TP-recherche-operationnelle/notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TP 2-3 : Branch-and-bound applied to a knapsack problem"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initialisation (à faire une seule fois)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m\u001b[1m    Updating\u001b[22m\u001b[39m registry at `~/.julia/registries/General`\n",
      "\u001b[32m\u001b[1m   Resolving\u001b[22m\u001b[39m package versions...\n",
      "\u001b[32m\u001b[1m  No Changes\u001b[22m\u001b[39m to `~/.julia/environments/v1.6/Project.toml`\n",
      "\u001b[32m\u001b[1m  No Changes\u001b[22m\u001b[39m to `~/.julia/environments/v1.6/Manifest.toml`\n",
      "\u001b[32m\u001b[1mPrecompiling\u001b[22m\u001b[39m project...\n",
      "\u001b[32m  ✓ \u001b[39mTestOptinum\n",
      "  1 dependency successfully precompiled in 6 seconds (158 already precompiled)\n",
      "\u001b[32m\u001b[1m   Resolving\u001b[22m\u001b[39m package versions...\n",
      "\u001b[32m\u001b[1m  No Changes\u001b[22m\u001b[39m to `~/.julia/environments/v1.6/Project.toml`\n",
      "\u001b[32m\u001b[1m  No Changes\u001b[22m\u001b[39m to `~/.julia/environments/v1.6/Manifest.toml`\n",
      "\u001b[32m\u001b[1mPrecompiling\u001b[22m\u001b[39m project...\n",
      "\u001b[32m  ✓ \u001b[39mTestOptinum\n",
      "  1 dependency successfully precompiled in 3 seconds (158 already precompiled)\n"
     ]
    }
   ],
   "source": [
    "import Pkg; \n",
    "Pkg.add(\"GraphRecipes\"); Pkg.add(\"Plots\"); \n",
    "using GraphRecipes, Plots #only used to visualize the search tree at the end of the branch-and-bound"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Récupération des données"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "readKnapInstance (generic function with 1 method)"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function readKnapInstance(filename)\n",
    "    price=[]\n",
    "    weight=[]\n",
    "    capacity = -1\n",
    "    open(filename) do f\n",
    "        for i in 1:3\n",
    "            tok = split(readline(f))\n",
    "            if (tok[1] == \"ListPrices=\")\n",
    "                for i in 2:(length(tok)-1)\n",
    "                    push!(price,parse(Int64, tok[i]))\n",
    "                end\n",
    "            elseif (tok[1] == \"ListWeights=\")\n",
    "                for i in 2:(length(tok)-1)\n",
    "                    push!(weight,parse(Int64, tok[i]))\n",
    "                end\n",
    "            elseif (tok[1] == \"Capacity=\")\n",
    "                capacity = parse(Int64, tok[2])\n",
    "            else\n",
    "                println(\"Unknown read :\", tok)\n",
    "            end \n",
    "        end\n",
    "    end\n",
    "    \n",
    "    return price, weight, capacity\n",
    "end\n",
    "\n",
    "# readKnapInstance(\"data/test.opb\")\n",
    "# readKnapInstance(\"data/almost_strongly_correlated/knapPI_5_50_1000_1_-2096.opb\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Procédure d'application des tests de sondabilités TA, TO et TR pour le cas de la relaxation linéaire"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TestsSondabilite_relaxlin (generic function with 1 method)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function TestsSondabilite_relaxlin(x, price, weight, capacity, BestProfit, Bestsol, affich)\n",
    "    TA, TO, TR = false, false, false\n",
    "    if (!Constraints(x, weight, capacity)) # Test de faisabilite\n",
    "        TA = true\n",
    "        if affich\n",
    "            println(\"TA\")\n",
    "        end\n",
    "    elseif (Objective(x, price) <= BestProfit) # Test d'optimalite\n",
    "        TO = true\n",
    "        if affich\n",
    "            println(\"TO\")\n",
    "        end\n",
    "    elseif (AllDef(x)) # Test de resolution\n",
    "        TR = true\n",
    "        if affich\n",
    "            println(\"TR : solution \", \" de profit \", Objective(x, price))\n",
    "        end\n",
    "        #if (value(benef) >= BestProfit)\n",
    "        if (Objective(x, price) >= BestProfit)\n",
    "            if affich\n",
    "                println(\"\\t-> Cette solution a un meilleur profit.\")\n",
    "            end\n",
    "            Bestsol = x\n",
    "            #BestProfit=value(benef)\n",
    "            BestProfit = Objective(x, price)\n",
    "        else\n",
    "            if affich\n",
    "                println(\"\\t-> Cette solution est moins bonne.\")\n",
    "            end\n",
    "        end\n",
    "    else\n",
    "        if affich \n",
    "            println(\"non sondable\")\n",
    "        end\n",
    "    end\n",
    "    if affich\n",
    "        println(\"\\n\")\n",
    "    end\n",
    "    TA, TO, TR, Bestsol, BestProfit\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Procédure de séparation et stratégie d'exploration permettant de se placer au prochain noeud à traiter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExplorerAutreNoeud_relaxlin (generic function with 1 method)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "function SeparerNoeud_relaxlin(price, listvars, listvals)\n",
    "    # le noeud est non-sondable. Appliquer le critère de séparation pour le séparer en sous-noeuds et choisir un noeud-fils le plus à gauche   \n",
    "\n",
    "    # Cas du noeud le plus à gauche\n",
    "    predX = pop!(listvars)\n",
    "    n = length(predX)\n",
    "    nextX0 = copy(predX)\n",
    "    nextX1 = copy(predX)\n",
    "    val0 = 0\n",
