diff --git a/Projet_algo.ipynb b/Projet_algo.ipynb index 6e6b2a9..8af40fa 100644 --- a/Projet_algo.ipynb +++ b/Projet_algo.ipynb @@ -1,6 +1,7 @@ { "cells": [ { + "attachments": {}, "cell_type": "markdown", "id": "83c2b1e7-b401-4a15-adf0-d43cebf9ad81", "metadata": {}, @@ -9,6 +10,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "id": "0b4f92e5-ac40-4491-983d-890c4f0f6694", "metadata": {}, @@ -17,6 +19,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "id": "5f483b31-1246-4f00-ae39-718ef92ce9eb", "metadata": {}, @@ -25,28 +28,41 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "id": "fc23daaf-9f25-4c5a-bf6c-300a7ed8d623", "metadata": {}, "source": [ - "Notre but est de réaliser un algorithme qui permette de passer par tout les points de livraisons avec un temps optimisé." + "Notre but est de réaliser un algorithme permettant de passer par tous les points de livraisons avec un temps optimisé." ] }, { + "attachments": {}, + "cell_type": "markdown", + "id": "d633beb7-8f26-46d4-9cd9-1d0093e5b5c3", + "metadata": {}, + "source": [ + "## Contraintes choisies :\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "35fc1c3c-d7a9-4423-a948-aa00ab51dbf4", + "metadata": {}, + "source": [ + "Les contraintes que nous avons choisi sont les suivantes:\n", + "- Avoir plusieurs camions disponible simultanément pour effectuer les livraisons.\n", + "- Fenêtre de temps de livraison pour chaque object\n" + ] + }, + { + "attachments": {}, "cell_type": "markdown", "id": "ba356166-604a-4627-ac0f-93b76eb7a22d", "metadata": {}, "source": [ - "## Modélisation du problème" - ] - }, - { - "cell_type": "markdown", - "id": "50a3e56e-3bdc-4f2c-a811-8433ffeb2a34", - "metadata": {}, - "source": [ - "L'objectif de notre algorithme est de calculer et de fournir le meilleur itinéraire possible et le plus optimisé en terme de temps.\n", - "\n" + "## Formulation du problème" ] }, { @@ -55,9 +71,159 @@ "id": "c4d6888b-14e6-4745-880f-0a063ebf7476", "metadata": {}, "source": [ - "Notre problème se modélise sous forme d'un graphe, chaque sommet du graphe va représenter une ville. Le but est de passer par chaque ville pour revenir au point de départ en empruntant le chemin le plus rapide. Chaque arête possède un poids qui représente le temps de trajet d'une ville A à une ville B. \n", + "Soit un graphe $G=(V,E)$, où $V$ est l'ensemble des villes (ou points de livraison) et $E$ est l'ensemble des routes entre les villes. Chaque ville $v∈V$ a une fenêtre de temps de livraison $[a_v,b_v]$, où $a_v$​ est le début de la fenêtre et $b_v$​ est la fin de la fenêtre. Il y a $k$ camions disponibles pour effectuer les livraisons.\n", + "\n", + "Le problème consiste à trouver une tournée pour chaque camion, de manière à ce que toutes les livraisons soient effectuées dans leurs fenêtres de temps respectives et que la date de retour du dernier camion à la base soit minimisée.\n", + "\n", + "\n", + "Le problème que nous avons avec les contraintes ci-dessus est le problème du VRPTW (Vehicule Routing Problem avec la contrainte supplémentaire de fenêtres d'ouverture)." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "6d392f68", + "metadata": {}, + "source": [ + "## Contraintes du problème" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "f95dba22", + "metadata": {}, + "source": [ + "Liste des contraintes du problème:\n", + "\n", + "- Tous les clients doivent être déservis\n", + "- Un client ne peut être servi que par un et un seul véhicule.\n", + "- En quittant un client, un véhicule peut aller que vers un seul autre client.\n", + "- Un véhicule ne peut servir un client que s'il a assez de capacité pour servir le client.\n", + "\n", + "On va donc affecter chaque client à une tournée effectué par un seul véhicule." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "16b3b8a7-4658-4509-a511-7a395654e6f1", + "metadata": {}, + "source": [ + "## Modélisation mathématique" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "b54bfa8c", + "metadata": {}, + "source": [ + "### Ensemble et paramètres:" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "637eb295", + "metadata": {}, + "source": [ + "$V=\\{0,1,2,...,n\\}$ : l'ensemble des villes, où 0 est la base (ou le dépôt) et $1,2,...,n$ sont les villes de livraison.
\n", + "$K=\\{1,2,...,k\\}$ : l'ensemble des camions.
\n", + "$d_{ij}$​ : la distance (ou le temps de trajet) de la ville ii à la ville $j$.
\n", + "$[a_i,b_i]$ : la fenêtre de temps de livraison pour la ville $i$.
\n", + "$M$ : une grande constante." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "1219e4f2", + "metadata": {}, + "source": [ + "### Variables de décision:" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "a6d398fa", + "metadata": {}, + "source": [ + "$x_{ijk}$​ : variable binaire qui vaut 1 si le camion $k$ se déplace de la ville $i$ à la ville $j$, et 0 sinon.
