\n",
      "Variable types: 0 continuous, 233 integer (233 binary)\n",
      "Found heuristic solution: objective 94.0000000\n",
      "\n",
      "Root relaxation: objective 1.371364e+02, 13 iterations, 0.00 seconds (0.00 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  137.13636    0    5   94.00000  137.13636  45.9%     -    0s\n",
      "H    0     0                     131.0000000  137.13636  4.68%     -    0s\n",
      "H    0     0                     133.0000000  137.13636  3.11%     -    0s\n",
      "H    0     0                     134.0000000  137.13636  2.34%     -    0s\n",
      "     0     0  136.76923    0    9  134.00000  136.76923  2.07%     -    0s\n",
      "     0     0  136.76923    0    3  134.00000  136.76923  2.07%     -    0s\n",
      "     0     0  136.35897    0    1  134.00000  136.35897  1.76%     -    0s\n",
      "     0     0  135.95690    0    9  134.00000  135.95690  1.46%     -    0s\n",
      "     0     0  135.95690    0   10  134.00000  135.95690  1.46%     -    0s\n",
      "     0     0  135.66667    0   10  134.00000  135.66667  1.24%     -    0s\n",
      "H    0     0                     135.0000000  135.66667  0.49%     -    0s\n",
      "     0     0  135.66667    0   10  135.00000  135.66667  0.49%     -    0s\n",
      "\n",
      "Cutting planes:\n",
      "  Gomory: 1\n",
      "  Cover: 1\n",
      "  StrongCG: 1\n",
      "  RLT: 1\n",
      "\n",
      "Explored 1 nodes (39 simplex iterations) in 0.08 seconds (0.00 work units)\n",
      "Thread count was 8 (of 8 available processors)\n",
      "\n",
      "Solution count 9: 135 134 133 ... 56\n",
      "\n",
      "Optimal solution found (tolerance 1.00e-04)\n",
      "Best objective 1.350000000000e+02, best bound 1.350000000000e+02, gap 0.0000%\n"
     ]
    }
   ],
   "source": [
    "m.optimize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Printing solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bin 0 - Value: 48 - Size: 100/100\n",
      "['P55', 'P71', 'P73']\n",
      "Bin 1 - Value: 19 - Size: 44/50\n",
      "['P23']\n",
      "Bin 2 - Value: 18 - Size: 37/40\n",
      "['P24']\n",
      "Bin 3 - Value: 50 - Size: 120/120\n",
      "['P68', 'P84', 'P90']\n",
      "---SUMMARY---\n",
      "Summed value : 135\n",
      "Summed size: 301\n",
      "Used items:\n",
      "['P23', 'P24', 'P55', 'P68', 'P71', 'P73', 'P84', 'P90']\n"
     ]
    }
   ],
   "source": [
    "sum_value = 0\n",
    "sum_size = 0\n",
    "sum_ans = []\n",
    "for i in range(len(bins)):\n",
    "    temp_size = 0\n",
    "    temp_value = 0\n",
    "    ans = []\n",
    "    for j in range(n):\n",
    "        if x[i,j].X > 0:\n",
    "            ans.append(list(Items_size.keys())[j])\n",
    "    for l in ans:\n",
    "        temp_size += Items_size[l]\n",
    "        temp_value += Items_value[l]\n",
    "    sum_size += temp_size\n",
    "    sum_value += temp_value\n",
    "    sum_ans += ans\n",
    "    print(f'Bin {i} - Value: {temp_value} - Size: {temp_size}/{bins[i]}')\n",
    "    print(ans)\n",
    "                \n",
    "print('---SUMMARY---')\n",
    "print(f'Summed value : {sum_value}\\nSummed size: {sum_size}')\n",
    "print(f'Used items:\\n{sorted(sum_ans)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Tabu search</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "def chceck_constr(x):\n",
    "    if multi_items_constr(x) == False:\n",
    "        return False\n",
    "    if sum_constr(x) == False:\n",
    "        return False\n",
    "    return True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking if we use one item multiple times."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "def multi_items_constr(x):\n",
    "    for i in range(len(y_size)):\n",
    "        if sum(x[:,i]) > 1:\n",
    "            print(f'One item is used multiple times.\\n{x[:,i]} - column {i}')\n",
    "            return False\n",
    "    print('Passed test 1')\n",
    "    return True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking if sum is larger than bin size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sum_constr(x):\n",
    "    print('Test 2')\n",
    "    for i in range(len(bins)):\n",
    "        Sum = 0\n",
    "        for j in range(len(y_size)):\n",
    "            Sum += x[i,j]*y_size[j]\n",
    "        print(f'Bin {i} - {Sum}/{bins[i]}')\n",
    "        if Sum > bins[i]:\n",
    "            print(f'Error')\n",
    "            return False\n",
    "    print('Passed test 2')\n",
    "    return True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Summing value and size of current result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Sum_value(x):\n",
