diff --git a/examples/notebook/constraint_solver/cvrp.ipynb b/examples/notebook/constraint_solver/cvrp.ipynb deleted file mode 100644 index 603d39b582..0000000000 --- a/examples/notebook/constraint_solver/cvrp.ipynb +++ /dev/null @@ -1,268 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "google", - "metadata": {}, - "source": [ - "##### Copyright 2023 Google LLC." - ] - }, - { - "cell_type": "markdown", - "id": "apache", - "metadata": {}, - "source": [ - "Licensed under the Apache License, Version 2.0 (the \"License\");\n", - "you may not use this file except in compliance with the License.\n", - "You may obtain a copy of the License at\n", - "\n", - " http://www.apache.org/licenses/LICENSE-2.0\n", - "\n", - "Unless required by applicable law or agreed to in writing, software\n", - "distributed under the License is distributed on an \"AS IS\" BASIS,\n", - "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", - "See the License for the specific language governing permissions and\n", - "limitations under the License.\n" - ] - }, - { - "cell_type": "markdown", - "id": "basename", - "metadata": {}, - "source": [ - "# cvrp" - ] - }, - { - "cell_type": "markdown", - "id": "link", - "metadata": {}, - "source": [ - "\n", - "\n", - "\n", - "
\n", - "Run in Google Colab\n", - "\n", - "View source on GitHub\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "doc", - "metadata": {}, - "source": [ - "First, you must install [ortools](https://pypi.org/project/ortools/) package in this colab." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "install", - "metadata": {}, - "outputs": [], - "source": [ - "%pip install ortools" - ] - }, - { - "cell_type": "markdown", - "id": "description", - "metadata": {}, - "source": [ - "Capacitated Vehicle Routing Problem (CVRP).\n", - "\n", - " This is a sample using the routing library python wrapper to solve a CVRP\n", - " problem.\n", - " A description of the problem can be found here:\n", - " http://en.wikipedia.org/wiki/Vehicle_routing_problem.\n", - "\n", - " Distances are in meters.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "code", - "metadata": {}, - "outputs": [], - "source": [ - "from functools import partial\n", - "\n", - "from ortools.constraint_solver import pywrapcp\n", - "from ortools.constraint_solver import routing_enums_pb2\n", - "\n", - "\n", - "###########################\n", - "# Problem Data Definition #\n", - "###########################\n", - "def create_data_model():\n", - " \"\"\"Stores the data for the problem\"\"\"\n", - " data = {}\n", - " # Locations in block unit\n", - " _locations = \\\n", - " [(4, 4), # depot\n", - " (2, 0), (8, 0), # locations to visit\n", - " (0, 1), (1, 1),\n", - " (5, 2), (7, 2),\n", - " (3, 3), (6, 3),\n", - " (5, 5), (8, 5),\n", - " (1, 6), (2, 6),\n", - " (3, 7), (6, 7),\n", - " (0, 8), (7, 8)]\n", - " # Compute locations in meters using the block dimension defined as follow\n", - " # Manhattan average block: 750ft x 264ft -> 228m x 80m\n", - " # here we use: 114m x 80m city block\n", - " # src: https://nyti.ms/2GDoRIe 'NY Times: Know Your distance'\n", - " data['locations'] = [(l[0] * 114, l[1] * 80) for l in _locations]\n", - " data['num_locations'] = len(data['locations'])\n", - " data['demands'] = \\\n", - " [0, # depot\n", - " 1, 1, # 1, 2\n", - " 2, 4, # 3, 4\n", - " 2, 4, # 5, 6\n", - " 8, 8, # 7, 8\n", - " 1, 2, # 9,10\n", - " 1, 2, # 11,12\n", - " 4, 4, # 13, 14\n", - " 8, 8] # 15, 16\n", - " data['num_vehicles'] = 4\n", - " data['vehicle_capacity'] = 15\n", - " data['depot'] = 0\n", - " return data\n", - "\n", - "\n", - "#######################\n", - "# Problem Constraints #\n", - "#######################\n", - "def manhattan_distance(position_1, position_2):\n", - " \"\"\"Computes the Manhattan distance between two points\"\"\"\n", - " return (abs(position_1[0] - position_2[0]) +\n", - " abs(position_1[1] - position_2[1]))\n", - "\n", - "\n", - "def create_distance_evaluator(data):\n", - " \"\"\"Creates callback to return distance between points.