\n", + "$t_{ik}$​ : le moment où le camion $k$ arrive à la ville $i$." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "e635cf14", + "metadata": {}, + "source": [ + "### Fonction objective:" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "56652859", + "metadata": {}, + "source": [ + "Minimiser $Z=max_{⁡k∈K}t_{0k}​$" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "a1465000", + "metadata": {}, + "source": [ + "### Contraintes:\n", + "Chaque ville est visitée une fois et une seule fois : $$\\sum_{⁡k∈K} \\sum_{⁡j∈V} x_{ijk} = 1, ∀i \\in V∖ \\{0\\}$$\n", + "\n", + "Si un camion se déplace de la ville ii à la ville $j$, alors le moment d'arrivée à la ville $j$ doit être plus grand que le moment d'arrivée à la ville $i$ plus le temps de trajet : $$t_{ik}​+d_{ij}​ \\leq t_{jk}​+M(1−x_{ijk}​),∀i,j \\in V,i \\ne j,∀k \\in K$$\n", + "\n", + "Les fenêtres de temps de livraison doivent être respectées : $$a_i​ \\leq t_{ik}​ \\leq b_i​, ∀i \\in V∖ \\{0 \\},∀k \\in K$$\n", + "\n", + "Les contraintes de flux pour garantir que si un camion entre dans une ville, il doit également en sortir :\n", + "$$i \\in V,i \\ne j∑​xijk​=i∈V,i \\ne j∑​xjik​=yjk​,∀j∈V,∀k∈K$$" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "286caeb7-26cb-486a-b5f0-94a3f24811fd", + "metadata": {}, + "source": [ + "Nous allons représenter notre problème par un graphe
\n", + "$G=(V,E)$\n", + "\n", + "$V$ représente les sommets du graphe qui correspondent aux clients\n", + "\n", + "$E$ représente les arcs entre deux clients $i,j \\in V$\n", + "\n", + "\n", + "\n", + "On a un ensemble $C=\\{1,2,...,n_c\\}$ de clients qui doivent obtenir leur livraison qui provient du dépot.\n", + "\n", + "\n", + "L'ensemble des emplacements des clients est défini comme :\n", + "$V = C \\cup \\{0, n_c+1\\} $\n", + "\n", + "$0$ et $n_c+1$ représente le dépot, puisqu'on doit revenir au point de départ le dépots est le dernier client plus un. \n", + "\n", + "\n", + "On a un ensemble $V=\\{1,2,...,n_v\\}$ de véhicule disponible et chaque véhicule possède une capacité $Q$.\n", + "\n", + "\n", + "\n", + "# Variables de décisions: \n", + "\n", + "\n", + "\n", + "\n", + "\n", "\n", - "Dans notre cas le problème peut s'assimiler à un problème du voyageur de commerce dynamique avec un graphe incomplet. On cherche le chemin le plus court en passant par toute les villes en tenant compte des variations des poids des arêtes au cours du temps. Il est possible de passer plusieurs fois par le même sommet si cela permet d'avoir le chemin qui prends le moins de temps. \n", "\n", "\n", "\n", @@ -70,95 +236,83 @@ ] }, { + "attachments": {}, "cell_type": "markdown", - "id": "5290881b", - "metadata": {}, - "source": [] - }, - { - "cell_type": "markdown", - "id": "3346ae96-1c36-4e9d-9480-3ab4657c7d68", + "id": "5a3512bc-02da-4e2d-b8f6-65e70c3fe686", "metadata": {}, "source": [ - "## Objectifs\n" + "Variables de décision:\n", + "- Un ensemble de k véhicule\n" ] }, { + "attachments": {}, "cell_type": "markdown", - "id": "dbd49849-988e-4915-8e28-e00085f4b97e", + "id": "7670fdf4-884c-4352-83fa-eed0c8b50074", "metadata": {}, "source": [ - "Minimiser les temps de trajets, en optimisant les itinéraires." - ] - }, - { - "cell_type": "markdown", - "id": "d633beb7-8f26-46d4-9cd9-1d0093e5b5c3", - "metadata": {}, - "source": [ - "## Contrainte :\n" - ] - }, - { - "cell_type": "markdown", - "id": "35fc1c3c-d7a9-4423-a948-aa00ab51dbf4", - "metadata": {}, - "source": [ - "Contrainte de temps entre les sommets. Les temps de parcours d'une arête peut varié au cours du temps en fonction de l'heure. \n" + "# Initialisation de la matrice" ] }, { "cell_type": "code", - "execution_count": 33, - "id": "337db031-2e38-4b6d-9d57-74e277d7f033", - "metadata": { - "scrolled": true - }, + "execution_count": 46, + "id": "c177ac22", + "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "array([[0, 0, 0, 0, 0, 0, 1, 0, 0, 1],\n", - " [0, 0, 0, 0, 1, 0, 0, 0, 0, 1],\n", - " [0, 0, 0, 0, 0, 1, 0, 1, 0, 0],\n", - " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n", - " [0, 1, 0, 0, 0, 0, 0, 0, 1, 0],\n", - " [0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n", - " [1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n", - " [0, 0, 1, 0, 0, 0, 0, 0, 0, 1],\n", - " [0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n", - " [1, 1, 0, 0, 0, 0, 0, 1, 0, 0]])" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import numpy as np\n", - "\n", - "mat = np.random.random((10, 10))\n", - "mat = mat + mat.T\n", - "adja = (mat >= 1.4).astype(int)\n", - "for i in range(adja.shape[0]):\n", - " adja[i ,i] = 0\n", - "adja" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "ca201fb6-740f-4f26-b7d0-d48ae037e8ef", - "metadata": { - "scrolled": true - }, - "outputs": [ + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0. 2. 2. 2. 3. 1. 3. 2. 3. 3. 1. 3. 2. 1. 2. 2. 3. 2. 2. 2. 1.]\n", + " [2. 0. 3. 3. 2. 3. 3. 3. 3. 3. 1. 2. 2. 2. 1. 1. 2. 2. 3. 3. 2.]\n", + " [2. 3. 0. 2. 3. 3. 3. 1. 2. 1. 2. 1. 2. 1. 2. 2. 2. 2. 2. 2. 1.]\n", + " [2. 3. 2. 0. 2. 1. 2. 3. 2. 2. 3. 2. 1. 1. 2. 2. 2. 1. 1. 2. 2.]