Notebook-B&B.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Projet Recherche Opérationelle - Branch And Bound"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lors de ce projet nous nous sommes penchés sur l'implémentation de l'algorithme de branch-and-bound pour un problème d'ordonnancement étudié en TD (présentation détaillée problème ci dessous). Outre la mise en pratique des connaissances aquises au cours de ce semestre, ce projet nous aura permis une compréhension plus fine de cet algorithme, en travaillant nottament sur chaque composante de l'algorithme : \n",
    "* sa règle de branchement \n",
    "* sa méthode d'exploration \n",
    "* le calcul de ses bornes (primales et duales)\n",
    "\n",
    "\n",
    "__Le problème considéré :__\n",
    "\n",
    "Un atelier de production doit usiner un ensemble $J=\\{1,\\dots,n\\}$ de pièces. Pour cela, l'atelier possède une machine unique. Chaque pièce $j \\in J$ nécessitera $p_j$ unités de temps pour être usinée. Nous supposons que l'on se trouve à la date t=0 et chaque pièce $j \\in J$ doit être prête à la date $d_j$. Si cette date n'est pas respectée, l'usine devra payer une pénalité $w_j$ par unité de temps de retard. Il s'agit donc d'un __problème de minimisation__ dans lequel nous devons déterminer l'ordre de passage des pièces sur la machine qui peremttra de minimiser les pénalités de retard.\n",
    "\n",
    "__Le plan du projet :__\n",
    "\n",
    "* __Structures de données__\n",
    "\n",
    "Choix des différentes structures utiles pour l'algorithme et pour définir les instances du problème. \n",
    "\n",
    "* __Déclaration externe de méthodes utiles pour branch and bound__ \n",
    "\n",
    "Implémentation de diverses méthodes utiles lors de l'implémentation de l'algorithme de branch and bound. \n",
    "\n",
    "* __Travail sur les composantes de branch and bound__ \n",
    "\n",
    "Nous implémentons différentes méthodes de calcul de bornes primales et duales ainsi que différentes méthodes d'exploration de l'arbre de branchement. Nous étudierons l'effet de ces différentes méthodes sur les performances de l'algorithme.\n",
    "\n",
    "* __Algorithme de branch and bound__ \n",
    "\n",
    "Nous implémentons l'algorithme de branch and bound. \n",
    "\n",
    "* __Choix des composantes du branch and bound__\n",
    "\n",
    "Nous testons l'influence de chaque composante sur les performances du Branch and bound.\n",
    "\n",
    "* __Evaluation du Branch and Bound__ \n",
    "\n",
    "Nous vérifions que l'Algorithme renvoie bien le cout minimum en comparant ses sorties et celles d'une résolution du problème par PLNE. Nous étudions les performances de l'algorithme en fonction du nombre d'instance d'un problème.\n",
    "\n",
    "* __Visualisation de l'arbre de branchement__\n",
    "\n",
    "Nous implémentons ici une méthode de visualisation de l'arbre de branchement.\n",
    "\n",
    "* __Atelier à plusieurs machines__\n",
    "\n",
    "Pour aller plus loin, nous considérons une extension du problème. \n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importation des modules\n",
    "\n",
    "Nous importons tout d'abord tous les modules nécessaires dans ce projet. Nous utilisons les modules classiques __numpy__ et __matplotlib (pyplot)__. Nous utilisons égalements les modules __networkx__ et un module particulier de pydot (cellule pour installation ci dessous) lors de la visualisation de parcours de l'arbre de branchement."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "conda install pydot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import math\n",
    "import networkx as nx\n",
    "from networkx.drawing.nx_pydot import graphviz_layout\n",
    "from matplotlib import pyplot as plt\n",
    "import copy\n",
    "import time\n",
    "from pulp import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Structure de données\n",
    "\n",
    "*Nous définissons un certain nombre de structures de données, liées au problème ou à l'algorithme de Branch and Bound.*\n",
    "\n",
    "### Les instances du problème\n",
    "\n",
    "Le problème possède 3 données clefs : \n",
    "* le nombre d'unités de temps nécessaire à l'usinage de chaque pièce\n",
    "* la date d'usinage a laquelle chque pièce doit être usinée\n",
    "* la pénalité de retard associée à chaque pièce\n",
    "\n",
    "Nous faisons le choix d'utiliser des listes pour stocker ces 3 instances du problème. Un problème donné sera donc caractérisé par 3 listes : \n",
    "* une liste __p__\n",
    "* une liste __d__\n",
    "* une liste __w__\n",
    "\n",
    "### Les noeuds \n",
    "\n",
    "Chaque noeud de l'arbre de branchement sera un objet. Chaque objet Noeud créé possède :\n",
    "* Un indexe : __idx__\n",
    "* Un noeud parent : __parent__\n",
    "* Une liste de noeuds enfants : __children__\n",
    "* Une borne duale associée (lower bound ici, puisqu'il s'agit d'un problème de minimisation) : __lb__\n",
    "* Une liste de noeuds visités : __visited__.\n",
    "* Une méthode d'affichage.\n",
    "\n",
    "Dans cette première partie, les index de noeuds seront des entiers, leur stockage sera rapide. Dans la partie __affichage__ les index seront des chaines de caractères plus ou moins longues, leur stockage sera légèrement plus long."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Exemple__\n",
    "\n",
    "![Arbre de branchement](img/EX2.png)\n",
    "\n",
    "En considérant que les noeud en vert on déjà été visités et que les noeuds rouges ne l'ont pas été, le noeud 1 représenté ci dessus possèderait (selon la structure choisie) les éléments suivants : \n",
    "* __idx__=1\n",
    "* __parent__=0\n",
    "* __children__=[ 4 , 5 ]\n",
    "* __visited__=[ 0 ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Node:\n",
    "    \n",
    "    def __init__(self,idx, parent, children, lb, visited):\n",
    "        self.idx = idx\n",
    "        self.parent = parent\n",
    "        self.children = children\n",
    "        self.lb = lb\n",
    "        self.visited = visited\n",
    "        \n",
    "    def __str__(self):\n",
    "        return (\"Parent : \" +str(self.parent)+\n",
    "                \"\\n Children : \"+str(self.children) + \n",
    "                \"\\n lb : \"+str(self.lb)+ \n",
    "                \"\\n visited : \"+str(self.visited))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Déclaration externe de fonctions utiles pour branch and bound\n",
    "\n",
    "### Fonctions abroresence et computeCost\n",
    "\n",
    "*Nous implémentons à présent un certain nombre de fonctions que nous utiliserons lors de l'implémentation de l'algorithme de branch and bound.*\n",
    "\n",
    "La première fonction est une fonction arborescence. Elle nous permet de donner une branche complète d'arbre de branchement en partant d'une feuille. Dans l'__exemple__ ci-dessus, la fonction : __arborescence(4,Tree)__ renverrait : [4,1,0]. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Afin de déterminer facilement le cout d'une solution donnée, nous implémentons une méthode générale computeCost. A partir des données du problème (p,d,w) et d'un ordre de taches à effectuer, la fonction renvoie le cout de la solution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def arborescence(node,Tree):\n",
    "    branch = []\n",
    "    while node.parent != None:\n",
    "        branch.append(node)\n",
    "        node = Tree[node.parent]\n",
    "    branch.append(node)\n",
    "    \n",
    "    return branch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def computeCost(p,d,w,order):\n",
    "    t=0\n",
    "    cost=0\n",
    "    for idx in order:\n",
    "        t += p[idx]\n",
    "        cost += max(0,(t-d[idx])*w[idx])\n",
    "    return cost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Afin de tester la dernière méthode nous utilisons le problème vu en TD (*exercice 7, TD branch and bound*) dont les instances sont rappelées ci-dessous : \n",
    "\n",
    "* __unités de temps pour que les pièces soient usinées :__ p=[12,8,15,9]\n",
    "* __date auxquelles les pièces doivent être prêtes :__ d=[16,26,25,27]\n",
    "* __pénalités de retard :__ w=[4,5,3,5]\n",
    "\n",
    "<img src=\"img/tdexample.png\"\n",
    "     style=\"\" />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = [12,8,15,9]\n",
    "d = [16,26,25,27]\n",
    "w = [4,5,3,5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La solution que nous testons (ordre d'usinage : [0,1,3,2]) est la solution optimale, obtenue lors du TD. Son coût vaut 67. On vérifie donc que la fonction computeCost nous donne bien 67 avec cet ordre d'usinage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "67"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "computeCost(p,d,w,[0,1,3,2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Travail sur les composantes de Branch and Bound\n",
    "\n",
    "### Calculs de bornes primales\n",
    "\n",
    "L'idée de ce projet est de tester différentes composantes de l'algorithme de Branch and Bound, afin de voir leur impact sur les performances de l'algorithme. \n",
    "\n",
    "Nous nous concentrons tout d'abord sur le calcul des bornes primales. Le calcul de borne primale s'effectue au début de l'algorithme. Plus le calcul est fin, i.e plus la borne primale obtenue possède une valeur faible, plus l'algorithme ira vite. En effet, une borne primale assez faible permettra d'élaguer plus rapidement des branches et donc de parcourir moins de noeuds.\n",
    "L'idée est donc d'essayer de trouver la méthode la plus interessante.\n",
    "\n",
    "\n",
    "Nous avons fait le choix de coder différentes fonctions :\n",
    "* __getPrimal_0__ : nous calculons une solution en usinant les pièces selon l'ordre croissant des dates d'usinage au plus tard.\n",
    "\n",
    "\n",
    "* __getPrimal_1__ : nous calculons une solution en usinant les pièces selon l'ordre croissant des (dates d'usinage au plus tard-le temps d'usinage)$*$pénalité\n",
    "Comme les premières pièces engendrent le moins de retard, nous nous sommes dit qu'il est possible d'obtenir une meilleure borne primale en réflechissant à la position des derniers éléments. Cela nous a amené à la fonction : \n",
    "\n",
    "\n",
    "* __getPrimal_2__ : nous calculons une solution en mettant à la dernière position disponible l'élément non traité donnant lieu à la plus petite pénalité de retard."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getPrimal_0(p,d,w):\n",
    "    sortedTasks = np.argsort(d)\n",
    "    cost = computeCost(p,d,w,sortedTasks)\n",
    "    return(cost,sortedTasks)\n",
    "\n",
    "\n",
    "def getPrimal_1(p,d,w):\n",
    "    sortedTasks = (np.argsort((np.array(d)-np.array(p))*np.array(w)))[::-1]\n",
    "    cost = computeCost(p,d,w,sortedTasks)\n",
    "    return(cost,sortedTasks)\n",
    "\n",
    "\n",
    "def getPrimal_2(p,d,w):\n",
    "    tasksLeft = [int(i) for i in range(len(p))]\n",
    "    order = []\n",
    "    while len(tasksLeft)!= 0:\n",
    "        time = np.sum(np.array(p)[tasksLeft])\n",
    "        penalities = (time-np.array(d)[tasksLeft])*np.array(w)[tasksLeft]\n",
    "        sortedTasks = np.argsort(penalities)\n",
    "        order.append(tasksLeft[sortedTasks[0]])\n",
    "        tasksLeft.pop(sortedTasks[0])\n",
    "    order.reverse()\n",
    "    cost = computeCost(p,d,w,order)\n",
    "    return(cost,order)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Temps production :  [12, 8, 15, 9]\n",
      "Date limite :       [16, 26, 25, 27]\n",
      "Pénalités :         [4, 5, 3, 5]\n",
      "0 :  (136, array([0, 2, 1, 3], dtype=int64))\n",
      "1 :  (133, array([3, 1, 2, 0], dtype=int64))\n",
      "2 :  (67, [0, 1, 3, 2])\n"
     ]
    }
   ],
   "source": [
    "print(\"Temps production : \", p)\n",
    "print(\"Date limite :      \", d)\n",
    "print(\"Pénalités :        \", w)\n",
    "print(\"0 : \",getPrimal_0(p,d,w))\n",
    "print(\"1 : \",getPrimal_1(p,d,w))\n",
    "print(\"2 : \",getPrimal_2(p,d,w))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calcul des bornes duales\n",
    "\n",