\"\"\"\n", - " _distances = {}\n", - " # precompute distance between location to have distance callback in O(1)\n", - " for from_node in range(data['num_locations']):\n", - " _distances[from_node] = {}\n", - " for to_node in range(data['num_locations']):\n", - " if from_node == to_node:\n", - " _distances[from_node][to_node] = 0\n", - " else:\n", - " _distances[from_node][to_node] = (manhattan_distance(\n", - " data['locations'][from_node], data['locations'][to_node]))\n", - "\n", - " def distance_evaluator(manager, from_node, to_node):\n", - " \"\"\"Returns the manhattan distance between the two nodes\"\"\"\n", - " return _distances[manager.IndexToNode(from_node)][manager.IndexToNode(\n", - " to_node)]\n", - "\n", - " return distance_evaluator\n", - "\n", - "\n", - "def create_demand_evaluator(data):\n", - " \"\"\"Creates callback to get demands at each location.\"\"\"\n", - " _demands = data['demands']\n", - "\n", - " def demand_evaluator(manager, node):\n", - " \"\"\"Returns the demand of the current node\"\"\"\n", - " return _demands[manager.IndexToNode(node)]\n", - "\n", - " return demand_evaluator\n", - "\n", - "\n", - "def add_capacity_constraints(routing, data, demand_evaluator_index):\n", - " \"\"\"Adds capacity constraint\"\"\"\n", - " capacity = 'Capacity'\n", - " routing.AddDimension(\n", - " demand_evaluator_index,\n", - " 0, # null capacity slack\n", - " data['vehicle_capacity'],\n", - " True, # start cumul to zero\n", - " capacity)\n", - "\n", - "\n", - "###########\n", - "# Printer #\n", - "###########\n", - "def print_solution(data, routing, manager, assignment): # pylint:disable=too-many-locals\n", - " \"\"\"Prints assignment on console\"\"\"\n", - " print(f'Objective: {assignment.ObjectiveValue()}')\n", - " total_distance = 0\n", - " total_load = 0\n", - " capacity_dimension = routing.GetDimensionOrDie('Capacity')\n", - " for vehicle_id in range(data['num_vehicles']):\n", - " index = routing.Start(vehicle_id)\n", - " plan_output = f'Route for vehicle {vehicle_id}:\\n'\n", - " distance = 0\n", - " while not routing.IsEnd(index):\n", - " load_var = capacity_dimension.CumulVar(index)\n", - " plan_output += (f' {manager.IndexToNode(index)} '\n", - " f'Load({assignment.Value(load_var)}) -> ')\n", - " previous_index = index\n", - " index = assignment.Value(routing.NextVar(index))\n", - " distance += routing.GetArcCostForVehicle(previous_index, index,\n", - " vehicle_id)\n", - " load_var = capacity_dimension.CumulVar(index)\n", - " plan_output += f' {manager.IndexToNode(index)} Load({assignment.Value(load_var)})\\n'\n", - " plan_output += f'Distance of the route: {distance}m\\n'\n", - " plan_output += f'Load of the route: {assignment.Value(load_var)}\\n'\n", - " print(plan_output)\n", - " total_distance += distance\n", - " total_load += assignment.Value(load_var)\n", - " print(f'Total Distance of all routes: {total_distance}m')\n", - " print(f'Total Load of all routes: {total_load}')\n", - "\n", - "\n", - "########\n", - "# Main #\n", - "########\n", - "def main():\n", - " \"\"\"Entry point of the program\"\"\"\n", - " # Instantiate the data problem.