\n", + " [3. 2. 3. 2. 0. 2. 1. 4. 3. 2. 3. 2. 1. 2. 1. 3. 3. 2. 3. 2. 3.]\n", + " [1. 3. 3. 1. 2. 0. 3. 3. 3. 2. 2. 3. 1. 2. 2. 3. 3. 2. 2. 3. 2.]\n", + " [3. 3. 3. 2. 1. 3. 0. 3. 2. 3. 3. 3. 2. 2. 2. 2. 2. 1. 3. 1. 2.]\n", + " [2. 3. 1. 3. 4. 3. 3. 0. 3. 2. 2. 2. 3. 2. 3. 3. 3. 2. 3. 3. 1.]\n", + " [3. 3. 2. 2. 3. 3. 2. 3. 0. 1. 3. 3. 2. 2. 3. 2. 2. 1. 3. 1. 2.]\n", + " [3. 3. 1. 2. 2. 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", + "image/png": 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", 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" ] }, "metadata": {}, @@ -166,30 +320,674 @@ } ], "source": [ + "\n", + "import random\n", + "import numpy as np\n", "import networkx as nx\n", - "import scipy\n", "import matplotlib.pyplot as plt\n", - "fix, ax = plt.subplots(1, 1,figsize=(4,4))\n", - "G = nx.DiGraph(np.array(adja))\n", - "layout = nx.kamada_kawai_layout(G)\n", - "nx.draw(G, with_labels=True, ax=ax, pos=layout)" + "\n", + "def generate_graph(num_nodes):\n", + " G = nx.Graph()\n", + " G.add_nodes_from(range(num_nodes + 1))\n", + " \n", + " for node in G.nodes():\n", + " connected_nodes = sorted(set(G.nodes()) - {node})\n", + " if len(connected_nodes) < 2:\n", + " continue\n", + " distance1 = random.randint(1, 10)\n", + " distance2 = random.randint(1, 10)\n", + " random_nodes = random.sample(connected_nodes, 2)\n", + " G.add_edges_from([(node, random_nodes[0], {'distance': distance1}),\n", + " (node, random_nodes[1], {'distance': distance2})])\n", + "\n", + " while not nx.is_connected(G):\n", + " node1, node2 = random.sample(G.nodes(), 2)\n", + " if not G.has_edge(node1, node2):\n", + " distance = random.randint(1, 10)\n", + " G.add_edge(node1, node2, distance=distance)\n", + "\n", + " # Assigner des positions aléatoires aux nœuds\n", + " pos = {node: (random.uniform(0, 10), random.uniform(0, 10)) for node in G.nodes()}\n", + " nx.set_node_attributes(G, pos, 'pos')\n", + "\n", + " return G\n", + "\n", + "graph = generate_graph(20)\n", + "A = nx.adjacency_matrix(graph).todense()\n", + "\n", + "def generate_distance_matrix(graph):\n", + " distance_matrix = dict(nx.floyd_warshall(graph))\n", + " num_nodes = graph.number_of_nodes()\n", + " distance_array = np.zeros((num_nodes, num_nodes))\n", + " for i, row in distance_matrix.items():\n", + " for j, distance in row.items():\n", + " \n", + " distance_array[i][j] = distance\n", + " return distance_array\n", + "\n", + "\n", + "\n", + "# Générer la matrice de distances\n", + "distance_matrix = generate_distance_matrix(graph)\n", + "\n", + "# Afficher la matrice de distances\n", + "print(distance_matrix)\n", + "\n", + "\n", + "print(A)\n", + "\n", + "pos = nx.get_node_attributes(graph, 'pos')\n", + "# Dessiner le graphe\n", + "nx.draw(graph, with_labels=True)\n", + "plt.show()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "9aac4453", + "metadata": {}, + "source": [ + "# ACO appliqué sur notre graphe\n" ] }, { "cell_type": "code", - "execution_count": null, - "id": "5baaba8c-1e8f-4da6-9504-1128644391dd", + "execution_count": 47, + "id": "301fb5e8-9804-4592-9193-5915f1b9ecad", "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meilleure solution : [0, 10, 1, 15, 13, 14, 4, 6, 19, 8, 9, 12, 5, 3, 18, 11, 16, 17, 20, 2, 7, 0]\n", + "Distance totale : 22.0\n" + ] + } + ], + "source": [ + "import time\n", + "\n", + "# Fonction d'évaluation de la qualité d'une solution (ici, la distance totale)\n", + "def evaluate_solution(solution, distances):\n", + " total_distance = 0\n", + " num_nodes = len(solution)\n", + "\n", + " for i in range(num_nodes - 1):\n", + " current_node = solution[i]\n", + " next_node = solution[i + 1]\n", + " total_distance += distances[current_node][next_node]\n", + "\n", + " # Ajouter la distance de retour au dépôt\n", + " total_distance += distances[solution[-1]][solution[0]]\n", + "\n", + " return total_distance\n", + "\n", + "\n", + "# Algorithme ACO\n", + "def ant_colony_optimization(distances, num_ants, num_iterations, evaporation_rate, alpha, beta):\n", + " num_nodes = len(distances)\n", + " \n", + " pheromone = np.ones((num_nodes, num_nodes)) # Matrice de phéromones initiale\n", + " best_solution = None\n", + " best_distance = float('inf')\n", + "\n", + " start_time = time.time()\n", + "\n", + "\n", + " for iteration in range(num_iterations):\n", + " # Construction de solutions par les fourmis\n", + " solutions = []\n", + "\n", + " for ant in range(num_ants):\n", + " visited = set()\n", + " current_node = random.randint(0, num_nodes - 1)\n", + " visited.add(current_node)\n", + " solution = [current_node]\n", + "\n", + " while len(visited) < num_nodes:\n", + " next_node = None\n", + " probabilities = []\n", + "\n", + " # Calcul des probabilités de choisir chaque prochain nœud\n", + " for node in range(num_nodes):\n", + " if node not in visited:\n", + " pheromone_value = pheromone[current_node][node]\n", + " distance_value = distances[current_node][node]\n", + " probability = (pheromone_value ** alpha) * ((1 / distance_value) ** beta)\n", + " probabilities.append((node, probability))\n", + "\n", + " total_probability = sum(prob for _, prob in probabilities)\n", + " probabilities = [(node, prob / total_probability) for node, prob in probabilities]\n", + "\n", + " # Choix du prochain nœud basé sur les probabilités\n", + " roulette_wheel = random.random()\n", + " probability_sum = 0\n", + "\n", + " for node, probability in probabilities:\n", + " probability_sum += probability\n", + " if probability_sum >= roulette_wheel:\n", + " next_node = node\n", + " break\n", + "\n", + " visited.add(next_node)\n", + " solution.append(next_node)\n", + " current_node = next_node\n", + "\n", + "\n", + " # Ajouter le retour au dépôt central à la fin du trajet\n", + " solution.append(0)\n", + "\n", + " solutions.append(solution)\n", + "\n", + " # Évaluation des solutions et mise à jour de la meilleure solution\n", + " for solution in solutions:\n", + " distance = evaluate_solution(solution, distances)\n", + " if distance < best_distance:\n", + " best_solution = solution\n", + " best_distance = distance\n", + "\n", + " # Mise à jour des phéromones\n", + " pheromone *= evaporation_rate # Évaporation des phéromones existantes\n", + "\n", + " for solution in solutions:\n", + " delta_pheromone = 1 / evaluate_solution(solution, distances)\n", + " for i in range(num_nodes - 1):\n", + " node1 = solution[i]\n", + " node2 = solution[i + 1]\n", + " pheromone[node1][node2] += delta_pheromone\n", + " pheromone[node2][node1] += delta_pheromone\n", + "\n", + " return best_solution, best_distance\n", + "\n", + "\n", + " \n", + "num_ants = 10\n", + "num_iterations = 50\n", + "evaporation_rate = 0.5\n", + "alpha = 1\n", + "beta = 1\n", + "\n", + "\n", + "best_solution, best_distance = ant_colony_optimization(distance_matrix, num_ants, num_iterations, evaporation_rate, alpha, beta)\n", + "\n", + "\n", + "\n", + "print(\"Meilleure solution :\", best_solution)\n", + "print(\"Distance totale :\", best_distance)\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "7e878834", + "metadata": {}, + "source": [ + "# Tracé du chemin" + ] }, { "cell_type": "code", - "execution_count": null, - "id": "91a312bd-26c5-424e-aa54-c08328831308", + "execution_count": 96, + "id": "2a019d78", "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x_values = [graph.nodes[x]['pos'][0] for x in best_solution]\n", + "y_values = [graph.nodes[x]['pos'][1] for x in best_solution]\n", + "\n", + "# Création de la figure et des axes\n", + "fig, ax = plt.subplots()\n", + "ax.set_aspect('equal')\n", + "ax.set_xlabel('Coordonnée X')\n", + "ax.set_ylabel('Coordonnée Y')\n", + "ax.set_title(\"Tracé du trajet emprunté par les fourmis\")\n", + "\n", + "# Tracer le graphe sans les arêtes\n", + "nx.draw_networkx_nodes(graph, pos=nx.get_node_attributes(graph, 'pos'), ax=ax)\n", + "nx.draw_networkx_labels(graph, pos=nx.get_node_attributes(graph, 'pos'), ax=ax)\n", + "\n", + "# Tracer uniquement le trajet emprunté par les fourmis\n", + "ax.plot(x_values, y_values, linestyle='-', marker='o')\n", + "\n", + "# Tracer une ligne du dernier nœud au premier nœud pour fermer la boucle\n", + "ax.plot([x_values[-1], x_values[0]], [y_values[-1], y_values[0]], linestyle='-', color='r')\n", + "\n", + "# Afficher le graphique\n", + "plt.show()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "9017d1eb", + "metadata": {}, + "source": [ + "# Avec un nombre de camion disponible" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "23e32e2a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meilleure solution : [[10, 1, 15, 17, 20, 7, 2, 0], [13, 14, 4, 6, 19, 8, 9, 0], [16, 11, 18, 3, 12, 5, 0, 0]]\n", + "Distance totale : 21.0\n" + ] + } + ], + "source": [ + "import concurrent.futures\n", + "num_trucks = 3 # Nombre de camions disponibles\n", + "\n", + "\n", + "# Fonction d'évaluation de la qualité d'une solution (ici, la distance totale)\n", + "def evaluate_solution(solution, distances):\n", + " total_distance = 0\n", + " num_nodes = len(solution)\n", + "\n", + " for i in range(num_nodes - 1):\n", + " current_node = solution[i]\n", + " next_node = solution[i + 1]\n", + " total_distance += distances[current_node][next_node]\n", + "\n", + "\n", + " #if solution[-1] == 0 and len(solution) < num_nodes:\n", + " #return float('inf')\n", + "\n", + " # Ajouter la distance de retour au dépôt\n", + " total_distance += distances[solution[-1]][solution[0]]\n", + "\n", + " return total_distance\n", + "\n", + " \n", + "def ant_colony_optimization(distances, num_ants, num_iterations, evaporation_rate, alpha, beta, num_trucks):\n", + " num_nodes = len(distances)\n", + " \n", + " pheromone = np.ones((num_nodes, num_nodes)) # Matrice de phéromones initiale\n", + " best_solution = None\n", + " best_distance = float('inf')\n", + "\n", + " start_time = time.time()\n", + "\n", + " for iteration in range(num_iterations):\n", + " # Construction de solutions par les fourmis\n", + " solutions = []\n", + "\n", + " for ant in range(num_ants):\n", + " visited = set()\n", + " current_node = random.randint(0, num_nodes - 1)\n", + " visited.add(current_node)\n", + " solution = [current_node]\n", + "\n", + " while len(visited) < num_nodes:\n", + " next_node = None\n", + " probabilities = []\n", + "\n", + " # Calcul des probabilités de choisir chaque prochain nœud\n", + " for node in range(num_nodes):\n", + " if node not in visited and (node !