    "Nous nous concentrons maintenant sur le calcul des bornes duales. Le calcul de bornes duales s'effectue à chaque ajout d'un nouveau noeud dans l'arbre. Ces bornes duales constituent le coeur de l'algorithme du Branch and Bound, et plus le calcul sera rapide ou les bornes de qualité, plus l'algorithme sera efficace.  \n",
    "Encore une fois, comme les premières pièces engendrent le moins de retard, nous définissons deux \"types\" de fonctions de calcul de bornes duales : Un calcul qui calcule ces bornes pour des pièces placées en début de planning, et une pour des pièces placées en fin de planning.\n",
    "\n",
    "L'idée est donc d'essayer de trouver la méthode la plus interessante.\n",
    "\n",
    "Nous avons donc codé les 3 fonctions suivantes : \n",
    "\n",
    "* __getDual_0__ : Si on a fixé l'ordre de $k$ pièces on calcule les pénalités de retard de ces $k$ pièces et on ajoute les pénalités des autres pièces en supposant qu'elles sont en $(k+1)$ème position. (Dans le cas où les $k$ pièces sont positionnées aux positions $[1,2,\\ldots,k-1,k]$)\n",
    "\n",
    "\n",
    "* __getDual_1__ : Si on a fixé l'ordre de $k$ pièces on calcule les pénalités de retard de ces $k$ pièces. (Dans le cas où les $k$ pièces sont positionnées aux positions $[1,2,\\ldots,k-1,k]$)\n",
    "\n",
    "\n",
    "* __getDual_2__ : Si on a fixé l'ordre de $k$ pièces on calcule les pénalités de retard de ces $k$ pièces. (Dans le cas où les $k$ pièces sont positionnées aux positions $[n-k+1, n-k+2,\\ldots, n-1,n]$)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getDual_0(p,d,w,visited):\n",
    "    dual=0\n",
    "    time=0\n",
    "    notVisited=list(range(len(p)))\n",
    "    for i in visited:\n",
    "        retard=((p[i]+time)-d[i])\n",
    "        dual+=retard*w[i]*(retard>0)\n",
    "        time+=p[i]\n",
    "        notVisited.remove(i)\n",
    "        \n",
    "    for j in notVisited:\n",
    "        retard=(p[j]+time)-d[j]\n",
    "        dual+=retard*w[j]*(retard>0)\n",
    "    return dual\n",
    "\n",
    "def getDual_1(p,d,w,visited):\n",
    "    dual = 0\n",
    "    time = 0\n",
    "    notVisited = list(range(len(p)))\n",
    "    for i in visited:\n",
    "        retard = ((p[i]+time)-d[i])\n",
    "        dual += retard*w[i]*(retard>0)\n",
    "        time += p[i]\n",
    "        notVisited.remove(i)\n",
    "    return dual\n",
    "\n",
    "def getDual_2(p,d,w,visited):\n",
    "    dual = 0\n",
    "    time = np.sum(p)\n",
    "    for i in visited:\n",
    "        retard = (time-d[i])\n",
    "        dual += retard*w[i]*(retard>0)\n",
    "        time -= p[i] \n",
    "    return dual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Temps production :  [12, 8, 15, 9]\n",
      "Date limite :       [16, 26, 25, 27]\n",
      "Pénalités :         [4, 5, 3, 5]\n",
      "0 :  40\n",
      "1 :  0\n"
     ]
    }
   ],
   "source": [
    "p = [12,8,15,9]\n",
    "d = [16,26,25,27]\n",
    "w = [4,5,3,5]\n",
    "\n",
    "print(\"Temps production : \", p)\n",
    "print(\"Date limite :      \", d)\n",
    "print(\"Pénalités :        \", w)\n",
    "print(\"0 : \", getDual_0(p,d,w,[0,1]))\n",
    "print(\"1 : \", getDual_1(p,d,w,[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Note :__  Afin de mieux comprendre ce qu'implique le choix d'une des deux méthodes et comment elle permet de dérouler l'arbre, nous proposerons des fonctions de visualisation qui permettront d'observer la construction de ce dernier."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Méthodes d'exploration\n",
    "\n",
    "Finalement, nous allons essayer différentes méthodes d'exploration de l'arbre de décision : \n",
    "\n",
    "* __exploration_0__ : \"Breadth First\" - Parcours en largeur de l'arbre. \n",
    "\n",
    "Cette méthode explorera tous les noeuds d'un niveau de l'arbre avant de passer au suivant.\n",
    "\n",
    "* __exploration_1__ : \"Depth First\" - Parcours en profondeur de l'arbre. \n",
    "\n",
    "Contrairement à la méthode précédente, cette méthode d'exploration va parcourir les branches en intégralité avant de remonter. Cette approche est à priori profitable puisque elle permet d'ajuster plus rapidement $z_P$ et donc de plus rapidement élaguer des branches de l'arbre.\n",
    "\n",
    "* __exploration_2__ : \"Best First\" - Meilleur d'abord. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def exploration_0(Queue,Tree,UB):\n",
    "    return(Queue[0])\n",
    "\n",
    "def exploration_1(Queue,Tree,UB):\n",
    "    return(Queue[-1])\n",
    "\n",
    "def exploration_2(Queue,Tree,UB):\n",
    "    bestNode = Queue[-1]\n",
    "    bestLB = -math.inf\n",
    "    for idx in Queue:\n",
    "        if Tree[idx].lb > bestLB and Tree[idx].lb < UB:\n",
    "            bestLB = Tree[idx].lb\n",
    "            bestNode = idx\n",
    "    return(bestNode)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Branch and Bound\n",
    "\n",
    "Nous allons ici décrire le fonctionnement de notre algorithme de _Branch and Bound_ qui sera au coeur de notre projet. Afin de facilement comparer les performances de nos différentes approches et de leurs méthodes associées, nous avons décidé de faire un algorithme le plus général possibles. Ses entrées sont les suivantes : \n",
    "\n",
    "__Entrées :__\n",
    "\n",
    "* __p :__ Donnée du problème : Temps de production de chaque pièce à usiner.\n",
    "\n",
    "* __d :__ Donnée du problème : Date limite de production de chaque pièce à usiner. Chaque pièce dont la production se terminera après cette date entraînera une pénalité de retard. \n",
    "\n",
    "* __w :__ Donnée du problème : Poids associé à la pénalité de retard. Dans un contexte d'entreprise, certains retards seront \"moins importants\" que d'autres. Une pièce très importante aura un poids associé à sa pénalité de retard très élevé.\n",
    "\n",
    "* __byLast :__ Booléen : Indique si les pièces sont ajoutées par la fin (=_True_)(_Méthode 2_) ou par le début (=_False_)(_Méthode 1_). Cette option n'aura pas beaucoup d'impact sur notre algorithme, elle permettra cependant de bien penser à inverser l'ordre des pièces avant d'utiliser notre fonction calculant le coût de l'ordonnancement proposé. \n",
    "\n",
    "* __getPrimal :__ Fonction utilisateur :  Fonction calculant la première borne primale au début de l'algorithme. Passer cet argument sous forme de fonction nous permettra de comparer diférentes approches sans avoir à coder un nouvel algorithme de _Branch and Bound_.\n",
    "\n",
    "* __getDual :__ Fonction utilisateur :  Fonction caculant les bornes duales à chaque noeud de l'arbre. L'intérêt de passer cet argument en paramètre est le même que pour _getPrimal_.\n",
    "\n",
    "* __exploration :__ Fonction utilisateur :  Fonction retournant le prochain noeud à visiter à chaque étape de l'algorithme.\n",
    "\n",
    "__Sorties :__\n",
    "\n",
    "* __UB :__ Solution du problème.\n",
    "\n",
    "* __currentOrder :__ Ordonnancement associé à cette solution.\n",
    "\n",
    "* __nbIterations :__ Nombre d'itérations de l'algorithme avant de retourner la solution.\n",
    "\n",
    "* __branch :__ Liste des noeuds remontant du noeud solution jusqu'au noeud racine (Utilisé pour surligner la solution sur l'affichage de l'arbre de branchement).\n",
    "\n",
    "* __Tree :__ Arbre de branchement final."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def branch_and_bound(p,d,w,byLast,getPrimal,getDual,exploration):\n",
    "    \n",
    "    Tasks = list(range(len(p)))\n",
    "    Tree = [] # list of all nodes created (a list of Node objects)\n",
    "    Queue = [] # list of nodes to process (a list of integers with the index of nodes to process in the Tree)\n",
    "    LB = 0 # best Lower Bound of the problem \n",
    "    \n",
    "    UB,currentOrder = getPrimal(p,d,w) #We start by computing the first primal/upper bound.\n",
    "\n",
    "    bestNode = Node(-1,-1, [], 0 ,[]) #default best node\n",
    "    \n",
    "    root = Node(0,None, [], 0, []) # define the root node of the tree\n",
    "    Tree.append(root) # start the tree with the root node\n",
    "    Queue.append(0) # start the list of nodes to process with the root node\n",
    "    \n",
    "    #continue processing nodes until the queue is empty\n",
    "    \n",
    "    nbIterations = 0\n",
    "    \n",
    "    while len(Queue)!=0 :\n",
    "        \n",
    "        # process the nodes in the queue according to the chosen exploration method\n",
    "        \n",
    "        currentIndex = exploration(Queue,Tree,UB)\n",
    "        currentNode = Tree[currentIndex]\n",
    "        nbIterations += 1\n",
    "        \n",
    "        if currentNode.lb < UB:\n",
    "            \n",
    "            # If the processed node corresponds to a \"full-schedule\" we check whether this solution is an improvement or not\n",
    "            # If so, we update the problem Upper Bound / best order / best node\n",
    "            \n",
    "            if len(currentNode.visited) == len(p):\n",
    "                order = copy.copy(currentNode.visited)\n",
    "                if byLast:\n",
    "                    order.reverse()\n",
    "                solutionCost = computeCost(p,d,w,order)\n",
    "                if solutionCost <= UB:\n",
    "                    UB = solutionCost\n",
    "                    currentOrder = order\n",
    "                    bestNode = currentNode\n",
    "            else:\n",
    "                \n",
    "                #for each task not processed yet we create a new node\n",
    "                for nextTask in (set(Tasks)-set(currentNode.visited)):\n",
    "                    nextVisited = copy.copy(currentNode.visited)\n",
    "                    nextVisited.append(nextTask)\n",
    "                    nextLb = getDual(p,d,w, nextVisited)\n",
    "\n",
    "                    newNode = Node(len(Tree),currentIndex, [], nextLb,nextVisited)\n",
    "                    Tree.append(newNode)\n",
    "                    Queue.append(len(Tree)-1)\n",
    "                    currentNode.children.append(len(Tree)-1)\n",
    "            \n",
    "        # When the processing of the node is completed remove the node from the queue     \n",
    "        Queue.remove(currentIndex)\n",
    "\n",
    "    # return the  solution\n",
    "    \n",
    "    if (UB-bestNode.lb)<=0.0001:\n",
    "        branch = arborescence(bestNode,Tree)\n",
    "    \n",
    "    # If the best node is not a leaf, the bestNode variable is still the default one default\n",
    "    else:\n",
    "        bestLB = -math.inf\n",
    "        for node in Tree:\n",
    "            if node.lb > bestLB and node.lb <=UB:\n",
    "                bestLB = node.lb\n",
    "                bestNode=node\n",
    "        branch = arborescence(bestNode,Tree)\n",
    "\n",
    "    return UB, currentOrder, nbIterations, branch, Tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Choix des composantes du Branch and Bound\n",
    "\n",
    "*Nous mettons en place plusieurs tests afin de comparer les performances de nos différentes méthodes de calcul de bornes, des méthodes d'exploration, et de notre algorithme sur des problèmes de différentes tailles*\n",
    "\n",
    "### Bornes duales, Bornes primales, Méthode d'exploration : \n",
    "\n",
    "Comme décrit plus haut, nous avons codé différentes fonction pour le calcul de bornes primales et de bornes duales, ainsi que différentes méthodes d'exploration de l'arbre de _Branch and Bound_. Nous comparerons ces différentes méthodes de la manière suivante : \n",
    "\n",
    "* __Bornes Primales :__ La comparaison des méthodes de calcul de bornes primales sera assez simple. En effet plus la valeur trouvée est petite plus la méthode est efficace. Nous comparerons donc les valeurs moyennes des bornes primales trouvées sur différentes instances aléatoires avec chacune de nos fonctions.\n",
    "\n",
    "\n",
    "* __Bornes Duales :__ La comparaison des méthodes de calcul de bornes duales ne se fera pas sur la valeur de ces dernières, mais directement sur la performance de l'agorithme. Nos différentes méthodes correspondent à deux règles de branchement et impliquent des parcours de l'arbre différent, avec un ajout des pièces _\"croissant\"/\"décroissant\"_. Nous comparerons le nombre d'itérations et le temps d'exécution nécessaire à l'obtention d'une solution.\n",