\n", - " data = create_data_model()\n", - "\n", - " # Create the routing index manager\n", - " manager = pywrapcp.RoutingIndexManager(data['num_locations'],\n", - " data['num_vehicles'], data['depot'])\n", - "\n", - " # Create Routing Model\n", - " routing = pywrapcp.RoutingModel(manager)\n", - "\n", - " # Define weight of each edge\n", - " distance_evaluator = routing.RegisterTransitCallback(\n", - " partial(create_distance_evaluator(data), manager))\n", - " routing.SetArcCostEvaluatorOfAllVehicles(distance_evaluator)\n", - "\n", - " # Add Capacity constraint\n", - " demand_evaluator_index = routing.RegisterUnaryTransitCallback(\n", - " partial(create_demand_evaluator(data), manager))\n", - " add_capacity_constraints(routing, data, demand_evaluator_index)\n", - "\n", - " # Setting first solution heuristic (cheapest addition).\n", - " search_parameters = pywrapcp.DefaultRoutingSearchParameters()\n", - " search_parameters.first_solution_strategy = (\n", - " routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC) # pylint: disable=no-member\n", - " search_parameters.local_search_metaheuristic = (\n", - " routing_enums_pb2.LocalSearchMetaheuristic.GUIDED_LOCAL_SEARCH)\n", - " search_parameters.time_limit.FromSeconds(1)\n", - "\n", - " # Solve the problem.\n", - " assignment = routing.SolveWithParameters(search_parameters)\n", - " print_solution(data, routing, manager, assignment)\n", - "\n", - "\n", - "main()\n", - "\n" - ] - } - ], - "metadata": {}, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/notebook/constraint_solver/cvrptw.ipynb b/examples/notebook/constraint_solver/cvrptw.ipynb deleted file mode 100644 index 965ca0f563..0000000000 --- a/examples/notebook/constraint_solver/cvrptw.ipynb +++ /dev/null @@ -1,366 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "google", - "metadata": {}, - "source": [ - "##### Copyright 2023 Google LLC." - ] - }, - { - "cell_type": "markdown", - "id": "apache", - "metadata": {}, - "source": [ - "Licensed under the Apache License, Version 2.0 (the \"License\");\n", - "you may not use this file except in compliance with the License.\n", - "You may obtain a copy of the License at\n", - "\n", - " http://www.apache.org/licenses/LICENSE-2.0\n", - "\n", - "Unless required by applicable law or agreed to in writing, software\n", - "distributed under the License is distributed on an \"AS IS\" BASIS,\n", - "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", - "See the License for the specific language governing permissions and\n", - "limitations under the License.\n" - ] - }, - { - "cell_type": "markdown", - "id": "basename", - "metadata": {}, - "source": [ - "# cvrptw" - ] - }, - { - "cell_type": "markdown", - "id": "link", - "metadata": {}, - "source": [ - "\n", - "\n", - "\n", - "
\n", - "Run in Google Colab\n", - "\n", - "View source on GitHub\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "doc", - "metadata": {}, - "source": [ - "First, you must install [ortools](https://pypi.org/project/ortools/) package in this colab." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "install", - "metadata": {}, - "outputs": [], - "source": [ - "%pip install ortools" - ] - }, - { - "cell_type": "markdown", - "id": "description", - "metadata": {}, - "source": [ - "Capacitated Vehicle Routing Problem with Time Windows (CVRPTW).\n", - "\n", - " This is a sample using the routing library python wrapper to solve a CVRPTW\n", - " problem.\n", - " A description of the problem can be found here:\n", - " http://en.wikipedia.org/wiki/Vehicle_routing_problem.\n", - "\n", - " Distances are in meters and time in minutes.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "code", - "metadata": {}, - "outputs": [], - "source": [ - "from functools import partial\n", - "from ortools.constraint_solver import routing_enums_pb2\n", - "from ortools.constraint_solver import pywrapcp\n", - "\n", - "\n", - "def create_data_model():\n", - " \"\"\"Stores the data for the problem.