=0 or len(visited) == num_nodes - 1):\n", + " pheromone_value = pheromone[current_node][node]\n", + " distance_value = distances[current_node][node]\n", + " probability = (pheromone_value ** alpha) * ((1 / distance_value) ** beta)\n", + " probabilities.append((node, probability))\n", + "\n", + " total_probability = sum(prob for _, prob in probabilities)\n", + " probabilities = [(node, prob / total_probability) for node, prob in probabilities]\n", + "\n", + " \n", + " roulette_wheel = random.random()\n", + " probability_sum = 0\n", + "\n", + "\n", + " for node, probability in probabilities:\n", + " \n", + " probability_sum += probability\n", + " if probability_sum >= roulette_wheel:\n", + " next_node = node\n", + " break\n", + "\n", + " visited.add(next_node)\n", + " solution.append(next_node)\n", + " current_node = next_node\n", + "\n", + " # Ajouter le retour au dépôt central à la fin du trajet\n", + " solution.append(0)\n", + "\n", + " solutions.append(solution)\n", + "\n", + " # Évaluation des solutions et mise à jour de la meilleure solution\n", + " for solution in solutions:\n", + " distance = evaluate_solution(solution, distances)\n", + " if distance < best_distance:\n", + " best_solution = solution\n", + " best_distance = distance\n", + "\n", + " # Mise à jour des phéromones\n", + " pheromone *= evaporation_rate # Évaporation des phéromones existantes\n", + "\n", + " for solution in solutions:\n", + " delta_pheromone = 1 / evaluate_solution(solution, distances)\n", + " for i in range(num_nodes - 1):\n", + " node1 = solution[i]\n", + " node2 = solution[i + 1]\n", + " pheromone[node1][node2] += delta_pheromone\n", + " pheromone[node2][node1] += delta_pheromone\n", + "\n", + " # Séparer la meilleure solution en trajets pour chaque camion\n", + " truck_solutions = []\n", + " num_nodes_per_truck = num_nodes // num_trucks\n", + "\n", + " for i in range(num_trucks):\n", + " start_index = i * num_nodes_per_truck\n", + " end_index = start_index + num_nodes_per_truck\n", + " truck_solution = best_solution[start_index:end_index]\n", + " truck_solutions.append(truck_solution + [0])\n", + " \n", + "\n", + " return truck_solutions, best_distance\n", + "\n", + "\n", + "def trucks_thread(i, num_nodes_per_truck, best_solution, truck_solutions):\n", + " start_index = i * num_nodes_per_truck\n", + " end_index = start_index + num_nodes_per_truck\n", + " truck_solution = best_solution[start_index:end_index]\n", + " truck_solutions.append(truck_solution)\n", + " return truck_solutions\n", + "\n", + "\n", + "\n", + "num_ants = 10\n", + "num_iterations = 100\n", + "evaporation_rate = 0.5\n", + "alpha = 1\n", + "beta = 1\n", + "\n", + "best_truck_solutions, best_distance = ant_colony_optimization(distance_matrix, num_ants, num_iterations, evaporation_rate, alpha, beta, num_trucks)\n", + "\n", + "\n", + "print(\"Meilleure solution :\", best_truck_solutions)\n", + "print(\"Distance totale :\", best_distance)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "106d6902", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Créer une liste de couleurs pour chaque véhicule\n", + "colors = ['red', 'blue', 'green', 'orange', 'purple'] # Ajoutez plus de couleurs si nécessaire\n", + "\n", + "# Création de la figure et des axes\n", + "fig, ax = plt.subplots()\n", + "ax.set_aspect('equal')\n", + "ax.set_xlabel('Coordonnée X')\n", + "ax.set_ylabel('Coordonnée Y')\n", + "ax.set_title(\"Tracé du trajet emprunté par les fourmis\")\n", + "\n", + "# Tracer le graphe sans les arêtes\n", + "nx.draw_networkx_nodes(graph, pos=nx.get_node_attributes(graph, 'pos'), ax=ax)\n", + "nx.draw_networkx_labels(graph, pos=nx.get_node_attributes(graph, 'pos'), ax=ax)\n", + "\n", + "# Tracer chaque trajet de chaque véhicule avec une couleur différente\n", + "for i, truck_solution in enumerate(best_truck_solutions):\n", + " x_values = [graph.nodes[x]['pos'][0] for x in truck_solution]\n", + " y_values = [graph.nodes[x]['pos'][1] for x in truck_solution]\n", + " ax.plot(x_values, y_values, linestyle='-', marker='o', color=colors[i % len(colors)])\n", + "\n", + " # Tracer une ligne du dernier nœud au premier nœud pour fermer la boucle\n", + " ax.plot([x_values[-1], x_values[0]], [y_values[-1], y_values[0]], linestyle='-', color=colors[i % len(colors)])\n", + "\n", + "# Afficher le graphique\n", + "plt.show()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "29df0137", + "metadata": {}, + "source": [ + "# En rajoutant la contrainte de Time