    "\n",
    "\n",
    "* __Méthodes d'exploration :__ Nous avons proposé trois méthodes d'exploration de l'arbre de _Branch and Bound_ différentes. nous observerons les implication du choix de chacune de ces méthodes sur le nombre d'itérations et le temps d'exécution de l'algorithme."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Instances aléatoires : \n",
    "\n",
    "*Avant d'entamer les tests de performances, nous introduisons un générateur d'instances*\n",
    "\n",
    "Nous définissons ci-dessous une fonction qui nous permettra de générer des instances aléatoires de problèmes d'ordonnancement. Cette fonction nous permettra de comparer les performances de nos différentes approches sur des problèmes de taille choisie.\n",
    "\n",
    "Plus le paramètre alpha diminue et plus les dates limites sont rapprochées, et donc le problème difficile. \n",
    "\n",
    "*Par la suite nous règlerons souvent le paramètre alpha à 0.3 pour être sûrs d'avoir des hors délais*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def genInstancies(n,alpha):\n",
    "    p=np.random.randint(1,15,n)\n",
    "    d=np.random.randint(3,int(alpha*n*15),n)\n",
    "    w=np.random.randint(1,5,n)\n",
    "    return(p,d,w)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Un exemple aléatoire  : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Temps production :  [ 7 12 10  3  8 10  2  5]\n",
      "Date limite :       [ 9 29 30 31 26 10 22 29]\n",
      "Pénalités :         [1 1 4 1 2 2 1 3]\n"
     ]
    }
   ],
   "source": [
    "p,d,w = genInstancies(8,.3)\n",
    "print(\"Temps production : \", p)\n",
    "print(\"Date limite :      \", d)\n",
    "print(\"Pénalités :        \", w)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Choix de la méthode de calcul pour les bornes primales\n",
    "\n",
    "__Rappel__: plus une fonction de calcul de borne primale est performante, plus la valeur de la borne primale trouvée est petite."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* __Exemple du cours__ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = [12,8,15,9]\n",
    "d = [16,26,25,27]\n",
    "w = [4,5,3,5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparons les résultats des méthodes de calcul de bornes primales sur cet exemple (*EX7 TD branch and bound*)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Temps production :  [12, 8, 15, 9]\n",
      "Date limite :       [16, 26, 25, 27]\n",
      "Pénalités :         [4, 5, 3, 5]\n",
      "Borne primale obtenue avec getPrimal_0 :  136\n",
      "Borne primale obtenue avec getPrimal_1 :  133\n",
      "Borne primale obtenue avec getPrimal_2 :  67\n"
     ]
    }
   ],
   "source": [
    "print(\"Temps production : \", p)\n",
    "print(\"Date limite :      \", d)\n",
    "print(\"Pénalités :        \", w)\n",
    "print(\"Borne primale obtenue avec getPrimal_0 : \",getPrimal_0(p,d,w)[0])\n",
    "print(\"Borne primale obtenue avec getPrimal_1 : \",getPrimal_1(p,d,w)[0])\n",
    "print(\"Borne primale obtenue avec getPrimal_2 : \",getPrimal_2(p,d,w)[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Conclusion :__ sur cet exemple, la méthode getPrimal_2 est bien plus performante que les méthodes 0 et 1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* __Exemple aléatoire__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "p,d,w = genInstancies(8,.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparons les résultats des méthodes de calcul de bornes primales sur cet exemple en utilisant la méthode get_dual_2 et ce pour les 3 méthodes d'exploration implémentées auparavant. Nous étudierons également l'influence de ces méthodes sur le nombre d'itérations de l'algorithme de Branch and Bound."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Depth First\n",
      "getPrimal         0 | 1  | 2\n",
      "Primal Bound   : 459 | 429 | 280\n",
      "Solution       : 227 | 227 | 227\n",
      "nbIterations   : 1318 | 1307 | 879\n",
      "Execution time : 0.031006813049316406 | 0.029006481170654297 | 0.018004179000854492\n",
      "\n",
      "\n",
      "Breadth First\n",
      "getPrimal         0 | 1  | 2\n",
      "Primal Bound   : 459 | 429 | 280\n",
      "Solution       : 227 | 227 | 227\n",
      "nbIterations   : 57299 | 43806 | 3058\n",
      "Execution time : 1.9335119724273682 | 1.4849631786346436 | 0.09001970291137695\n"
     ]
    }
   ],
   "source": [
    "#DFS\n",
    "start_0 = time.time()\n",
    "solution_0, order_0, nbIterations_0, branch_0, Tree = branch_and_bound(p,d,w, True, getPrimal_0, getDual_2,exploration_1)\n",
    "end_0 = time.time()\n",
    "start_1 = time.time()\n",
    "solution_1, order_1, nbIterations_1, branch_1, Tree = branch_and_bound(p,d,w, True, getPrimal_1, getDual_2,exploration_1)\n",
    "end_1 = time.time()\n",
    "start_2 = time.time()\n",
    "solution_2, order_2, nbIterations_2, branch_2, Tree = branch_and_bound(p,d,w, True, getPrimal_2, getDual_2,exploration_1)\n",
    "end_2 = time.time()\n",
    "\n",
    "print(\"Depth First\")\n",
    "print(\"getPrimal         0 | 1  | 2\")\n",
    "print(\"Primal Bound   : {} | {} | {}\".format(getPrimal_0(p,d,w)[0],getPrimal_1(p,d,w)[0],getPrimal_2(p,d,w)[0]))\n",
    "print(\"Solution       : {} | {} | {}\".format(solution_0,solution_1,solution_2))\n",
    "print(\"nbIterations   : {} | {} | {}\".format(nbIterations_0,nbIterations_1,nbIterations_2))\n",
    "print(\"Execution time : {} | {} | {}\".format(end_0-start_0,end_1-start_1,end_2-start_2))\n",
    "\n",
    "#BFS\n",
    "start_0 = time.time()\n",
    "solution_0, order_0, nbIterations_0, branch_0, Tree = branch_and_bound(p,d,w, True, getPrimal_0, getDual_2,exploration_0)\n",
    "end_0 = time.time()\n",
    "start_1 = time.time()\n",
    "solution_1, order_1, nbIterations_1, branch_1, Tree = branch_and_bound(p,d,w, True, getPrimal_1, getDual_2,exploration_0)\n",
    "end_1 = time.time()\n",
    "start_2 = time.time()\n",
    "solution_2, order_2, nbIterations_2, branch_2, Tree = branch_and_bound(p,d,w, True, getPrimal_2, getDual_2,exploration_0)\n",
    "end_2 = time.time()\n",
    "\n",
    "print(\"\\n\\nBreadth First\")\n",
    "print(\"getPrimal         0 | 1  | 2\")\n",
    "print(\"Primal Bound   : {} | {} | {}\".format(getPrimal_0(p,d,w)[0],getPrimal_1(p,d,w)[0],getPrimal_2(p,d,w)[0]))\n",
    "print(\"Solution       : {} | {} | {}\".format(solution_0,solution_1,solution_2))\n",
    "print(\"nbIterations   : {} | {} | {}\".format(nbIterations_0,nbIterations_1,nbIterations_2))\n",
    "print(\"Execution time : {} | {} | {}\".format(end_0-start_0,end_1-start_1,end_2-start_2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Résultat :__ Encore une fois, la méthode $getPrimal\\_2$ semble être la plus efficace. \n",
    "\n",
    "Notons également que le choix de la méthode de calcul de borne primale est corrélé au nombre d'itérations de l'algorithme de branch and bound :\n",
    "\n",
    "* lorsque l'on utilise un parcours en largeur, le nombre d'itérations varie régulièrement du simple au centuple entre l'exécution démarrée avec $getPrimal\\_2$ et celle avec $getPrimal\\_1$. Une meilleure borne primale permet de rapidement élaguer des noeuds en haut de l'arbre et ainsi de réduire considérablement le temps d'exécution. \n",
    "\n",
    "* lorsque l'on utilise un parcours en profondeur, cette différence est moins marquée puisque l'algorithme va de lui même aller mettre à jour cette borne primale assez rapidement. On observe quand même régulièrement des nombres d'itérations allant du simple au double entre $getPrimal\\_2$ et $getPrimal\\_1$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* __Valeurs de bornes primales sur un grand nombre d'instances aléatoires__\n",
    "\n",
    "Comme le titre l'indique, nous comparons ici les performances des méthodes de calcul de bornes primales pour de nombreuses instances aléatoires. L'idée est simple : générer 100 valeurs de bornes primales pour des problèmes de taille 4 à 12, puis calculer la valeur moyenne de la borne primale obtenue avec chaque méthode."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "N = int(1e3) #Nombre d'échantillons\n",
    "n = 12 #Taille maximum du problème\n",
    "resultats_0 = [[] for i in range(4,n)]\n",
    "resultats_1 = [[] for i in range(4,n)]\n",
    "resultats_2 = [[] for i in range(4,n)]\n",
    "for _ in range(N):\n",
    "    for i in range(4,n):\n",
    "        p,d,w = genInstancies(i,.3)\n",
    "        resultats_0[i-4].append(getPrimal_0(p,d,w)[0])\n",
    "        resultats_1[i-4].append(getPrimal_1(p,d,w)[0])\n",
    "        resultats_2[i-4].append(getPrimal_2(p,d,w)[0])\n",
    "resultats_0 = [np.mean(res) for res in resultats_0]\n",
    "resultats_1 = [np.mean(res) for res in resultats_1]\n",
    "resultats_2 = [np.mean(res) for res in resultats_2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "X = list(range(4,n))\n",
    "plt.plot(X,resultats_0, label=\"getPrimal_0\")\n",
    "plt.plot(X,resultats_1, label=\"getPrimal_1\")\n",
    "plt.plot(X,resultats_2, label=\"getPrimal_2\")\n",
    "plt.legend()\n",
    "plt.xlabel(\"Taille du problème\")\n",
    "plt.ylabel(\"Borne Primale moyenne\")\n",
    "plt.title(\"Comparaisons des fonctions de calcul de bornes primales : N={}\".format(N))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Conclusion :__ Comme nous avions pu le présager avec les tests précédents, la fonction $getPrimal\\_2$ donne de meilleurs résultats que $getPrimal\\_0$, qui sont toutes les deux bien meilleures que $getPrimal\\_1$. \n",
    "\n",
    "Par la suite, nous utiliserons ainsi majoritairement $getPrimal\\_2$ par défaut pour la comparaison des autres fonctions et les tests de performances. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Choix de la méthode d'exploration \n",
    "\n",
    "Nous utiliserons maintenant la méthode de calcul de bornes primales $getPrimal\\_2$ et comparerons les nombre d'itérations de l'algorithme pour Breadth First, Depth First et Best First  avec les méthodes getDual_0,getDual_1 et getDual_2.\n",
    "\n",
    "* __Exemple de cours__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = [12,8,15,9]\n",
    "d = [16,26,25,27]\n",
    "w = [4,5,3,5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Breadth First\n",
      "getDual         0 | 1  | 2\n",
      "Solution     : 67 | 67 | 67\n",
      "nbIterations : 23 | 59 | 8\n"
     ]
    }
   ],
   "source": [
    "solution_0, order_0, nbIterations_0, branch_0, Tree = branch_and_bound(p,d,w, False, getPrimal_2, getDual_0,exploration_0)\n",
    "solution_1, order_1, nbIterations_1, branch_1, Tree = branch_and_bound(p,d,w, False, getPrimal_2, getDual_1,exploration_0)\n",
    "solution_2, order_2, nbIterations_2, branch_2, Tree = branch_and_bound(p,d,w, True, getPrimal_2, getDual_2,exploration_0)\n",
    "print(\"Breadth First\")\n",
    "print(\"getDual         0 | 1  | 2\")\n",
    "print(\"Solution     : {} | {} | {}\".format(solution_0,solution_1,solution_2))\n",
    "print(\"nbIterations : {} | {} | {}\".format(nbIterations_0,nbIterations_1,nbIterations_2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Depth First\n",
      "getDual         0 | 1  | 2\n",
      "Solution     : 67 | 67 | 67\n",
      "nbIterations : 23 | 59 | 8\n"
     ]
    }
   ],
   "source": [
    "solution_0, order_0, nbIterations_0, branch_0, Tree = branch_and_bound(p,d,w, False, getPrimal_2, getDual_0,exploration_1)\n",
    "solution_1, order_1, nbIterations_1, branch_1, Tree = branch_and_bound(p,d,w, False, getPrimal_2, getDual_1,exploration_1)\n",
    "solution_2, order_2, nbIterations_2, branch_2, Tree = branch_and_bound(p,d,w, True, getPrimal_2, getDual_2,exploration_1)\n",
    "print(\"Depth First\")\n",
    "print(\"getDual         0 | 1  | 2\")\n",
    "print(\"Solution     : {} | {} | {}\".format(solution_0,solution_1,solution_2))\n",
    "print(\"nbIterations : {} | {} | {}\".format(nbIterations_0,nbIterations_1,nbIterations_2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best First\n",
      "getDual         0 | 1  | 2\n",
      "Solution     : 67 | 67 | 67\n",
      "nbIterations : 23 | 59 | 8\n"
     ]
    }
   ],