\"\"\"\n", - " data = {}\n", - " # Locations in block unit\n", - " _locations = \\\n", - " [(4, 4), # depot\n", - " (2, 0), (8, 0), # locations to visit\n", - " (0, 1), (1, 1),\n", - " (5, 2), (7, 2),\n", - " (3, 3), (6, 3),\n", - " (5, 5), (8, 5),\n", - " (1, 6), (2, 6),\n", - " (3, 7), (6, 7),\n", - " (0, 8), (7, 8)]\n", - " # Compute locations in meters using the block dimension defined as follow\n", - " # Manhattan average block: 750ft x 264ft -> 228m x 80m\n", - " # here we use: 114m x 80m city block\n", - " # src: https://nyti.ms/2GDoRIe \"NY Times: Know Your distance\"\n", - " data['locations'] = [(l[0] * 114, l[1] * 80) for l in _locations]\n", - " data['num_locations'] = len(data['locations'])\n", - " data['time_windows'] = \\\n", - " [(0, 0),\n", - " (75, 85), (75, 85), # 1, 2\n", - " (60, 70), (45, 55), # 3, 4\n", - " (0, 8), (50, 60), # 5, 6\n", - " (0, 10), (10, 20), # 7, 8\n", - " (0, 10), (75, 85), # 9, 10\n", - " (85, 95), (5, 15), # 11, 12\n", - " (15, 25), (10, 20), # 13, 14\n", - " (45, 55), (30, 40)] # 15, 16\n", - " data['demands'] = \\\n", - " [0, # depot\n", - " 1, 1, # 1, 2\n", - " 2, 4, # 3, 4\n", - " 2, 4, # 5, 6\n", - " 8, 8, # 7, 8\n", - " 1, 2, # 9,10\n", - " 1, 2, # 11,12\n", - " 4, 4, # 13, 14\n", - " 8, 8] # 15, 16\n", - " data['time_per_demand_unit'] = 5 # 5 minutes/unit\n", - " data['num_vehicles'] = 4\n", - " data['vehicle_capacity'] = 15\n", - " data['vehicle_speed'] = 83 # Travel speed: 5km/h converted in m/min\n", - " data['depot'] = 0\n", - " return data\n", - "\n", - "\n", - "#######################\n", - "# Problem Constraints #\n", - "#######################\n", - "def manhattan_distance(position_1, position_2):\n", - " \"\"\"Computes the Manhattan distance between two points\"\"\"\n", - " return (abs(position_1[0] - position_2[0]) +\n", - " abs(position_1[1] - position_2[1]))\n", - "\n", - "\n", - "def create_distance_evaluator(data):\n", - " \"\"\"Creates callback to return distance between points.\"\"\"\n", - " _distances = {}\n", - " # precompute distance between location to have distance callback in O(1)\n", - " for from_node in range(data['num_locations']):\n", - " _distances[from_node] = {}\n", - " for to_node in range(data['num_locations']):\n", - " if from_node == to_node:\n", - " _distances[from_node][to_node] = 0\n", - " else:\n", - " _distances[from_node][to_node] = (manhattan_distance(\n", - " data['locations'][from_node], data['locations'][to_node]))\n", - "\n", - " def distance_evaluator(manager, from_node, to_node):\n", - " \"\"\"Returns the manhattan distance between the two nodes\"\"\"\n", - " return _distances[manager.IndexToNode(from_node)][manager.IndexToNode(\n", - " to_node)]\n", - "\n", - " return distance_evaluator\n", - "\n", - "\n", - "def create_demand_evaluator(data):\n", - " \"\"\"Creates callback to get demands at each location.\"\"\"\n", - " _demands = data['demands']\n", - "\n", - " def demand_evaluator(manager, node):\n", - " \"\"\"Returns the demand of the current node\"\"\"\n", - " return _demands[manager.IndexToNode(node)]\n", - "\n", - " return demand_evaluator\n", - "\n", - "\n", - "def add_capacity_constraints(routing, data, demand_evaluator_index):\n", - " \"\"\"Adds capacity constraint\"\"\"\n", - " capacity = 'Capacity'\n", - " routing.AddDimension(\n", - " demand_evaluator_index,\n", - " 0, # null capacity slack\n", - " data['vehicle_capacity'],\n", - " True, # start cumul to zero\n", - " capacity)\n", - "\n", - "\n", - "def create_time_evaluator(data):\n", - " \"\"\"Creates callback to get total times between locations.\"\"\"\n", - "\n", - " def service_time(data, node):\n", - " \"\"\"Gets the service time for the specified location.\"\"\"\n", - " return data['demands'][node] * data['time_per_demand_unit']\n", - "\n", - " def travel_time(data, from_node, to_node):\n", - " \"\"\"Gets the travel times between two locations.