Window pour une instance de VRPTW" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "8266e414-d350-4101-8f15-9cc05ee02934", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "20\n", + "Client 0 : (0, inf)\n", + "Client 1 : (28, 47)\n", + "Client 2 : (96, 116)\n", + "Client 3 : (49, 79)\n", + "Client 4 : (78, 91)\n", + "Client 5 : (13, 51)\n", + "Client 6 : (17, 55)\n", + "Client 7 : (95, 132)\n", + "Client 8 : (24, 44)\n", + "Client 9 : (75, 116)\n", + "Client 10 : (15, 32)\n", + "Client 11 : (21, 65)\n", + "Client 12 : (72, 118)\n", + "Client 13 : (45, 84)\n", + "Client 14 : (2, 46)\n", + "Client 15 : (26, 38)\n", + "Client 16 : (83, 107)\n", + "Client 17 : (9, 43)\n", + "Client 18 : (21, 33)\n", + "Client 19 : (58, 89)\n", + "Client 20 : (62, 109)\n", + "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]\n", + "[(0, 5), (0, 13), (0, 10), (0, 20), (1, 10), (1, 14), (1, 15), (2, 13), (2, 20), (2, 7), (2, 9), (2, 11), (3, 13), (3, 18), (3, 5), (3, 12), (3, 17), (4, 14), (4, 12), (4, 6), (5, 12), (6, 17), (6, 19), (7, 20), (8, 9), (8, 17), (8, 19), (9, 16), (9, 12), (10, 20), (11, 16), (11, 14), (11, 18), (12, 14), (13, 14), (13, 19), (13, 15), (13, 18), (15, 16), (15, 17), (16, 17), (17, 20)]\n" + ] + } + ], + "source": [ + "\n", + "\n", + "# 2. Attribuer les fenêtres de temps aux clients\n", + "def assign_time_windows(graph):\n", + " # Créer un dictionnaire pour stocker les fenêtres de temps des clients\n", + " time_windows = {}\n", + "\n", + " # Définir la fenêtre de temps pour le dépôt central (nœud 0)\n", + " time_windows[0] = (0, float('inf'))\n", + "\n", + " # Assigner une fenêtre de temps à chaque client\n", + " for node in graph.nodes():\n", + " if node !=0 and node != graph.number_of_nodes() :\n", + " # Générer une fenêtre de temps aléatoire pour chaque client\n", + " start_time = random.randint(0, 100)\n", + " end_time = start_time + random.randint(10, 50)\n", + " time_windows[node] = (start_time, end_time)\n", + " \n", + "\n", + " return time_windows\n", + "\n", + "# Attribuer les fenêtres de temps aux clients\n", + "time_windows = assign_time_windows(graph)\n", + "\n", + "print(max(graph.nodes()))\n", + "# Afficher les fenêtres de temps assignées\n", + "for node, window in time_windows.items():\n", + " print(\"Client\", node, \":\", window)\n", + " \n", + "#paramètres ACO\n", + "\n", + "print(graph.nodes())\n", + "print(graph.edges())\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "901e6b93", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meilleure solution : [[9, 15, 10, 8, 3, 0], [13, 17, 14, 18, 6, 0], [1, 5, 11, 12, 19, 0], [16, 20, 2, 7, 4, 0]]\n", + "Distance totale : 51.0\n" + ] + } + ], + "source": [ + "import concurrent.futures\n", + "import random\n", + "import numpy as np\n", + "import time\n", + "\n", + "num_trucks = 4 # Nombre de camions disponibles\n", + "\n", + "\n", + "# Fonction d'évaluation de la qualité d'une solution (ici, la distance totale)\n", + "def evaluate_solution(solution, distances, time_windows):\n", + " total_distance = 0\n", + " total_delay = 0\n", + " arrival_time = 0\n", + " num_nodes = len(solution)\n", + " waiting_times = []\n", + "\n", + " for i in range(num_nodes - 1):\n", + " current_node = solution[i]\n", + " next_node = solution[i + 1]\n", + " total_distance += distances[current_node][next_node]\n", + " arrival_time += distances[current_node][next_node]\n", + "\n", + " if arrival_time < time_windows[next_node][0]:\n", + " waiting_time = time_windows[next_node][0] - arrival_time\n", + " waiting_times.append((next_node, waiting_time))\n", + " arrival_time = time_windows[next_node][0]\n", + " elif arrival_time > time_windows[next_node][1]:\n", + " total_delay += arrival_time - time_windows[next_node][1]\n", + "\n", + " # Ajouter la distance de retour au dépôt\n", + " total_distance += distances[solution[-1]][solution[0]]\n", + "\n", + " return total_distance, total_delay, waiting_times\n", + "\n", + " \n", + "num_ants = 10\n", + "num_iterations = 100\n", + "evaporation_rate = 0.5\n", + "alpha = 1\n", + "beta = 1\n", + "\n", + "def ant_colony_optimization(distances, num_ants, num_iterations, evaporation_rate, alpha, beta, num_trucks):\n", + " num_nodes = len(distances)\n", + " pheromone = np.ones((num_nodes, num_nodes)) \n", + " best_solution = None\n", + " best_distance = float('inf')\n", + " best_delay = float('inf')\n", + " best_score = float('inf') # Initialize the best_score\n", + " best_waiting_times = None\n", + "\n", + " for iteration in range(num_iterations):\n", + " solutions = []\n", + " for ant in range(num_ants):\n", + " visited = set()\n", + " current_node = random.randint(0, num_nodes - 1)\n", + " visited.add(current_node)\n", + " solution = [current_node]\n", + " while len(visited) < num_nodes:\n", + " next_node = None\n", + " probabilities = []\n", + " arrival_time = 0\n", + " for node in range(num_nodes):\n", + " if node not in visited and (node !