   "source": [
    "solution_0, order_0, nbIterations_0, branch_0, Tree = branch_and_bound(p,d,w, False, getPrimal_2, getDual_0,exploration_2)\n",
    "solution_1, order_1, nbIterations_1, branch_1, Tree = branch_and_bound(p,d,w, False, getPrimal_2, getDual_1,exploration_2)\n",
    "solution_2, order_2, nbIterations_2, branch_2, Tree = branch_and_bound(p,d,w, True, getPrimal_2, getDual_2,exploration_2)\n",
    "print(\"Best First\")\n",
    "print(\"getDual         0 | 1  | 2\")\n",
    "print(\"Solution     : {} | {} | {}\".format(solution_0,solution_1,solution_2))\n",
    "print(\"nbIterations : {} | {} | {}\".format(nbIterations_0,nbIterations_1,nbIterations_2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Conclusion :__ Nous ne remarquons pas de différences entre les méthodes d'exploration \"Breadth First / Depth First / Best First\". \n",
    "\n",
    "Notons aussi que sur cet exemple, la méthode de branchement \"inverse\" avec getDual_2 semble bien plus efficace que les deux autres méthodes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* __Instances aléatoires__\n",
    "\n",
    "Après n'avoir trouvé aucune différence de nombre d'itérations entre les différentes méthodes sur l'exemple vu en TD, nous allons comparer les méthodes sur des instances aléatoires de taille raisonnable et ajoutant un nouvel élément de comparaison : le temps d'exécution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Depth First\n",
      "getDual           0 | 1  | 2\n",
      "Solution       : 56 | 56 | 56\n",
      "nbIterations   : 984 | 6904 | 89\n",
      "Execution time : 0.19304299354553223\n",
      "\n",
      "Breadth First\n",
      "getDual           0 | 1  | 2 \n",
      "Solution       : 56 | 56 | 56\n",
      "nbIterations   : 1791 | 8386 | 875\n",
      "Execution time : 0.279066801071167\n",
      "\n",
      "Best First : \n",
      "getDual           0 | 1  | 2 \n",
      "Solution       : 56 | 56 | 56\n",
      "nbIterations   : 1055 | 6841 | 279\n",
      "Execution time : 15.406826972961426\n"
     ]
    }
   ],
   "source": [
    "p,d,w = genInstancies(7,.3)\n",
    "\n",
    "\n",
    "start = time.time()\n",
    "solution_0, order_0, nbIterations_0, branch_0, Tree = branch_and_bound(p,d,w, False, getPrimal_2, getDual_0,exploration_1)\n",
    "solution_1, order_1, nbIterations_1, branch_1, Tree = branch_and_bound(p,d,w, False, getPrimal_2, getDual_1,exploration_1)\n",
    "solution_2, order_2, nbIterations_2, branch_2, Tree = branch_and_bound(p,d,w, True, getPrimal_2, getDual_2,exploration_1)\n",
    "end = time.time()\n",
    "print(\"Depth First\")\n",
    "print(\"getDual           0 | 1  | 2\")\n",
    "print(\"Solution       : {} | {} | {}\".format(solution_0,solution_1,solution_2))\n",
    "print(\"nbIterations   : {} | {} | {}\".format(nbIterations_0,nbIterations_1,nbIterations_2))\n",
    "print(\"Execution time :\", end-start)\n",
    "\n",
    "\n",
    "start = time.time()\n",
    "solution_0, order_0, nbIterations_0, branch_0, Tree = branch_and_bound(p,d,w, False, getPrimal_2, getDual_0,exploration_0)\n",
    "solution_1, order_1, nbIterations_1, branch_1, Tree = branch_and_bound(p,d,w, False, getPrimal_2, getDual_1,exploration_0)\n",
    "solution_2, order_2, nbIterations_2, branch_2, Tree = branch_and_bound(p,d,w, True, getPrimal_2, getDual_2,exploration_0)\n",
    "end = time.time()\n",
    "print(\"\\nBreadth First\")\n",
    "print(\"getDual           0 | 1  | 2 \")\n",
    "print(\"Solution       : {} | {} | {}\".format(solution_0,solution_1,solution_2))\n",
    "print(\"nbIterations   : {} | {} | {}\".format(nbIterations_0,nbIterations_1,nbIterations_2))\n",
    "print(\"Execution time :\", end-start)\n",
    "\n",
    "start = time.time()\n",
    "solution_0, order_0, nbIterations_0, branch_0, Tree = branch_and_bound(p,d,w, False, getPrimal_2, getDual_0,exploration_2)\n",
    "solution_1, order_1, nbIterations_1, branch_1, Tree = branch_and_bound(p,d,w, False, getPrimal_2, getDual_1,exploration_2)\n",
    "solution_2, order_2, nbIterations_2, branch_2, Tree = branch_and_bound(p,d,w, True, getPrimal_2, getDual_2,exploration_2)\n",
    "end = time.time()\n",
    "print(\"\\nBest First : \")\n",
    "print(\"getDual           0 | 1  | 2 \")\n",
    "print(\"Solution       : {} | {} | {}\".format(solution_0,solution_1,solution_2))\n",
    "print(\"nbIterations   : {} | {} | {}\".format(nbIterations_0,nbIterations_1,nbIterations_2))\n",
    "print(\"Execution time :\", end-start)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Conclusion :__ Si on ne s'intéresse qu'au nombre d'itérations, les méthodes d'exploration \"Depth First\" et \"Best First\" semblement surpasser la méthode \"Breadth First\". En effet, on obtient quasiment systématiquement un nombre d'itérations plus important avec cette dernière et ce pour les raisons que nous avons évoquées auparavant: Le parcours en profondeur permet de plus rapidement mettre à jour la valeur de $z_P$ et donc d'élaguer plus rapidement des branches. \n",
    "\n",
    "Cependant, lorsque l'on s'intéresse aux temps d'éxécution, c'est bien la méthode \"Best First\" qui est la moins efficace. Elle s'exécute en un nombre d'itérations plus faible que la méthode \"Breadth First\" mais chaque itération est beaucoup plus coûteuse et son temps d'exécution est largement supérieur à celui des deux autres. Ainsi, cette méthode d'exploration sera à bannir sur de plus grandes instances."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* __Comparaison des méthodes sur un nombre d'instances aléatoires élevé__\n",
    "\n",
    "\n",
    "__1)Temps d'exécution__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "N = int(1e2) #Nombre d'échantillons\n",
    "n = 7 #Taille maximum du problème\n",
    "resultats_0 = [[] for i in range(3,n)]\n",
    "resultats_1 = [[] for i in range(3,n)]\n",
    "resultats_2 = [[] for i in range(3,n)]\n",
    "for _ in range(N):\n",
    "    for i in range(3,n):\n",
    "        p,d,w = genInstancies(i,.3)\n",
    "        \n",
    "        start_0 = time.time()\n",
    "        _, _, _, _, _ = branch_and_bound(p,d,w, True, getPrimal_2, getDual_2,exploration_0)\n",
    "        end_0 = time.time()\n",
    "        \n",
    "        start_1 = time.time()\n",
    "        _, _, _, _, _ = branch_and_bound(p,d,w, True, getPrimal_2, getDual_2,exploration_1)\n",
    "        end_1 = time.time()\n",
    "        \n",
    "        start_2 = time.time()\n",
    "        _, _, _, _, _ = branch_and_bound(p,d,w, True, getPrimal_2, getDual_2,exploration_2)\n",
    "        end_2 = time.time()\n",
    "        \n",
    "        resultats_0[i-3].append(end_0-start_0)\n",
    "        resultats_1[i-3].append(end_1-start_1)\n",
    "        resultats_2[i-3].append(end_2-start_2)\n",
    "        \n",
    "resultats_0 = [np.mean(res) for res in resultats_0]\n",
    "resultats_1 = [np.mean(res) for res in resultats_1]\n",
    "resultats_2 = [np.mean(res) for res in resultats_2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "X = list(range(3,n))\n",
    "plt.plot(X,resultats_2, label=\"exploration_2 - Best First\")\n",
    "plt.plot(X,resultats_0, label=\"exploration_0 - Breadth First\")\n",
    "plt.plot(X,resultats_1, label=\"exploration_1 - Depth First\")\n",
    "plt.legend()\n",
    "plt.xlabel(\"Taille du problème\")\n",
    "plt.xticks(X)\n",
    "plt.ylabel(\"Temps d'exécution moyen\")\n",
    "plt.title(\"Comparaisons des méthodes d'exploration : N={}\".format(N))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__2)Nombre d'itérations__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "N = int(1e2) #Nombre d'échantillons\n",
    "n = 7 #Taille maximum du problème\n",
    "resultats_0 = [[] for i in range(3,n)]\n",
    "resultats_1 = [[] for i in range(3,n)]\n",
    "resultats_2 = [[] for i in range(3,n)]\n",
    "for _ in range(N):\n",
    "    for i in range(3,n):\n",
    "        p,d,w = genInstancies(i,.3)\n",
    "        _, _, nbIterations_0, _, _ = branch_and_bound(p,d,w, True, getPrimal_2, getDual_2,exploration_0)\n",
    "        _, _, nbIterations_1, _, _ = branch_and_bound(p,d,w, True, getPrimal_2, getDual_2,exploration_1)\n",
    "        _, _, nbIterations_2, _, _ = branch_and_bound(p,d,w, True, getPrimal_2, getDual_2,exploration_2)\n",
    "        \n",
    "        resultats_0[i-3].append(nbIterations_0)\n",
    "        resultats_1[i-3].append(nbIterations_1)\n",
    "        resultats_2[i-3].append(nbIterations_2)\n",
    "        \n",
    "resultats_0 = [np.mean(res) for res in resultats_0]\n",
    "resultats_1 = [np.mean(res) for res in resultats_1]\n",
    "resultats_2 = [np.mean(res) for res in resultats_2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "X = list(range(3,n))\n",
    "plt.plot(X,resultats_2, label=\"exploration_2 - Best First\")\n",
    "plt.plot(X,resultats_0, label=\"exploration_0 - Breadth First\")\n",
    "plt.plot(X,resultats_1, label=\"exploration_1 - Depth First\")\n",
    "plt.legend()\n",
    "plt.xlabel(\"Taille du problème\")\n",
    "plt.ylabel(\"Nombre d'itérations moyen\")\n",
    "plt.xticks(X)\n",
    "plt.title(\"Comparaisons des méthodes d'exploration : N={}\".format(N))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Conclusion :__ Que ce soit en termes de temps d'exécution, ou en termes des nombre d'itérations, la méthode d'exploration la plus performante semble être la méthode _Depth First_. La méthode _Best First_ est assez similaire en nombre d'itérations mais chaque itération étant beaucoup plus longue, son temps d'exécution est largement supérieur. Finalement, la méthode _Breadth First_ est toujours moins bonne que la méthode _Depth First_, bien que cette différence soit moins flagrante en termes de temps d'exécution.\n",
    "\n",
    "Comme évoqué précédemment, la méthode _Depth First_ permet de plus rapidement mettre à jour la valeur de la borne primale du problème et ainsi d'élaguer plus rapidement les noeuds, ce qui a pour conséquence de réduire le nombre d'itérations de l'algorithme et ainsi son temps d'exécution.\n",
    "\n",
    "__Nous utiliserons donc la méthode \"Depth First\" pour la suite.__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Choix de la méthode de calcul de bornes duales\n",
    "\n",
    "Maintenant que nous savons quelles méthodes de caclul de bornes primales et d'exploration utiliser, nous allons pouvoir nous attaquer au coeur du problème et comparer les performances de nos différentes approches du problème ou règles de branchement avec le calcul des bornes duales.\n",
    "\n",
    "Pour rappel, les méthodes $getDual\\_0$ et $getDual\\_1$ correspondent à un ajout des tâches par le début et la fonction $getDual\\_2$ correspond à un ajout des tâches en commençant par la fin.\n",
    "\n",
    "* __getDual_0__ : Si on a fixé l'ordre de $k$ pièces on calcule les pénalités de retard de ces $k$ pièces et on ajoute les pénalités des autres pièces en supposant qu'elles sont en $(k+1)$ème position. (Dans le cas où les $k$ pièces sont positionnées aux positions $[1,2,\\ldots,k-1,k]$).\n",
    "\n",
    "\n",
    "* __getDual_1__ : Si on a fixé l'ordre de $k$ pièces on calcule les pénalités de retard de ces $k$ pièces. (Dans le cas où les $k$ pièces sont positionnées aux positions $[1,2,\\ldots,k-1,k]$).\n",
    "\n",
    "\n",
    "* __getDual_2__ : Si on a fixé l'ordre de $k$ pièces on calcule les pénalités de retard de ces $k$ pièces. (Dans le cas où les $k$ pièces sont positionnées aux positions $[n-k+1, n-k+2,\\ldots, n-1,n]$)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* __Comparaison des méthodes sur un grand nombre d'instances aléatoires__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__1) Temps d'exécution__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "N = int(1e2) #Nombre d'échantillons\n",
    "n = 7 #Taille maximum du problème\n",
    "resultats_0 = [[] for i in range(3,n)]\n",
    "resultats_1 = [[] for i in range(3,n)]\n",
    "resultats_2 = [[] for i in range(3,n)]\n",
    "resultats_3 = [[] for i in range(3,n)]\n",
    "for _ in range(N):\n",
    "    for i in range(3,n):\n",
    "        p,d,w = genInstancies(i,.3)\n",
    "        \n",
    "        start_0 = time.time()\n",