\"\"\"\n", - " if from_node == to_node:\n", - " travel_time = 0\n", - " else:\n", - " travel_time = manhattan_distance(\n", - " data['locations'][from_node],\n", - " data['locations'][to_node]) / data['vehicle_speed']\n", - " return travel_time\n", - "\n", - " _total_time = {}\n", - " # precompute total time to have time callback in O(1)\n", - " for from_node in range(data['num_locations']):\n", - " _total_time[from_node] = {}\n", - " for to_node in range(data['num_locations']):\n", - " if from_node == to_node:\n", - " _total_time[from_node][to_node] = 0\n", - " else:\n", - " _total_time[from_node][to_node] = int(\n", - " service_time(data, from_node) +\n", - " travel_time(data, from_node, to_node))\n", - "\n", - " def time_evaluator(manager, from_node, to_node):\n", - " \"\"\"Returns the total time between the two nodes\"\"\"\n", - " return _total_time[manager.IndexToNode(from_node)][manager.IndexToNode(\n", - " to_node)]\n", - "\n", - " return time_evaluator\n", - "\n", - "\n", - "def add_time_window_constraints(routing, manager, data, time_evaluator_index):\n", - " \"\"\"Add Global Span constraint\"\"\"\n", - " time = 'Time'\n", - " horizon = 120\n", - " routing.AddDimension(\n", - " time_evaluator_index,\n", - " horizon, # allow waiting time\n", - " horizon, # maximum time per vehicle\n", - " False, # don't force start cumul to zero since we are giving TW to start nodes\n", - " time)\n", - " time_dimension = routing.GetDimensionOrDie(time)\n", - " # Add time window constraints for each location except depot\n", - " # and 'copy' the slack var in the solution object (aka Assignment) to print it\n", - " for location_idx, time_window in enumerate(data['time_windows']):\n", - " if location_idx == 0:\n", - " continue\n", - " index = manager.NodeToIndex(location_idx)\n", - " time_dimension.CumulVar(index).SetRange(time_window[0], time_window[1])\n", - " routing.AddToAssignment(time_dimension.SlackVar(index))\n", - " # Add time window constraints for each vehicle start node\n", - " # and 'copy' the slack var in the solution object (aka Assignment) to print it\n", - " for vehicle_id in range(data['num_vehicles']):\n", - " index = routing.Start(vehicle_id)\n", - " time_dimension.CumulVar(index).SetRange(data['time_windows'][0][0],\n", - " data['time_windows'][0][1])\n", - " routing.AddToAssignment(time_dimension.SlackVar(index))\n", - " # Warning: Slack var is not defined for vehicle's end node\n", - " #routing.AddToAssignment(time_dimension.SlackVar(self.routing.End(vehicle_id)))\n", - "\n", - "\n", - "def print_solution(manager, routing, assignment): # pylint:disable=too-many-locals\n", - " \"\"\"Prints assignment on console\"\"\"\n", - " print(f'Objective: {assignment.ObjectiveValue()}')\n", - " time_dimension = routing.GetDimensionOrDie('Time')\n", - " capacity_dimension = routing.GetDimensionOrDie('Capacity')\n", - " total_distance = 0\n", - " total_load = 0\n", - " total_time = 0\n", - " for vehicle_id in range(manager.GetNumberOfVehicles()):\n", - " index = routing.Start(vehicle_id)\n", - " plan_output = f'Route for vehicle {vehicle_id}:\\n'\n", - " distance = 0\n", - " while not routing.IsEnd(index):\n", - " load_var = capacity_dimension.CumulVar(index)\n", - " time_var = time_dimension.CumulVar(index)\n", - " slack_var = time_dimension.SlackVar(index)\n", - " plan_output += (\n", - " f' {manager.IndexToNode(index)} '\n", - " f'Load({assignment.Value(load_var)}) '\n", - " f'Time({assignment.Min(time_var)},{assignment.Max(time_var)}) '\n", - " f'Slack({assignment.Min(slack_var)},{assignment.Max(slack_var)}) ->'\n", - " )\n", - " previous_index = index\n", - " index = assignment.Value(routing.NextVar(index))\n", - " distance += routing.GetArcCostForVehicle(previous_index, index,\n", - " vehicle_id)\n", - " load_var = capacity_dimension.CumulVar(index)\n", - " time_var = time_dimension.CumulVar(index)\n", - " slack_var = time_dimension.SlackVar(index)\n", - " plan_output += (\n", - " f' {manager.IndexToNode(index)} '\n", - " f'Load({assignment.Value(load_var)}) '\n", - " f'Time({assignment.Min(time_var)},{assignment.Max(time_var)})\\n')\n", - " plan_output += f'Distance of the route: {distance}m\\n'\n", - " plan_output += f'Load of the route: {assignment.Value(load_var)}\\n'\n", - " plan_output += f'Time of the route: {assignment.Value(time_var)}\\n'\n", - " print(plan_output)\n", - " total_distance += distance\n", - " total_load += assignment.Value(load_var)\n", - " total_time += assignment.Value(time_var)\n", - " print(f'Total Distance of all routes: {total_distance}m')\n", - " print(f'Total Load of all routes: {total_load}')\n", - " print(f'Total Time of all routes: {total_time}min')\n", - "\n", - "\n", - "def main():\n", - " \"\"\"Solve the Capacitated VRP with time windows.\"\"\"\n", - " # Instantiate the data problem.\n", - " data = create_data_model()\n", - "\n", - " # Create the routing index manager.\n", - " manager = pywrapcp.RoutingIndexManager(data['num_locations'],\n", - " data['num_vehicles'], data['depot'])\n", - "\n", - " # Create Routing Model.\n", - " routing = pywrapcp.RoutingModel(manager)\n", - "\n", - " # Define weight of each edge.\n", - " distance_evaluator_index = routing.RegisterTransitCallback(\n", - " partial(create_distance_evaluator(data), manager))\n", - "\n", - " # Define cost of each arc.\n", - " routing.SetArcCostEvaluatorOfAllVehicles(distance_evaluator_index)\n", - "\n", - " # Add Capacity constraint.\n", - " demand_evaluator_index = routing.RegisterUnaryTransitCallback(\n", - " partial(create_demand_evaluator(data), manager))\n", - " add_capacity_constraints(routing, data, demand_evaluator_index)\n", - "\n", - " # Add Time Window constraint.\n", - " time_evaluator_index = routing.RegisterTransitCallback(\n", - " partial(create_time_evaluator(data), manager))\n", - " add_time_window_constraints(routing, manager, data, time_evaluator_index)\n", - "\n", - " # Setting first solution heuristic (cheapest addition).\n", - " search_parameters = pywrapcp.DefaultRoutingSearchParameters()\n", - " search_parameters.first_solution_strategy = (\n", - " routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC)\n", - " search_parameters.local_search_metaheuristic = (\n", - " routing_enums_pb2.LocalSearchMetaheuristic.GUIDED_LOCAL_SEARCH)\n", - " search_parameters.time_limit.FromSeconds(2)\n", - " search_parameters.log_search = True\n", - "\n", - " # Solve the problem.\n", - " solution = routing.SolveWithParameters(search_parameters)\n", - "\n", - " # Print solution on console.\n", - " if solution:\n", - " print_solution(manager, routing, solution)\n", - " else:\n", - " print('No solution found!')\n", - "\n", - "\n", - "main()\n", - "\n" - ] - } - ], - "metadata": {}, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/notebook/constraint_solver/vrp_initial_routes.ipynb b/examples/notebook/constraint_solver/vrp_initial_routes.ipynb index b8a8fab8fa..5051d3f932 100644 --- a/examples/notebook/constraint_solver/vrp_initial_routes.ipynb +++ b/examples/notebook/constraint_solver/vrp_initial_routes.ipynb @@ -146,6 +146,7 @@ " print(f\"Maximum of the route distances: {max_route_distance}m\")\n", "\n", "\n", + "\n", "def main():\n", " \"\"\"Solve the CVRP problem.\"\"\"\n", " # Instantiate the data problem.