=0 or len(visited) == num_nodes - 1):\n", + " pheromone_value = pheromone[current_node][node]\n", + " distance_value = distances[current_node][node]\n", + " wait_time = max(0, time_windows[node][0]- (arrival_time + distance_value))\n", + " probability = (pheromone_value ** alpha) * ((1 / (distance_value + wait_time)) ** beta)\n", + " probabilities.append((node, probability))\n", + " total_probability = sum(prob for _, prob in probabilities)\n", + " probabilities = [(node, prob / total_probability) for node, prob in probabilities]\n", + " roulette_wheel = random.random()\n", + " probability_sum = 0\n", + " for node, probability in probabilities:\n", + " probability_sum += probability\n", + " if probability_sum >= roulette_wheel:\n", + " next_node = node\n", + " break\n", + " visited.add(next_node)\n", + " solution.append(next_node)\n", + " current_node = next_node\n", + " solution.append(0)\n", + " solutions.append(solution)\n", + "\n", + " for solution in solutions:\n", + " distance, delay, waiting_times = evaluate_solution(solution, distances, time_windows)\n", + " score = distance + delay\n", + " if score < best_score:\n", + " best_solution = solution\n", + " best_distance = distance\n", + " best_delay = delay\n", + " best_waiting_times = waiting_times\n", + " pheromone *= evaporation_rate\n", + " for solution in solutions:\n", + " delta_pheromone = 1 / (evaluate_solution(solution, distances, time_windows)[0] + 0.01)\n", + " for i in range(num_nodes - 1):\n", + " node1 = solution[i]\n", + " node2 = solution[i + 1]\n", + " pheromone[node1][node2] += delta_pheromone\n", + " pheromone[node2][node1] += delta_pheromone\n", + " truck_solutions = []\n", + " num_nodes_per_truck = num_nodes // num_trucks\n", + " for i in range(num_trucks):\n", + " start_index = i * num_nodes_per_truck\n", + " end_index = start_index + num_nodes_per_truck\n", + " truck_solution = best_solution[start_index:end_index]\n", + " truck_solutions.append(truck_solution + [0])\n", + " return truck_solutions, best_distance, best_waiting_times\n", + "\n", + "\n", + "best_truck_solutions, best_distance, best_waiting_times = ant_colony_optimization(distance_matrix, num_ants, num_iterations, evaporation_rate, alpha, beta, num_trucks)\n", + "\n", + "print(\"Meilleure solution :\", best_truck_solutions)\n", + "print(\"Distance totale :\", best_distance)\n" + ] } ], "metadata": { diff --git a/README.md b/README.md index c1dfc0b..b73f0b3 100644 --- a/README.md +++ b/README.md @@ -1,92 +1,16 @@ -# a3-algorithmique-avancée - - - -## Getting started - -To make it easy for you to get started with GitLab, here's a list of recommended next steps. - -Already a pro? Just edit this README.md and make it your own. Want to make it easy? [Use the template at the bottom](#editing-this-readme)! - -## Add your files - -- [ ] [Create](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#create-a-file) or [upload](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#upload-a-file) files -- [ ] [Add files using the command line](https://docs.gitlab.com/ee/gitlab-basics/add-file.html#add-a-file-using-the-command-line) or push an existing Git repository with the following command: - -``` -cd existing_repo -git remote add origin https://gitlab.lihoco.fr/axok/a3-algorithmique-avancee.git -git branch -M main -git push -uf origin main -``` - -## Integrate with your tools - -- [ ] [Set up project integrations](https://gitlab.lihoco.fr/axok/a3-algorithmique-avancee/-/settings/integrations) - -## Collaborate with your team - -- [ ] [Invite team members and collaborators](https://docs.gitlab.com/ee/user/project/members/) -- [ ] [Create a new merge request](https://docs.gitlab.com/ee/user/project/merge_requests/creating_merge_requests.html) -- [ ] [Automatically close issues from merge requests](https://docs.gitlab.com/ee/user/project/issues/managing_issues.html#closing-issues-automatically) -- [ ] [Enable merge request approvals](https://docs.gitlab.com/ee/user/project/merge_requests/approvals/) -- [ ] [Automatically merge when pipeline succeeds](https://docs.gitlab.com/ee/user/project/merge_requests/merge_when_pipeline_succeeds.html) - -## Test and Deploy - -Use the built-in continuous integration in GitLab. - -- [ ] [Get started with GitLab CI/CD](https://docs.gitlab.com/ee/ci/quick_start/index.html) -- [ ] [Analyze your code for known vulnerabilities with Static Application Security Testing(SAST)](https://docs.gitlab.com/ee/user/application_security/sast/) -- [ ] [Deploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy](https://docs.gitlab.com/ee/topics/autodevops/requirements.html) -- [ ] [Use pull-based deployments for improved Kubernetes management](https://docs.gitlab.com/ee/user/clusters/agent/) -- [ ] [Set up protected environments](https://docs.gitlab.com/ee/ci/environments/protected_environments.html) - -*** - -# Editing this README - -When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!). Thank you to [makeareadme.com](https://www.makeareadme.com/) for