    "        _, _, _, _, _ = branch_and_bound(p,d,w, False, getPrimal_2, getDual_0,exploration_1)\n",
    "        end_0 = time.time()\n",
    "        \n",
    "        start_1 = time.time()\n",
    "        _, _, _, _, _ = branch_and_bound(p,d,w, False, getPrimal_2, getDual_1,exploration_1)\n",
    "        end_1 = time.time()\n",
    "        \n",
    "        start_2 = time.time()\n",
    "        _, _, _, _, _ = branch_and_bound(p,d,w, True, getPrimal_2, getDual_2,exploration_1)\n",
    "        end_2 = time.time()\n",
    "        \n",
    "        resultats_0[i-3].append(end_0-start_0)\n",
    "        resultats_1[i-3].append(end_1-start_1)\n",
    "        resultats_2[i-3].append(end_2-start_2)\n",
    "        \n",
    "resultats_0 = [np.mean(res) for res in resultats_0]\n",
    "resultats_1 = [np.mean(res) for res in resultats_1]\n",
    "resultats_2 = [np.mean(res) for res in resultats_2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "X = list(range(3,n))\n",
    "plt.plot(X,resultats_0, label=\"getDual_0\")\n",
    "plt.plot(X,resultats_1, label=\"getDual_1\")\n",
    "plt.plot(X,resultats_2, label=\"getDual_2\")\n",
    "plt.legend()\n",
    "plt.xlabel(\"Taille du problème\")\n",
    "plt.xticks(X)\n",
    "plt.ylabel(\"Temps d'exécution moyen\")\n",
    "plt.title(\"Comparaisons des méthodes d'exploration : N={}\".format(N))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__2) Nombre d'itérations__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "N = int(1e2) #Nombre d'échantillons\n",
    "n = 7 #Taille maximum du problème\n",
    "resultats_0 = [[] for i in range(3,n)]\n",
    "resultats_1 = [[] for i in range(3,n)]\n",
    "resultats_2 = [[] for i in range(3,n)]\n",
    "resultats_3 = [[] for i in range(3,n)]\n",
    "for _ in range(N):\n",
    "    for i in range(3,n):\n",
    "        p,d,w = genInstancies(i,.3)\n",
    "        _, _, nbIterations_0, _, _ = branch_and_bound(p,d,w, False, getPrimal_2, getDual_0,exploration_1)\n",
    "        _, _, nbIterations_1, _, _ = branch_and_bound(p,d,w, False, getPrimal_2, getDual_1,exploration_1)\n",
    "        _, _, nbIterations_2, _, _ = branch_and_bound(p,d,w, True, getPrimal_2, getDual_2,exploration_1)\n",
    "        \n",
    "        resultats_0[i-3].append(nbIterations_0)\n",
    "        resultats_1[i-3].append(nbIterations_1)\n",
    "        resultats_2[i-3].append(nbIterations_2)\n",
    "        \n",
    "resultats_0 = [np.mean(res) for res in resultats_0]\n",
    "resultats_1 = [np.mean(res) for res in resultats_1]\n",
    "resultats_2 = [np.mean(res) for res in resultats_2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "X = list(range(3,n))\n",
    "plt.plot(X,resultats_0, label=\"getDual_0\")\n",
    "plt.plot(X,resultats_1, label=\"getDual_1\")\n",
    "plt.plot(X,resultats_2, label=\"getDual_2\")\n",
    "plt.legend()\n",
    "plt.xlabel(\"Taille du problème\")\n",
    "plt.ylabel(\"Nombre d'itérations moyen\")\n",
    "plt.xticks(X)\n",
    "plt.title(\"Comparaisons des méthodes d'exploration : N={}\".format(N))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Résultats :__ Comme précédemment, on observe que la règle de branchement qui consiste à ajouter les tâches en partant de la fin est bien plus efficace que celle qui les ajoute au début. \n",
    "\n",
    "\n",
    "Sans grandes surprises, la méthode $getDual\\_0$ est plus performante que la méthode $getDual\\_1$. En effet la première donnera toujours des bornes duales supérieures à la deuxième, étant donné qu'elle prend en compte les pénalités de retard des pièces déjà placées mais aussi de celles qui ne sont pas encore placées mais qui auront forcément du retard.\n",
    "\n",
    "\n",
    "L'algorithme de Branch and Bound est plus performant avec la méthode $getDual\\_2$ qu'avec la méthode $getDual\\_0$. C'est donc cette fonction que nous utiliserons pour comparer les performances du _Branch and Bound_ sur des problèmes de taille variée."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bilan \n",
    "\n",
    "Afin d'optimiser les performance de l'algorithme de Branch and Bound, nous utiliserons les méthodes : \n",
    "* __getPrimal_2__\n",
    "* __getDual_2__\n",
    "* __exploration_1__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test de l'algorithme de _Branch and Bound_ sur des problèmes de tailles différentes\n",
    "\n",
    "Nous réalisons ici plusieurs tests de notre algorithme : nous vérifions d'abord que l'algorithme fonctionne, puis étudions ses performances. \n",
    "\n",
    "### Comparaison avec un PLNE \n",
    "\n",
    "Dans un premier temps, il est nécessaire de voir si l'algorithme est fonctionnel, i.e si il renvoie bien une solution de cout minimal. Pour vérfier cela, nous implémentons la résolution du même problème d'ordonnancement par l'intermédiaire d'un PLNE, et comparons les valeurs obtenues via l'algorithme de Branch&Bound, et celles obtenues par l'intermédiaire du PLNE. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Soit $J$ l'ensemble des pièces à usiner. \n",
    "\n",
    "__Variables :__\n",
    "\n",
    "* $r_j$ est le retard d'usinage de la pièce j.\n",
    "* $f_j$ est \"le temps\"/\"la date\" à laquelle est usinée la pièce j.\n",
    "* $x_{ij}\\in \\{0,1\\}$, =1 ssi la pièce i est usinée avant la pièce j.\n",
    "\n",
    "__Fonction objectif :__ \n",
    "\n",
    "* on veut minimiser le cout total des pénalités de retard : min$\\underset{j\\in J}{\\sum} w_jr_j$\n",
    "\n",
    "__Contraintes :__\n",
    "* le retard d'usinage de chaque pièce correspond à sa date de fabrication - la date d a laquele elle doit être prête : $r_j \\geq f_j-d_j$, $~~~~~\\forall j \\in J$\n",
    "* le retard d'usinage de chaque pièce est positif ou nul : $r_j \\geq 0$, $~~~~~\\forall j \\in J$\n",
    "* la date de fin d'usinage de chaque pièce correspond au temps nécessaire pour usiner les pièces précédentes + le temps d'usinage de la pièce considérée : $f_j=p_j+\\underset{\\underset{i\\neq j}{i \\in J}}{\\sum} p_ix_{ij}$, $~~~~~\\forall j \\in J$\n",
    "* Si la pièce i est usinée avant la pièce j, alors j ne peut pas être usinée avant i :$x_{ij}+x_{ji}=1$, $~~~~~\\forall (i,j) \\in J^2,~~i<j$\n",
    "* Transitivité : si i est usinée avant j et j est usinée avant k, alors i est usinée avant k : $x_{ik} \\geq x_{ij}+x_{jk}-1$, $~~~~~\\forall(i,j,k) \\in J^3,~~i<j$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plne_Ordonnancement(p,d,w):\n",
    "    \n",
    "    Tasks=range(len(p)) #Liste des taches \n",
    "    \n",
    "    # Declaration du modèle\n",
    "    model = LpProblem(\"Ordonnancement\", LpMinimize)\n",
    "    \n",
    "    # Variables \n",
    "    x = LpVariable.dicts(\"x\", ((i, j) for i in Tasks for j in Tasks),0,1,LpInteger) #x_{i,j}=1 si i est usinée avant j\n",
    "    f= LpVariable.dicts(\"f\",Tasks,0,None,LpInteger) # f_j = date de fin d'usinage de la pièce j \n",
    "    r= LpVariable.dicts(\"r\",Tasks,0,None,LpInteger) #r_j = retard lié à la pièce j (forcément positif)\n",
    "    \n",
    "    # Contraintes :\n",
    "    for j in Tasks:\n",
    "        model+= f[j]==p[j]+lpSum(p[i]*x[i,j] for i in Tasks)-p[j]*x[j,j]\n",
    "        model+= r[j]>=f[j]-d[j]  #retard >= fin d'usinage - date de rendu souhaite\n",
    "        \n",
    "    #Contraintes d'ordre    \n",
    "    for i in Tasks : \n",
    "        for j in range(i) : \n",
    "            model+= x[i,j]+x[j,i]==1 #Si i avant j alors j est après i \n",
    "            for k in Tasks:\n",
    "                model+= x[i,k]>= x[i,j]+x[j,k]-1 #Transitivité\n",
    "    \n",
    "    \n",
    "    # Objectif : \n",
    "    model+=lpSum(w[i]*r[i] for i in Tasks) #Minimisation des pénalités de retard\n",
    "    \n",
    "    #Resolution du PLNE : \n",
    "    starttime=time.time() #Pour avoir le temps d'execution\n",
    "    model.solve(PULP_CBC_CMD())\n",
    "    solveTime=time.time()-starttime\n",
    "    \n",
    "    #Affichage des éléments de résolution\n",
    "    f_val=[]\n",
    "    for i in range(0,len(Tasks)):\n",
    "        f_val.append(model.variables()[i].varValue)\n",
    "    order=np.argsort(f_val)\n",
    "    \n",
    "        \n",
    "    #print(\"Temps de résolution = \", solveTime)\n",
    "    print(\"Statut de la solution = \", LpStatus[model.status])\n",
    "    #print(\"Valeur optimale = \", value(model.objective))\n",
    "    #print(\"Ordre des taches =\",order)\n",
    "    return(solveTime,value(model.objective),order)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparons à présent les valeurs optimales obtenues via l'algorithme de branch and bound, et via le PLNE. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p :  [11  5 10 12  9 13]\n",
      "d :  [20  3  4 30 34 17]\n",
      "w :  [2 3 2 1 2 1]\n",
      "Statut de la solution =  Optimal\n",
      "Execution time : B&B : 0.002000093460083008 | PLNE : 0.03000783920288086\n",
      "Cout de la solution : B&B : 102 | PLNE : 102.0\n",
      "Ordre des taches : B&B : [1, 2, 0, 4, 3, 5] | PLNE : [1 2 0 4 3 5]\n",
      "\n",
      "\n",
      "p :  [ 7 12  5  8  6  8]\n",
      "d :  [41 41 42 38 21 13]\n",
      "w :  [4 1 3 2 4 3]\n",
      "Statut de la solution =  Optimal\n",
      "Execution time : B&B : 0.0 | PLNE : 0.04600930213928223\n",
      "Cout de la solution : B&B : 5 | PLNE : 5.0\n",
      "Ordre des taches : B&B : [5, 4, 3, 2, 0, 1] | PLNE : [5 0 4 2 3 1]\n",
      "\n",
      "\n",
      "p :  [ 7  4  2  5  5 11]\n",
      "d :  [22 14  8 12 42 14]\n",
      "w :  [4 3 3 3 2 3]\n",
      "Statut de la solution =  Optimal\n",
      "Execution time : B&B : 0.0010006427764892578 | PLNE : 0.04501056671142578\n",
      "Cout de la solution : B&B : 45 | PLNE : 45.0\n",
      "Ordre des taches : B&B : [1, 2, 3, 0, 5, 4] | PLNE : [3 2 1 0 5 4]\n",
      "\n",
      "\n",
      "p :  [ 2 13 10 11  7  3]\n",
      "d :  [ 8  3 41 35 18  6]\n",
      "w :  [3 3 1 1 2 3]\n",
      "Statut de la solution =  Optimal\n",
      "Execution time : B&B : 0.0020003318786621094 | PLNE : 0.038008689880371094\n",
      "Cout de la solution : B&B : 65 | PLNE : 65.0\n",
      "Ordre des taches : B&B : [0, 5, 1, 4, 3, 2] | PLNE : [0 5 1 4 3 2]\n",
      "\n",
      "\n",
      "p :  [10  2  2  8  8 13]\n",
      "d :  [44 38 30  3 18 13]\n",
      "w :  [3 1 4 3 2 3]\n",
      "Statut de la solution =  Optimal\n",
      "Execution time : B&B : 0.005001068115234375 | PLNE : 0.03800797462463379\n",
      "Cout de la solution : B&B : 65 | PLNE : 65.0\n",
      "Ordre des taches : B&B : [3, 5, 4, 2, 1, 0] | PLNE : [3 5 4 2 1 0]\n",
      "\n",
      "\n",
      "p :  [ 1  5  4  3 13 12]\n",
      "d :  [28 28  3  7 40 42]\n",
      "w :  [2 3 3 2 3 3]\n",
      "Statut de la solution =  Optimal\n",
      "Execution time : B&B : 0.0020003318786621094 | PLNE : 0.023006677627563477\n",
      "Cout de la solution : B&B : 3 | PLNE : 3.0\n",
      "Ordre des taches : B&B : [2, 3, 0, 1, 4, 5] | PLNE : [2 3 1 5 0 4]\n",
      "\n",
      "\n",
      "p :  [ 7 10  3  6 13 12]\n",
      "d :  [10 19  9 10 39 12]\n",
      "w :  [2 2 1 4 1 1]\n",
      "Statut de la solution =  Optimal\n",
      "Execution time : B&B : 0.0020008087158203125 | PLNE : 0.03200697898864746\n",
      "Cout de la solution : B&B : 64 | PLNE : 64.0\n",
      "Ordre des taches : B&B : [2, 3, 0, 1, 5, 4] | PLNE : [3 2 0 1 5 4]\n",
      "\n",
      "\n",
      "p :  [ 6  3  6 13  5  3]\n",
      "d :  [37 11 25 33 22  3]\n",
      "w :  [3 1 4 4 3 2]\n",
      "Statut de la solution =  Optimal\n",
      "Execution time : B&B : 0.0 | PLNE : 0.024005413055419922\n",
      "Cout de la solution : B&B : 0 | PLNE : 0.0\n",
      "Ordre des taches : B&B : [5, 1, 4, 2, 3, 0] | PLNE : [5 4 1 2 3 0]\n",
      "\n",
      "\n",
      "p :  [ 4  9  6  3 12 12]\n",
      "d :  [15 18 20 15 23 42]\n",
      "w :  [3 1 2 3 1 1]\n",
      "Statut de la solution =  Optimal\n",
      "Execution time : B&B : 0.0 | PLNE : 0.044010162353515625\n",
      "Cout de la solution : B&B : 19 | PLNE : 19.0\n",
      "Ordre des taches : B&B : [3, 0, 2, 1, 4, 5] | PLNE : [0 3 1 2 4 5]\n",
      "\n",
      "\n",
      "p :  [ 4 14 10 11 12  7]\n",
      "d :  [34  3  4  6 24 19]\n",
      "w :  [3 1 2 1 3 3]\n",
      "Statut de la solution =  Optimal\n",