    "    Sum_values = 0\n",
    "    Sum_size = 0\n",
    "    for i in range(len(bins)):\n",
    "        for j in range(len(y_values)):\n",
    "            Sum_values += x[i,j]*y_values[j]\n",
    "            Sum_size += x[i,j]*y_size[j]\n",
    "    return Sum_values, Sum_size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generating first random solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "flag = False\n",
    "while flag == False:\n",
    "    x_rand = np.array([[0 for _ in range(len(y_size))] for _ in range(len(bins)) ])\n",
    "    for _ in range(10):\n",
    "        x_rand[np.random.randint(0,len(bins)),np.random.randint(0,len(y_size))] += np.random.randint(0,2)\n",
    "    flag = chceck_constr(x_rand)\n",
    "value_rand, size_rand= Sum_value(x_rand)\n",
    "print(flag,value_rand,x_rand)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Searching\n",
    "1. Generate random point\n",
    "2. If this point is in use than switch it to 0. If not - switch to 1\n",
    "3. Check constrains\n",
    "4. If test are passed append array to dict and return new solution. Else, return original solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tabu_search(x,i):\n",
    "    global Tabu\n",
    "    global tabu_plot_value\n",
    "    global tabu_plot_size\n",
    "    x_new = x.copy()\n",
    "    point = [np.random.randint(0,len(bins)),np.random.randint(0,len(y_size))]\n",
    "    if x_new[point[0],point[1]] == 1:\n",
    "        x_new[point[0],point[1]] = 0\n",
    "    else:\n",
    "        x_new[point[0],point[1]] = 1\n",
    "    if chceck_constr(x_new) == False:\n",
    "        value,size = Sum_value(x)\n",
    "        tabu_plot_value[i] = value\n",
    "        tabu_plot_size[i] = size\n",
    "        return x\n",
    "    else:\n",
    "        value,size = Sum_value(x_new)\n",
    "        tabu_plot_value[i] = value\n",
    "        tabu_plot_size[i] = size\n",
    "        Tabu[value] = x_new\n",
    "        return x_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "Tabu = {}\n",
    "iterations = 10000\n",
    "tabu_plot_value = [0 for _ in range(iterations)]\n",
    "tabu_plot_size = [0 for _ in range(iterations)]\n",
    "x = x_rand.copy()\n",
    "for i in range(iterations):\n",
    "    x = tabu_search(x,i)\n",
    "value_max,size_max = Sum_value(Tabu[max(list(Tabu.keys()))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sum 0 - 12 | Size 0 - 52\n",
      "Sum N - 103 | Size N - 283\n",
      "[[0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0\n",
      "  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0\n",
      "  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1\n",
      "  0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0]]\n",
      "Passed test 1\n",
      "Test 2\n",
      "Bin 0 - 94/100\n",
      "Bin 1 - 34/50\n",
      "Bin 2 - 38/40\n",
      "Bin 3 - 117/120\n",
      "Passed test 2\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(f'Sum 0 - {value_rand} | Size 0 - {size_rand}\\nSum N - {value_max} | Size N - {size_max}\\n{Tabu[max(list(Tabu.keys()))]}')\n",
    "chceck_constr(Tabu[max(list(Tabu.keys()))])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Making plots</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(tabu_plot_value)):\n",
    "    if tabu_plot_value[i] == value_max and tabu_plot_size[i] == size_max:\n",
    "        point = i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,6))\n",
    "plt.style.use('seaborn')\n",
    "plt.plot(tabu_plot_value,label = 'Value')\n",
    "plt.plot(tabu_plot_size,label = 'Size')\n",
    "plt.vlines(x=point,ymin=0,ymax=300,linewidth=2,color='r',ls='--',label='Result')\n",
    "plt.title('Tabu search')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Value')\n",
    "plt.legend()\n",
    "plt.grid(which='both', alpha=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = np.array(['Gurobi','Tabu search'])\n",
    "x = np.arange(len(labels))\n",
    "res_value = [sum_value,value_max]\n",
    "res_size = [sum_size,size_max]\n",
    "width=0.2\n",
    "plt.figure(figsize=(8,8))\n",
    "plt.style.use('seaborn')\n",
    "plt.bar(x+width/2,res_value,width,label=\"Value\")\n",
    "plt.bar(x-width/2,res_size,width,label=\"Size\")\n",
    "plt.xticks(x,labels=labels)\n",
    "plt.legend()\n",
    "plt.grid(which='both', alpha=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "5b173c1de4ba5aaae6f9efd705f2ac1dc2d13ba329406e61b65965767d7fb689"
  },
  "kernelspec": {
   "display_name": "Python 3.10.0 64-bit",
   "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": "3.10.0"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}