\n", diff --git a/examples/notebook/constraint_solver/vrp_solution_callback.ipynb b/examples/notebook/constraint_solver/vrp_solution_callback.ipynb index cb535ca70f..2d2ac653bd 100644 --- a/examples/notebook/constraint_solver/vrp_solution_callback.ipynb +++ b/examples/notebook/constraint_solver/vrp_solution_callback.ipynb @@ -149,6 +149,7 @@ " print(f\"Total Distance of all routes: {total_distance}m\")\n", "\n", "\n", + "\n", "class SolutionCallback:\n", " \"\"\"Create a solution callback.\"\"\"\n", "\n", @@ -174,6 +175,7 @@ " self._routing_model.solver().FinishCurrentSearch()\n", "\n", "\n", + "\n", "def main():\n", " \"\"\"Entry point of the program.\"\"\"\n", " # Instantiate the data problem.\n", diff --git a/examples/notebook/constraint_solver/vrpgs.ipynb b/examples/notebook/constraint_solver/vrpgs.ipynb deleted file mode 100644 index 53f9b0a5d0..0000000000 --- a/examples/notebook/constraint_solver/vrpgs.ipynb +++ /dev/null @@ -1,247 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "google", - "metadata": {}, - "source": [ - "##### Copyright 2023 Google LLC." - ] - }, - { - "cell_type": "markdown", - "id": "apache", - "metadata": {}, - "source": [ - "Licensed under the Apache License, Version 2.0 (the \"License\");\n", - "you may not use this file except in compliance with the License.\n", - "You may obtain a copy of the License at\n", - "\n", - " http://www.apache.org/licenses/LICENSE-2.0\n", - "\n", - "Unless required by applicable law or agreed to in writing, software\n", - "distributed under the License is distributed on an \"AS IS\" BASIS,\n", - "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", - "See the License for the specific language governing permissions and\n", - "limitations under the License.\n" - ] - }, - { - "cell_type": "markdown", - "id": "basename", - "metadata": {}, - "source": [ - "# vrpgs" - ] - }, - { - "cell_type": "markdown", - "id": "link", - "metadata": {}, - "source": [ - "\n", - "\n", - "\n", - "
\n", - "Run in Google Colab\n", - "\n", - "View source on GitHub\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "doc", - "metadata": {}, - "source": [ - "First, you must install [ortools](https://pypi.org/project/ortools/) package in this colab." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "install", - "metadata": {}, - "outputs": [], - "source": [ - "%pip install ortools" - ] - }, - { - "cell_type": "markdown", - "id": "description", - "metadata": {}, - "source": [ - "\n", - "Simple Vehicle Routing Problem (VRP).\n", - "\n", - "This is a sample using the routing library Python wrapper to solve a VRP\n", - "instance.\n", - "A description of the problem can be found here:\n", - "http://en.wikipedia.org/wiki/Vehicle_routing_problem.\n", - "\n", - "Distances are in meters.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "code", - "metadata": {}, - "outputs": [], - "source": [ - "import functools\n", - "from ortools.constraint_solver import routing_enums_pb2\n", - "from ortools.constraint_solver import pywrapcp\n", - "\n", - "\n", - "def create_data_model():\n", - " \"\"\"Stores the data for the problem.\"\"\"\n", - " data = {}\n", - " # Locations in block unit\n", - " locations_ = [\n", - " # fmt: off\n", - " (4, 4), # depot\n", - " (2, 0), (8, 0), # locations to visit\n", - " (0, 1), (1, 1),\n", - " (5, 2), (7, 2),\n", - " (3, 3), (6, 3),\n", - " (5, 5), (8, 5),\n", - " (1, 6), (2, 6),\n", - " (3, 7), (6, 7),\n", - " (0, 8), (7, 8),\n", - " # fmt: on\n", - " ]\n", - " # Compute locations in meters using the block dimension defined as follow\n", - " # Manhattan average block: 750ft x 264ft -> 228m x 80m\n", - " # here we use: 114m x 80m city block\n", - " # src: https://nyti.ms/2GDoRIe 'NY Times: Know Your distance'\n", - " data[\"locations\"] = [(l[0] * 114, l[1] * 80) for l in locations_]\n", - " data[\"num_locations\"] = len(data[\"locations\"])\n", - " data[\"num_vehicles\"] = 4\n", - " data[\"depot\"] = 0\n", - " return data\n", - "\n", - "\n", - "\n", - "def print_solution(data, manager, routing, assignment):\n", - " \"\"\"Prints solution on console.