      "Execution time : B&B : 0.0020003318786621094 | PLNE : 0.034008026123046875\n",
      "Cout de la solution : B&B : 120 | PLNE : 120.0\n",
      "Ordre des taches : B&B : [2, 5, 4, 0, 3, 1] | PLNE : [2 5 4 0 3 1]\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in range(10):\n",
    "    p,d,w = genInstancies(6,.5)\n",
    "    print(\"p : \", p)\n",
    "    print(\"d : \", d)\n",
    "    print(\"w : \", w)\n",
    "    start= time.time()\n",
    "    solBB, orderBB, nbIterations, branch, Tree=branch_and_bound(p,d,w, True, getPrimal_2, getDual_2,exploration_1)\n",
    "    solveTimeBB=time.time()-start\n",
    "    solveTimePLNE,solPLNE,orderPLNE=plne_Ordonnancement(p,d,w)\n",
    "    print(\"Execution time : B&B : {} | PLNE : {}\".format(solveTimeBB,solveTimePLNE))\n",
    "    print(\"Cout de la solution : B&B : {} | PLNE : {}\".format(solBB,solPLNE))\n",
    "    print(\"Ordre des taches : B&B : {} | PLNE : {}\".format(orderBB,orderPLNE))\n",
    "    print(\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Conclusion :__ les valeurs optimales obtenues par l'algorithme de Branch and Bound coïncident avec celles obtenues via le PLNE. Cela atteste du bon fonctionnement de l'algorithme. \n",
    "\n",
    "Remarquons deux choses : \n",
    "* Dans certains cas, l'odre des taches de la solution optimale obtenu par PLNE diffère de celui obtenu par le Branch and Bound mais le cout de la solution est identique, donc la conclusion ci dessus tient toujours.\n",
    "* Nous voyons dès à présent que l'algorithme de Branch and Bound est nettement plus performant (en temps de calcul) que celui du PLNE pour la résolution. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test de de performance de _Branch and Bound__ sur des problèmes de tailles différentes : \n",
    "\n",
    "Testonts à présent la performance de l'algorithme. Commencons par comparer le temps d'execution de ce dernier au temps d'execution du PLNE pour résoudre des problèmes aléatoire de taille allant de 3 à 16."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statut de la solution =  Optimal\n",
      "Statut de la solution =  Optimal\n",
      "Statut de la solution =  Optimal\n",
      "Statut de la solution =  Optimal\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 1 #Nombre d'échantillons\n",
    "n = 7 #Taille maximum du problème\n",
    "alpha = 0.5\n",
    "\n",
    "resultats_PLNE = [[] for i in range(3,n)]\n",
    "resultats_BB = [[] for i in range(3,n)]\n",
    "\n",
    "for _ in range(N):\n",
    "    for i in range(3,n):\n",
    "        p,d,w = genInstancies(i,alpha)\n",
    "                \n",
    "        start = time.time()\n",
    "        _, _, _, _, _ = branch_and_bound(p,d,w, True, getPrimal_2, getDual_2,exploration_1)\n",
    "        end = time.time()\n",
    "        \n",
    "        resultats_BB[i-3].append(end-start)\n",
    "        \n",
    "        solveTimePLNE, _, _ = plne_Ordonnancement(p,d,w)\n",
    "        \n",
    "        resultats_PLNE[i-3].append(solveTimePLNE)\n",
    "        \n",
    "resultats_BB = [np.mean(res) for res in resultats_BB]\n",
    "resultats_PLNE = [np.mean(res) for res in resultats_PLNE]\n",
    "resultats_BB = np.array(resultats_BB)\n",
    "resultats_PLNE = np.array(resultats_PLNE)\n",
    "\n",
    "\n",
    "plt.figure(figsize=(10,6))\n",
    "X = list(range(3,n))\n",
    "plt.plot(X,resultats_BB,label=\"Branch and Bound\")\n",
    "plt.plot(X,resultats_PLNE,label=\"PLNE\")\n",
    "plt.xlabel(\"Taille du problème\")\n",
    "plt.xticks(X)\n",
    "plt.ylabel(\"Temps d'exécution moyen\")\n",
    "plt.title(\"Temps d'exécution en fonction de la taille du problème - alpha:{} : N={}\".format(alpha,N))\n",
    "plt.legend()\n",
    "#plt.savefig('PERF_PLNE_BB.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Résultat :__ la résolution du problème via l'algorithme de Branch & Bound se fait bien plus rapidmeent qu'avec le PLNE. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, \"Temps d'exécution en fonction de la taille du problème - alpha:0.5 : N=1\")"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 1 #Nombre d'échantillons\n",
    "n = 8 #Taille maximum du problème\n",
    "alpha = 0.5\n",
    "\n",
    "resultats_BB = [[] for i in range(3,n)]\n",
    "\n",
    "for _ in range(N):\n",
    "    for i in range(3,n):\n",
    "        p,d,w = genInstancies(i,alpha)\n",
    "                \n",
    "        start = time.time()\n",
    "        _, _, _, _, _ = branch_and_bound(p,d,w, True, getPrimal_2, getDual_2,exploration_1)\n",
    "        end = time.time()\n",
    "        \n",
    "        resultats_BB[i-3].append(end-start)\n",
    "        \n",
    "\n",
    "resultats_BB = [np.mean(res) for res in resultats_BB]\n",
    "resultats_BB = np.array(resultats_BB)\n",
    "\n",
    "\n",
    "\n",
    "plt.figure(figsize=(10,6))\n",
    "X = list(range(3,n))\n",
    "plt.plot(X,resultats_BB)\n",
    "plt.xlabel(\"Taille du problème\")\n",
    "plt.xticks(X)\n",
    "plt.ylabel(\"Temps d'exécution moyen\")\n",
    "plt.title(\"Temps d'exécution en fonction de la taille du problème - alpha:{} : N={}\".format(alpha,N))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Résultats :__ Bien que performante par rapport aux autres approches, notre fonction _Brand and Bound_ utilisée avec la méthode 2 (remplissage par la fin), $getDual\\_3$ (Notre meilleure méthode de calcul de bornes duales) et $getPrimal\\_2$ (Notre meilleure méthode de calcul de bornes primales) se retrouve rapidement \"bloquée\" sur des problèmes de taille importante. Pour des problèmes avec 15 pièces à usiner, il n'est pas rare de dépasser la minute de temps d'exécution. Notre problème d'ordonnancement est un problème NP-Difficile et il n'est pas étonnant de le voir exploser en temps aussi rapidement."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualisation de l'arbre de branchement.\n",
    "\n",
    "L'idée est ici d'implémenter une fonction de visualisation de l'arbre de branchement. \n",
    "\n",
    "### Redéfinition de quelques éléments de structure\n",
    "\n",
    "Comme évoqué aupravant, nous changeons les index des noeuds. Nous sommes conscients que stocker des entiers est moins couteux en temps de calcul que stocker des grandes chaines de caractères et c'est pour cette raison que nous avons fait une nouvelle version de la fonction branch and bound ci dessous. \n",
    "\n",
    "Afin de mieux comprendre notre facon d'indexer les noeuds, nous présentons un exemple succinct. \n",
    "\n",
    "![Arbre de branchement](img/EX1.png)\n",
    "\n",
    "* Le noeud racine sera __P__\n",
    "* Le noeud __P(1)__ signifie que la solution liée à ce noeud place l'usinage de la pièce 1 en dernier.\n",
    "* Le noeud __P(2,1)__ signifie que la solution liée à ce noeud place l'usinage de la pièce 1 en dernier, puis celui de la pièce 2 en avant dernier.\n",
    "\n",
    "Pour des exmples de taille plus conséquente (+ de taches), par exemple 5, certains des noeuds seront instanciés par __P(*4 chiffres séparés par des virgules*)__."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Branch and bound redéfini\n",
    "\n",
    "Nous utilisons ces nouvelles indexations pour mettre à jour la fonction branch and bound. \n",
    "\n",
    "Notons aussi que nous ajoutons quelques élements de code permettant de trouver la branche de l'arbre qui aura donné lieu à la meilleure solution. Pour cela, nous localisons le meilleur noeud, i.e le noeud qui :\n",
    "* est tel que la borne duale du noeud est inférieure ou égale à la borne primale du problème                **(1)**\n",
    "* est tel que la borne duale du noeud est plus grande que n'importe quel borne duale des noeuds vérifiant (1).\n",
    "\n",
    "Nous remontons ensuite (en utilisant les parents) afin d'obtenir la branche de l'arbre donnant lieu à la meilleure solution. Cela nous sera utile lors de la visualisation de l'arbre de branchement.\n",
    "\n",
    "Les éléments retournés par la fonction sont également ajustés. Nous ajoutons le retour de : \n",
    "* __branch__ : la branche donnant lieu à la solution optimale du problème\n",
    "* __Tree__: l'arbre correspondant au problème"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def arborescence_display(node,Tree):\n",
    "    branch = []\n",
    "    while node.parent != None:\n",
    "        branch.append(node.idx)\n",
    "        node = node.parent\n",
    "    branch.append(node.idx)\n",
    "    \n",
    "    return branch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def branch_and_bound_display(p,d,w,byLast,getPrimal,getDual,exploration):\n",
    "    \n",
    "    Tasks = list(range(len(p)))\n",
    "    Tree = [] # list of all nodes created (a list of Node objects)\n",
    "    Queue = [] # list of nodes to process (a list of integers with the index of nodes to process in the Tree)\n",
    "    LB = 0 # we set the lower bound to a sufficiently small number \n",
    "    \n",
    "    UB,currentOrder = getPrimal(p,d,w)\n",
    "\n",
    "    bestNode = Node(\"\",-1, [], 0 ,[])\n",
    "    \n",
    "    root = Node(\"P\",None, [], 0, []) # at the root node no variables are fixed \n",
    "    Tree.append(root) # start the tree with the root node\n",
    "    Queue.append(0) # start the list of nodes to process with the root node\n",
    "    \n",
    "    #continue processing nodes until the queue is empty\n",
    "    \n",
    "    nbIterations = 0\n",
    "    \n",
    "    while Queue!=[]:\n",
    "        \n",
    "        # process the first node in the queue\n",
    "\n",
    "        currentIndex = exploration(Queue,None,None)\n",
    "        currentNode = Tree[currentIndex]\n",
    "        nbIterations +=1\n",
    "        \n",
    "        if currentNode.lb < UB:\n",
    "            \n",
    "            if len(currentNode.visited) == len(p):\n",
    "                order = currentNode.visited.copy()\n",
    "                if byLast:\n",
    "                    order.reverse()\n",
    "                solutionCost = computeCost(p,d,w,order)\n",
    "                if solutionCost <= UB:\n",
    "                    UB = solutionCost\n",
    "                    currentOrder = order\n",
    "                    bestNode = currentNode\n",
    "            else:\n",
    "                for nextTask in (set(Tasks)-set(currentNode.visited)):\n",
    "                    nextVisited = currentNode.visited.copy()\n",
    "                    nextVisited.append(nextTask)\n",
    "                    nextLb = getDual(p,d,w, nextVisited)\n",
    "\n",
    "                    newNode = Node(\"\",currentNode, [], nextLb,nextVisited)\n",
    "                    #Creating good labels for each node :\n",
    "                    if currentNode.visited!=[]:\n",
    "                        newNode.idx=\"P(\"\n",
    "                        for i in range(len(nextVisited)-1):\n",
    "                            newNode.idx+=str(nextVisited[i])+\",\"\n",
    "                        newNode.idx+=str(nextVisited[len(nextVisited)-1])+\")\"\n",
    "                    else:\n",
    "                        newNode.idx=\"P(\"+str(nextTask)+\")\"\n",
    "                        \n",
    "                    Tree.append(newNode)\n",
    "                    Queue.append(len(Tree)-1)\n",
    "                    currentNode.children.append(newNode)\n",
    "            \n",
    "        # When the processing of the node is completed remove the node from the queue     \n",
    "        Queue.remove(currentIndex)\n",
    "\n",
    "    # return the  solution\n",
    "    \n",
    "    if 2*(UB-bestNode.lb)<=0.0001:\n",
    "        branch = arborescence_display(bestNode,Tree)\n",
    "    else:\n",
    "        bestLB = -math.inf\n",
    "        for node in Tree:\n",
    "            if node.lb > bestLB and node.lb <=UB:\n",
    "                bestLB = node.lb\n",
    "                bestNode=node\n",
    "        branch = arborescence_display(bestNode,Tree)\n",
    "\n",
    "    return UB, currentOrder, nbIterations, branch, Tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fonction d'affichage de l'arbre\n",
    "\n",
    "Nous implémentons à présent une fonction d'affichage de l'arbre. Cette méthode utilise différents élements de packages spécifiques (importés au préalable, et non utilisés jusqu'alors). Nous utilisons :\n",