this template. - -## Suggestions for a good README -Every project is different, so consider which of these sections apply to yours. The sections used in the template are suggestions for most open source projects. Also keep in mind that while a README can be too long and detailed, too long is better than too short. If you think your README is too long, consider utilizing another form of documentation rather than cutting out information. - -## Name -Choose a self-explaining name for your project. +# Projet de Mobilité Multimodale Intelligente - Optimisation des tournées de livraison ## Description -Let people know what your project can do specifically. Provide context and add a link to any reference visitors might be unfamiliar with. A list of Features or a Background subsection can also be added here. If there are alternatives to your project, this is a good place to list differentiating factors. -## Badges -On some READMEs, you may see small images that convey metadata, such as whether or not all the tests are passing for the project. You can use Shields to add some to your README. Many services also have instructions for adding a badge. +Dans le cadre de l'appel à manifestation d'intérêt lancé par l'ADEME (Agence de l’Environnement et de la Maîtrise de l’Energie) visant à promouvoir la réalisation de démonstrateurs et d'expérimentations de nouvelles solutions de mobilité pour les personnes et les marchandises adaptées à différents types de territoires, notre structure CesiCDP, en collaboration avec plusieurs partenaires, a décidé de concentrer ses efforts sur l'optimisation de la gestion des tournées de livraison. -## Visuals -Depending on what you are making, it can be a good idea to include screenshots or even a video (you'll frequently see GIFs rather than actual videos). Tools like ttygif can help, but check out Asciinema for a more sophisticated method. +Notre problème algorithmique consiste à déterminer, sur un réseau routier, une tournée qui permet de relier entre elles un sous-ensemble de villes, puis de revenir à son point de départ de manière à minimiser la durée totale de la tournée. Cette optimisation tient compte du trafic prévu sur chaque axe pour les différentes tranches horaires. -## Installation -Within a particular ecosystem, there may be a common way of installing things, such as using Yarn, NuGet, or Homebrew. However, consider the possibility that whoever is reading your README is a novice and would like more guidance. Listing specific steps helps remove ambiguity and gets people to using your project as quickly as possible. If it only runs in a specific context like a particular programming language version or operating system or has dependencies that have to be installed manually, also add a Requirements subsection. +Dans un souci de réalisme et pour capturer toute l’attention de l’ADEME, nous avons décidé d'ajouter la contrainte du nombre de camions disponibles simultanément pour effectuer les livraisons, en implémentant l'algorithme VRP (Vehicle Routing Problem). -## Usage -Use examples liberally, and show the expected output if you can. It's helpful to have inline the smallest example of usage that you can demonstrate, while providing links to more sophisticated examples if they are too long to reasonably include in the README. +## Membres -## Support -Tell people where they can go to for help. It can be any combination of an issue tracker, a chat room, an email address, etc. - -## Roadmap -If you have ideas for releases in the future, it is a good idea to list them in the README. - -## Contributing -State if you are open to contributions and what your requirements are for accepting them. - -For people who want to make changes to your project, it's helpful to have some documentation on how to get started. Perhaps there is a script that they should run or some environment variables that they need to set. Make these steps explicit. These instructions could also be useful to your future self. - -You can also document commands to lint the code or run tests. These steps help to ensure high code quality and reduce the likelihood that the changes inadvertently break something. Having instructions for running tests is especially helpful if it requires external setup, such as starting a Selenium server for testing in a browser. - -## Authors and acknowledgment -Show your appreciation to those who have contributed to the project. - -## License -For open source projects, say how it is licensed. - -## Project status -If you have run out of energy or time for your project, put a note at the top of the README saying that development has slowed down or stopped completely. Someone may choose to fork your project or volunteer to step in as a maintainer or owner, allowing your project to keep going. You can also make an explicit request for maintainers. +- Louis DUMONT +- Romain HUNTZINGER +- Nathan KISS +- Léandre FERNANDEZ \ No newline at end of file