    "    val1 = 0\n",
    "    for i in 1:n\n",
    "        if predX[i] == -1\n",
    "            nextX0[i] = 0\n",
    "            nextX1[i] = 1\n",
    "\n",
    "            val0 = Objective(nextX0, price)\n",
    "            val1 = Objective(nextX1, price)\n",
    "            break\n",
    "        end\n",
    "    end\n",
    "    push!(listvars, nextX1)\n",
    "    push!(listvars, nextX0)\n",
    "    push!(listvals, val1)\n",
    "    push!(listvals, val0)\n",
    "    listvars, listvals\n",
    "end\n",
    "\n",
    "\n",
    "function ExplorerAutreNoeud_relaxlin(listvars, listvals)\n",
    "    # this node is sondable, go back to parent node then right child if possible\n",
    "    \n",
    "    stop = false\n",
    "    # check if we are not at the root node\n",
    "    if (length(listvars) > 1)\n",
    "        # go back to parent node\n",
    "        var = pop!(listvars)\n",
    "        val = pop!(listvals)\n",
    "    else\n",
    "        # the root node was sondable\n",
    "        println(\"\\nFINISHED\")\n",
    "        stop = true\n",
    "    end\n",
    "    listvars, listvals, stop \n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AllDef (generic function with 1 method)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fonction objectif que l'on souhaite maximiser/minimiser\n",
    "Objective(x, price) = \n",
    "    sum(\n",
    "        if x[i] < 0\n",
    "            price[i]\n",
    "        else\n",
    "            price[i]*x[i] \n",
    "        end\n",
    "            for i in 1:length(x)\n",
    "    )\n",
    "\n",
    "# fonction permettant de vérfier toutes les contraintes du modèle\n",
    "Constraints(x, weight, capacity) =\n",
    "    sum(\n",
    "        if x[i] < 0\n",
    "            0\n",
    "        else \n",
    "            weight[i]*x[i]\n",
    "        end\n",
    "            for i in 1:length(x)\n",
    "    ) <= capacity\n",
    "\n",
    "\n",
    "function AllDef(x)\n",
    "    for i in 1:length(x)\n",
    "        if x[i] < 0\n",
    "            return false\n",
    "        end\n",
    "    end\n",
    "    return true\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "Error",
     "evalue": "Session cannot generate requests",
     "output_type": "error",
     "traceback": [
      "Error: Session cannot generate requests",
      "at S.executeCodeCell (/home/damien/.vscode/extensions/ms-toolsai.jupyter-2021.10.1101450599/out/client/extension.js:66:301742)",
      "at S.execute (/home/damien/.vscode/extensions/ms-toolsai.jupyter-2021.10.1101450599/out/client/extension.js:66:300732)",
      "at S.start (/home/damien/.vscode/extensions/ms-toolsai.jupyter-2021.10.1101450599/out/client/extension.js:66:296408)",
      "at processTicksAndRejections (internal/process/task_queues.js:93:5)",
      "at async t.CellExecutionQueue.executeQueuedCells (/home/damien/.vscode/extensions/ms-toolsai.jupyter-2021.10.1101450599/out/client/extension.js:66:312326)",
      "at async t.CellExecutionQueue.start (/home/damien/.vscode/extensions/ms-toolsai.jupyter-2021.10.1101450599/out/client/extension.js:66:311862)"
     ]
    }
   ],
   "source": [
    "function SolveKnapInstance(filename)\n",
    "\n",
    "    price, weight, capacity = readKnapInstance(filename)\n",
    "\n",
    "    println(\"Capacity : \", capacity, \" | Number of objects : \", length(price), \"\\n\")\n",
    "\n",
    "    #create the structure to memorize the search tree for visualization at the end\n",
    "    trParentnodes=Int64[] #will store orig node of arc in search tree\n",
    "    trChildnodes=Int64[] #will store destination node of arc in search tree\n",
    "    trNamenodes=[] #will store names of nodes in search tree\n",
    "        \n",
    "    #intermediate structure to navigate in the search tree\n",
    "    listvars=[]\n",
    "    listvals=[]\n",
    "\n",
    "    BestProfit=-1\n",
    "    Bestsol=[]\n",
    "\n",
    "    current_node_number=0\n",
    "    stop = false\n",
    "    affich = false\n",
    "\n",
    "    push!(listvars, [-1 for p in price])\n",
    "    push!(listvals, Objective(last(listvars), price))\n",
    "\n",
    "    while (!stop)\n",
    "        x = last(listvars)\n",
    "\n",
    "        if affich\n",
    "            print(\"----------\\nNode n°\", current_node_number, \" : \")\n",
    "            println(\" \")\n",
    "            println(\"\\nPrevious Solution memorized \", \" with bestprofit \", BestProfit, \"\\n\")\n",
    "        end\n",
    "\n",
    "        TA, TO, TR, Bestsol, BestProfit = TestsSondabilite_relaxlin(x, price, weight, capacity, BestProfit, Bestsol, affich)\n",
    "        \n",
    "        is_node_sondable = TA || TO || TR\n",
    "        if (!is_node_sondable)\n",
    "            listvars, listvals = SeparerNoeud_relaxlin(price, listvars, listvals)\n",
    "        else\n",
    "            listvars, listvals, stop = ExplorerAutreNoeud_relaxlin(listvars, listvals)\n",
    "        end\n",
    "\n",
    "        if current_node_number % 1000000 == 1000\n",
    "            affich = true\n",
    "        else\n",
    "            affich = false\n",
    "        end\n",
    "        current_node_number += 1\n",
    "    end\n",
    "\n",
    "    println(\"\\n******\\n\\nOptimal value = \", BestProfit, \"\\n\\nOptimal x=\", Bestsol)\n",
    "\n",
    "end\n",
    "\n",
    "SolveKnapInstance(\"data/subset_sum/knapPI_6_100_10000_2_-10726.opb\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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