    "* __networkx__ afin de crééer une structure d'arbre facile d'utilisation et facile à représenter.\n",
    "* __pydot__ qui permettra de visualiser simplement l'abre de branchement.\n",
    "\n",
    "La méthode consiste principalement à reconstitué l'arbre de branchement à partir de la liste de Noeud __Tree__. De l'afficher de manière adequat, et de mettre en évidence la branche donnant lieu à la solution optimale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def displayTree(Tree,currentOrder,branch,byLast):\n",
    "    \n",
    "    #Utilisation de networkx : création d'un graphe a partir de la liste Tree\n",
    "    index=['P']\n",
    "    parent=['P']\n",
    "    for node in Tree:\n",
    "        if(node.idx!=\"P\"):\n",
    "            index.append(node.idx)\n",
    "            parent.append((node.parent).idx)\n",
    "    graphe=dict(zip(index,parent))\n",
    "    \n",
    "    graph=nx.Graph()\n",
    "    graph.add_nodes_from(index)\n",
    "    for idx in graphe.keys():\n",
    "        graph.add_edge(idx,graphe[idx])\n",
    "        \n",
    "    if(byLast==True):\n",
    "        branch.reverse()\n",
    "\n",
    "    bestPath= set(zip(branch[:-1], branch[1:]))\n",
    "    \n",
    "    #Gestion des couleurs :\n",
    "    edge_colors=dict()\n",
    "    for edge in graph.edges():\n",
    "        if edge in bestPath:\n",
    "            edge_colors[edge]='red'\n",
    "        else:\n",
    "            edge_colors[edge]='lightblue'\n",
    "            \n",
    "    node_colors=dict()\n",
    "    for node in graph.nodes():\n",
    "        if node in branch:\n",
    "            node_colors[node]='red'\n",
    "        else:\n",
    "            node_colors[node]=\"lightblue\"\n",
    "            \n",
    "    #Affichage : \n",
    "    plt.figure(figsize=(12,8))\n",
    "    pos = graphviz_layout(graph, prog=\"dot\")\n",
    "    nx.draw(graph, pos,with_labels=True,node_size=1200,node_color=node_colors.values(),edge_color=edge_colors.values())\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test de la fonction d'affichage\n",
    "\n",
    "Nous effectuons le test de notre fonction d'affichage. Pour cela, nous prenons différents problèmes. Dans un premier temps, nous utiliserons l'exemple de TD (*EX7 TD Branch and bound*). Nous comparons ensuite l'abre de branchement affiché à celui obtenu lors de la résolution du branch and bound en TD."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Textbook Example\n",
    "pj = [12,8,15,9]\n",
    "dj = [16,26,25,27]\n",
    "wj = [4,5,3,5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "solution,order,nbIterations,branch,Tree=branch_and_bound_display(pj,dj,wj, True, getPrimal_2, getDual_2,exploration_0)\n",
    "displayTree(Tree,order,branch,True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Commentaires :__ nous voyons ici les noeuds ayant donné lieu à des élagages et la branche ayant donné lieu à la solution optimale. Les noeuds P(0), P(1), et P(3) on donné lieu à des élagages. Les noeuds P(2,0) et P(2,1) également. Le noeud P(2,3) a quant a lui permis de trouver la solution optimale puisque sa borne duale vaut la borne primale du problème (67)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Notons que cette visualisation n'a de sens que si le nombre d'objets à usiner n'est pas trop élevé. Un nombre élevé d'objets donnera souvent lieu à un nombre d'itérations élevées et donc l'arbre de branchement sera gigantesque (et illisible).*\n",
    "\n",
    "On teste de nouveau la visualisation sur des instances générées aléatoirement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p :  [14  5  7 11]\n",
      "d :  [19 16 22 16]\n",
      "w :  [4 2 2 2]\n",
      "Final Results: 18 Iterations\n",
      "Order : [1, 0, 2, 3] | Cost : 50\n",
      "['P(3,2,0,1)', 'P(3,2,0)', 'P(3,2)', 'P(3)', 'P']\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p,d,w = genInstancies(4,.5)\n",
    "print(\"p : \", p)\n",
    "print(\"d : \", d)\n",
    "print(\"w : \", w)\n",
    "solution, order, nbIterations,branch,Tree = branch_and_bound_display(p,d,w, True, getPrimal_2, getDual_2,exploration_1)\n",
    "print(\"Final Results: {} Iterations\".format(nbIterations))\n",
    "print(\"Order : {} | Cost : {}\".format(order,computeCost(p,d,w,order)))\n",
    "print(branch)\n",
    "displayTree(Tree,order,branch,True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p :  [11 11 12  1  8  4]\n",
      "d :  [12  5 28 20  7 42]\n",
      "w :  [1 2 4 4 1 3]\n",
      "Final Results: 145 Iterations\n",
      "Order : [1, 3, 2, 4, 5, 0] | Cost : 72\n",
      "['P(0,5,4,2,3,1)', 'P(0,5,4,2,3)', 'P(0,5,4,2)', 'P(0,5,4)', 'P(0,5)', 'P(0)', 'P']\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p,d,w = genInstancies(6,.5)\n",
    "print(\"p : \", p)\n",
    "print(\"d : \", d)\n",
    "print(\"w : \", w)\n",
    "solution, order, nbIterations,branch,Tree = branch_and_bound_display(p,d,w, True, getPrimal_2, getDual_2,exploration_1)\n",
    "print(\"Final Results: {} Iterations\".format(nbIterations))\n",
    "print(\"Order : {} | Cost : {}\".format(order,computeCost(p,d,w,order)))\n",
    "print(branch)\n",
    "displayTree(Tree,order,branch,True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p :  [ 5  3 10  6 14 12  6 11]\n",
      "d :  [29 13 39 17 40 15 14 58]\n",
      "w :  [3 4 1 4 1 1 4 1]\n",
      "Final Results: 111 Iterations\n",
      "Order : [1, 6, 3, 0, 5, 2, 4, 7] | Cost : 45\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p,d,w = genInstancies(8,.5)\n",
    "print(\"p : \", p)\n",
    "print(\"d : \", d)\n",
    "print(\"w : \", w)\n",
    "solution, order, nbIterations,branch,Tree = branch_and_bound_display(p,d,w, True, getPrimal_2, getDual_2,exploration_1)\n",
    "print(\"Final Results: {} Iterations\".format(nbIterations))\n",
    "print(\"Order : {} | Cost : {}\".format(order,computeCost(p,d,w,order)))\n",
    "#print(branch)\n",
    "displayTree(Tree,order,branch,True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pour aller plus loin"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous allons maintenant considérer une extension de notre problème dans lequel l'atelier dispose de plusieurs machines : \n",
    "\n",
    "Nous ne changeons pas la structure du code, nous nous appuierons toujours sur la classe Node, les fonctions _getPrimal_, _getDual_, _exploration_ et sur la fonction _Branch and Bound_.\n",
    "\n",
    "\n",
    "Le changement majeur est le changement de format de l'attribut _visited_ de chacun de nos noeuds. En effet, auparavant, cet attribut était une liste stockant les tâches déja réalisées (visted = [0,4,7] <=> les tâches 0,4,7 sont éffectuées, que ce soit en partant de la fin ou du début). \n",
    "Donéranavant, l'attribut _visited_ sera une liste de listes, où la liste à l'index 0 correspondra aux tâches réalisées dans l'odre par la machine 0, etc... \n",
    "\n",
    "\n",
    "visited : [0,2,3] -> [[0,2],[3]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Node:\n",
    "    \n",
    "    def __init__(self,idx, parent, children, lb, visited):\n",
    "        self.idx = idx\n",
    "        self.parent = parent\n",
    "        self.children = children\n",
    "        self.lb = lb\n",
    "        self.visited = visited\n",
    "        \n",
    "    def __str__(self):\n",
    "        return (\"Parent : \" +str(self.parent)+\n",
    "                \"\\n Children : \"+str(self.children) + \n",
    "                \"\\n lb : \"+str(self.lb)+ \n",
    "                \"\\n visited : \"+str(self.visited))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Une des premières fonctions à redéfinir pour ce nouveau problème était la fonction _computeCost_ qui nous permettra de calculer le coût d'une solution proposée. Le fonctionnement est le même qu'aparavant, on prend juste soin d'itérer sur chaque machine en pensant à réinitialiser le temps à 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "def computeCost(p,d,w,orders,n):\n",
    "    cost=0\n",
    "    for order in orders:\n",
    "        t=0\n",
    "        for idx in order:\n",
    "            t += p[idx]\n",
    "            cost += max(0,(t-d[idx])*w[idx])\n",
    "    return cost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Suite à ça, nous définissons une nouvelle fonction _getPrimal_. La manière la plus simple de calculer une borne primale que nous ayons trouvé est de ranger les tâches par odre croissant de date limite et de les affecter une par une à chacune des machines. À défaut d'être optimale voir même performante, cette méthode nous a néanmoins permis de vérifier le bon fonctionnement de notre algorithme."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getPrimal(p,d,w,n):\n",
    "    sortedTasks = np.argsort(d)\n",
    "    orders = [[] for _ in range(n)]\n",
    "    for i in range(len(sortedTasks)):\n",
    "        orders[i%n].append(sortedTasks[i])\n",
    "    cost = computeCost(p,d,w,orders,n)\n",
    "    return(cost,orders)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous définissons ensuite notre fonction _getDual_ qui sera exécutée à chaque création d'un nouveau noeud de l'arbre pour en calculer sa borne primale. Si on a fixé l'ordre de $k$ pièces on calcule les pénalités de retard de ces $k$ pièces pour chaque machine et on ajoute les pénalités des autres pièces en supposant qu'elles sont en $(k_i+1)$ème position, et en prenant la pénalité de retard minimum parmis toutes les machines.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getDual(p,d,w,visited,n):\n",
    "    dual = 0\n",
    "    notVisited=list(range(len(p)))\n",
    "    time = [0]*n \n",
    "    for k in range(n):\n",
    "        machine = visited[k]\n",
    "        for i in machine:\n",
    "            retard = (time[k] + p[i] -d[i])\n",
    "            dual += retard*w[i]*(retard>0)\n",
    "            time[k] += p[i]\n",
    "            notVisited.remove(i)\n",
    "    for j in notVisited:\n",
    "        retard=min([(p[j]+time[k])-d[j] for k in range(n)])\n",
    "        dual+=retard*w[j]*(retard>0)\n",
    "    return dual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getDual_2(p,d,w,visited,n):\n",
    "    dual = 0\n",
    "    notVisited=list(range(len(p)))\n",
    "    time = [0]*n \n",
    "    for k in range(n):\n",
    "        machine = visited[k]\n",
    "        for i in machine:\n",
    "            retard = (time[k] + p[i] -d[i])\n",
    "            dual += retard*w[i]*(retard>0)\n",
    "            time[k] += p[i]\n",
    "            notVisited.remove(i)\n",
    "    return dual"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On conserve les mêmes méthodes d'exploration que précédemment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "def exploration_0(Queue,Tree,UB):\n",
    "    return(Queue[0])\n",
    "\n",
    "def exploration_1(Queue,Tree,UB):\n",
    "    return(Queue[-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finalement, nous modifions notre algorithme _Branch and Bound_ pour correspondre à ce problème. Les éléments que nous avons changé sont : \n",
    "\n",
    "__Entrée :__ \n",
    "\n",
    "* __n :__ Ajout d'un nouveau paramètre pour indiquer le nombre de machines à disposition.\n",
    "\n",
    "__Exécution :__\n",
    "\n",
    "* __getPrimal :__ Utilisation de notre nouvelle fonction getPrimal pour n machines\n",
    "\n",
    "* __root node :__ Changement de format de root.visited.\n",
    "\n",
    "* __Vérification tâches éffectuées :__ On ne peut plus simplement regarder la longueur/composition de noeud.visited mais on doit additionner la longueur de chaque liste/ itérer sur les listes en plusieurs lignes.\n",
    "\n",
    "* __computeCost :__ Utilisation de notre nouvelle fonction computeCost pour n machines\n",
    "\n",
    "* __getDual :__ Utilisation de notre nouvelle fonction getDual pour n machines\n",
    "\n",
    "* __nextVisited :__ Une utilisation d'un simple copy.copy() n'était plus suffisante pour une liste de listes. L'ajout d'un noeud à nextVisited l'ajoutait aussi à currentNode. Nous avons domc remplacé par un deepcopy()\n",
    "\n",
    "* __Création nouveau noeud :__ On crée un nouveau noeud par tâche non visitée pour chaque machine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "def branch_and_bound(p,d,w,byLast,getPrimal,getDual,exploration,n):\n",
    "    \n",
    "    Tasks = list(range(len(p)))\n",
    "    Tree = [] \n",
    "    Queue = [] \n",
    "    UB = 10000000 \n",
    "    LB = 0 \n",
    "    \n",
    "    UB,currentOrder = getPrimal(p,d,w,n) #New primal bound computation\n",
    "    #print(\"Primal Bound : {} | Order : {}\".format(UB,currentOrder))\n",
    "    bestNode = Node(-1,-1, [], 0 ,[])\n",
    "    \n",
    "    #NEW\n",
    "    #We changed the root node visited attribute to match the problem\n",
    "    root = Node(0,None, [], 0, [[] for _ in range(n)]) \n",
    "    Tree.append(root) \n",
    "    Queue.append(0) \n",
    "    \n",
    "    nbIterations = 0\n",
    "    \n",
    "    while len(Queue)!=0 :\n",
    "        \n",
    "        currentIndex = exploration(Queue,Tree,UB)\n",
    "        currentNode = Tree[currentIndex]\n",
    "        nbIterations += 1\n",
    "        \n",
    "        if currentNode.lb < UB:\n",
    "            \n",
    "            #NEW\n",
    "            #We needed to change the conition to know whether we visited all the tasks or not\n",
    "            if sum([len(visited) for visited in currentNode.visited]) >= len(p):\n",
    "                orders = copy.copy(currentNode.visited)\n",
    "                if byLast:\n",
    "                    for order in orders:\n",
    "                        order.reverse()\n",
    "                #NEW\n",
    "                #ComputeCost changed\n",
    "                solutionCost = computeCost(p,d,w,orders,n)\n",
    "                if solutionCost <= UB:\n",
    "                    UB = solutionCost\n",
    "                    currentOrder = orders\n",
    "                    bestNode = currentNode\n",
    "            else:\n",
    "                \n",
    "                #NEW\n",
    "                #Listing all the visited tasks is now too difficult to be nicely written in a single line\n",
    "                allVisitedTasks = []\n",
    "                visited_copy = copy.copy(currentNode.visited)\n",
    "                for machine in visited_copy:\n",
    "                    for task in machine:\n",
    "                        allVisitedTasks.append(task)\n",
    "                \n",
    "                #print(currentNode.visited)\n",
    "                \n",
    "                #NEW\n",
    "                #Iterate over all the machines\n",
    "                for k in range(n):\n",
    "                    for nextTask in (set(Tasks)-set(allVisitedTasks)):\n",
    "                        #NEW\n",
    "                        #visited is now a list of lists so we need to deepcopy it\n",
    "                        nextVisited = copy.deepcopy(currentNode.visited)\n",
    "                        #machine by machine\n",
    "                        nextVisited[k].append(nextTask)\n",
    "                        nextLb = getDual(p,d,w, nextVisited,n) #New dual bound computation\n",
    "\n",
    "                        newNode = Node(len(Tree),currentIndex, [], nextLb,nextVisited)\n",
    "                        Tree.append(newNode)\n",
    "                        Queue.append(len(Tree)-1)\n",
    "                        currentNode.children.append(len(Tree)-1)\n",
    "               \n",
    "        Queue.remove(currentIndex)\n",
    "\n",
    "    \n",
    "    if (UB-bestNode.lb)<=0.0001:\n",
    "        branch = arborescence(bestNode,Tree)\n",
    "    \n",
    "    else:\n",
    "        bestLB = -math.inf\n",
    "        for node in Tree:\n",
    "            if node.lb > bestLB and node.lb <=UB:\n",
    "                bestLB = node.lb\n",
    "                bestNode=node\n",
    "        branch = arborescence(bestNode,Tree)\n",
    "\n",
    "    return UB, currentOrder, nbIterations, branch, Tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test de nos fonctions : "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dans un premier temps nous définissons un exemple assez simple qui nous permettra de vérifier le bon fonctionnement de notre algortihme. Nous choisissons de n'utiliser que deux machines et de ne donner que 6 pièces à produire. Toutes les pièces seront usinables en temps sauf une, la pièce 6, qui engendrera un pénalité. \n",
    "\n",
    "* __Temps de produiction :__ p = [2,3,4,2,3,6]\n",
    "\n",
    "\n",
    "* __Dates limite :__ d = [2,5,9,2,5,7]\n",
    "\n",
    "\n",
    "* __Poids de pénalité :__ w = [1,1,1,1,1,1]\n",
    "\n",
    "\n",
    "Si notre algorithme fonctionne correctement, il devrait renvoyer une solution de 4 illustrée sur le schéma ci-dessous. \n",
    "\n",
    "<img src=\"img/2machinestoyexample.png\"\n",
    "     style=\"\" />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = [2,3,4,2,3,6]\n",
    "d = [2,5,9,2,5,7]\n",
    "w = [1,1,1,1,1,1]\n",
    "\n",
    "orders = [[0,1,2],[3,4,5]]\n",
    "computeCost(p,d,w,orders,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Solution : 4\n",
      "Ordre :  [[0, 1, 5], [3, 4, 2]]\n"
     ]
    }
   ],
   "source": [
    "sol, order, _ ,_ ,_= branch_and_bound(p,d,w,True,getPrimal,getDual,exploration_1,2)\n",
    "print(\"Solution :\", sol)\n",
    "print(\"Ordre : \", order)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Conclusion :__ Notre algorithme nous a renvoyé la bonne réponse sur ce problème particulièrement simple. Nous allons maintenant le tester sur différentes intances aléatoires de notre problème."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Instances aléatoires :__ Comme précédemment nous allons définir une fonction pour générer des instances aléatoires de notre problème. Le code fonctionne de la même manière que notre ancienne fonction, mais nous décidons de diviser les dates limites par le nombre de machines pour être sûr d'avoir un problème non réalisable sans pénalités."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "def genMultiInstances(n,alpha,n_machines):\n",
    "    p=np.random.randint(1,15,n)\n",
    "    d=np.random.randint(3,int(alpha*n*15/n_machines),n)\n",
    "    w=np.random.randint(1,5,n)\n",
    "    return(p,d,w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "______________________\n",
      "p :  [10  3  9  4  1  2]\n",
      "d :  [13 13 14 12 13  7]\n",
      "w :  [1 1 2 2 2 1]\n",
      "Solution : 4 | Ordre : [[5, 0, 4], [3, 1, 2]] | nbIt : 16093\n",
      "Solution : 4 | Ordre : [[5, 0, 4], [3, 1, 2]] | nbIt : 28581\n",
      "______________________\n",
      "p :  [ 1 11  6  7  5 10]\n",
      "d :  [ 7  9 11  8 17  9]\n",
      "w :  [1 1 1 4 3 1]\n",
      "Solution : 33 | Ordre : [[0, 1, 2], [3, 5, 4]] | nbIt : 25413\n",
      "Solution : 33 | Ordre : [[0, 1, 2], [3, 5, 4]] | nbIt : 45477\n",
      "______________________\n",
      "p :  [ 8  9  5 12  9  5]\n",
      "d :  [ 4 13  6  3  3 13]\n",
      "w :  [4 3 4 4 4 3]\n",
      "Solution : 215 | Ordre : [[2, 1, 0], [3, 5, 4]] | nbIt : 32737\n",
      "Solution : 215 | Ordre : [[2, 1, 0], [3, 5, 4]] | nbIt : 62913\n"
     ]
    }
   ],
   "source": [
    "for _ in range(3):\n",
    "    print(\"______________________\")\n",
    "    p,d,w = genMultiInstances(6,.4,2)\n",
    "    print(\"p : \", p)\n",
    "    print(\"d : \", d)\n",
    "    print(\"w : \", w)\n",
    "    sol, order, nbIt ,_ ,_= branch_and_bound(p,d,w,True,getPrimal,getDual,exploration_1,2)\n",
    "    print(\"Solution : {} | Ordre : {} | nbIt : {}\".format(sol,order,nbIt))\n",
    "    sol, order, nbIt ,_ ,_= branch_and_bound(p,d,w,True,getPrimal,getDual_2,exploration_1,2)\n",
    "    print(\"Solution : {} | Ordre : {} | nbIt : {}\".format(sol,order,nbIt))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Résultats :__ On remarque que comme on pouvait s'y attendre, la méthode $getDual\\_2$ est moins performante que $getDual$. À défaut d'avoir une modélisation PLNE ou des exemples avec solution, le fait que les deux éxécutions avec les méthodes _getDual_ et _getdual_2_ donnent le même résultat nous conforte sur le bon fonctionnement de l'algorithme. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Nombre de machines et temps d'exécution :__ \n",
    "\n",
    "Une question que l'on peut naturellement se poser est la question de l'influence du nombre de machines sur le temps d'exécution. Intuitivement plus on aura de machines et plus on devra créer de nouvelles branches à chaque noeud. On s'attend donc à ce que le temps d'exécution grimpe de manière factorielle avec le nombre de machines. \n",
    "\n",
    "Nous proposons ci dessous une comparaison des temps d'exécution de problèmes d'ordonnancement à 1-2-3 machines pour des problèmes de 3 à 7 pièces."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, \"Temps d'exécution en fonction de la taille du problème - alpha:0.4 | N=1 | n_machines=1/2/3\")"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 1 #Nombre d'échantillons\n",
    "n = 6 #Taille maximum du problème\n",
    "alpha = 0.4\n",
    "\n",
    "resultats_BB = [[] for i in range(3,n)]\n",
    "resultats_BB_2 = [[] for i in range(3,n)]\n",
    "resultats_BB_3 = [[] for i in range(3,n)]\n",
    "\n",
    "for _ in range(N):\n",
    "    for i in range(3,n):\n",
    "        \n",
    "        p,d,w = genMultiInstances(i,alpha,1)\n",
    "        #n_machines = 1 \n",
    "        start = time.time()\n",
    "        _, _, _, _, _ = branch_and_bound(p,d,w, True, getPrimal, getDual,exploration_1, 1)\n",
    "        end = time.time()\n",
    "        resultats_BB[i-3].append(end-start)\n",
    "        \n",
    "        p,d,w = genMultiInstances(i,alpha,2)\n",
    "        #n_machines = 2 \n",
    "        start = time.time()\n",
    "        _, _, _, _, _ = branch_and_bound(p,d,w, True, getPrimal, getDual,exploration_1, 2)\n",
    "        end = time.time()\n",
    "        resultats_BB_2[i-3].append(end-start)\n",
    "        \n",
    "        p,d,w = genMultiInstances(i,alpha,3)\n",
    "        #n_machines = 3\n",
    "        start = time.time()\n",
    "        _, _, _, _, _ = branch_and_bound(p,d,w, True, getPrimal, getDual,exploration_1, 3)\n",
    "        end = time.time()\n",
    "        resultats_BB_3[i-3].append(end-start)\n",
    "\n",
    "\n",
    "resultats_BB_3 = [np.mean(res) for res in resultats_BB_3]\n",
    "resultats_BB_3 = np.array(resultats_BB_3)\n",
    "\n",
    "resultats_BB_2 = [np.mean(res) for res in resultats_BB_2]\n",
    "resultats_BB_2 = np.array(resultats_BB_2)\n",
    "\n",
    "resultats_BB = [np.mean(res) for res in resultats_BB]\n",
    "resultats_BB = np.array(resultats_BB)\n",
    "\n",
    "plt.figure(figsize=(10,6))\n",
    "X = list(range(3,n))\n",
    "plt.plot(X,resultats_BB, label=\"1 machines\")\n",
    "plt.plot(X,resultats_BB_2, label=\"2 machines\")\n",
    "plt.plot(X,resultats_BB_3, label=\"3 machines\")\n",
    "plt.xlabel(\"Taille du problème\")\n",
    "plt.xticks(X)\n",
    "plt.legend()\n",
    "plt.ylabel(\"Temps d'exécution\")\n",
    "plt.title(\"Temps d'exécution en fonction de la taille du problème - alpha:{} | N={} | n_machines={}\".format(alpha,N,\"1/2/3\"))\n",
    "#plt.savefig(\"img/1-2-3-machines-8.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Afin de vous éviter un long temps de calcul, voici le résultat obtenu pour : $N=1$, $n=8$, $\\alpha = 0.4$\n",
    "\n",
    "<img src=\"img/1-2-3-machines-8.png\"\n",
    "     style=\"\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Conclusion :__ Comme prévu le temps d'exécution explose lorsque le nombre de machines augmente. \n",
    "\n",
    "Pour un problèmes à 7 pièces, la version à 1 machine se termine en moins d'une seconde, la version à 2 machines en un peu moins de 10s alors que la version à 3 machines prend plus de 2 minutes à s'exécuter.\n",
    "\n",
    "Ces résultats sont à prendre avec des pincettes puisque nos méthodes _getDual_ et _getPrimal_ sont certainement très loin d'être optimales. Cependant, la tendance générale devrait rester identique même avec des meilleures fonctions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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