\"\"\"\n", - " print(f\"Objective: {assignment.ObjectiveValue()}\")\n", - " total_distance = 0\n", - " for vehicle_id in range(data[\"num_vehicles\"]):\n", - " index = routing.Start(vehicle_id)\n", - " plan_output = f\"Route for vehicle {vehicle_id}:\\n\"\n", - " route_distance = 0\n", - " while not routing.IsEnd(index):\n", - " plan_output += f\" {manager.IndexToNode(index)} ->\"\n", - " previous_index = index\n", - " index = assignment.Value(routing.NextVar(index))\n", - " route_distance += routing.GetArcCostForVehicle(\n", - " previous_index, index, vehicle_id\n", - " )\n", - " plan_output += f\" {manager.IndexToNode(index)}\\n\"\n", - " plan_output += f\"Distance of the route: {route_distance}m\\n\"\n", - " print(plan_output)\n", - " total_distance += route_distance\n", - " print(f\"Total Distance of all routes: {total_distance}m\")\n", - "\n", - "\n", - "\n", - "#######################\n", - "# Problem Constraints #\n", - "#######################\n", - "def manhattan_distance(position_1, position_2):\n", - " \"\"\"Computes the Manhattan distance between two points.\"\"\"\n", - " return abs(position_1[0] - position_2[0]) + abs(position_1[1] - position_2[1])\n", - "\n", - "\n", - "def create_distance_evaluator(data):\n", - " \"\"\"Creates callback to return distance between points.\"\"\"\n", - " distances_ = {}\n", - " # precompute distance between location to have distance callback in O(1)\n", - " for from_node in range(data[\"num_locations\"]):\n", - " distances_[from_node] = {}\n", - " for to_node in range(data[\"num_locations\"]):\n", - " if from_node == to_node:\n", - " distances_[from_node][to_node] = 0\n", - " else:\n", - " distances_[from_node][to_node] = manhattan_distance(\n", - " data[\"locations\"][from_node], data[\"locations\"][to_node]\n", - " )\n", - "\n", - " def distance_evaluator(manager, from_index, to_index):\n", - " \"\"\"Returns the manhattan distance between the two nodes.\"\"\"\n", - " # Convert from routing variable Index to distance matrix NodeIndex.\n", - " from_node = manager.IndexToNode(from_index)\n", - " to_node = manager.IndexToNode(to_index)\n", - " return distances_[from_node][to_node]\n", - "\n", - " return distance_evaluator\n", - "\n", - "\n", - "def add_distance_dimension(routing, distance_evaluator_index):\n", - " \"\"\"Add Global Span constraint.\"\"\"\n", - " distance = \"Distance\"\n", - " routing.AddDimension(\n", - " distance_evaluator_index,\n", - " 0, # null slack\n", - " 3000, # maximum distance per vehicle\n", - " True, # start cumul to zero\n", - " distance,\n", - " )\n", - " distance_dimension = routing.GetDimensionOrDie(distance)\n", - " # Try to minimize the max distance among vehicles.\n", - " # /!\\ It doesn't mean the standard deviation is minimized\n", - " distance_dimension.SetGlobalSpanCostCoefficient(100)\n", - "\n", - "\n", - "def main():\n", - " \"\"\"Entry point of the program.\"\"\"\n", - " # Instantiate the data problem.\n", - " data = create_data_model()\n", - "\n", - " # Create the routing index manager.\n", - " manager = pywrapcp.RoutingIndexManager(\n", - " data[\"num_locations\"], data[\"num_vehicles\"], data[\"depot\"]\n", - " )\n", - "\n", - " # Create Routing Model.\n", - " routing = pywrapcp.RoutingModel(manager)\n", - "\n", - " # Define weight of each edge\n", - " distance_evaluator_index = routing.RegisterTransitCallback(\n", - " functools.partial(create_distance_evaluator(data), manager)\n", - " )\n", - "\n", - " # Define cost of each arc.\n", - " routing.SetArcCostEvaluatorOfAllVehicles(distance_evaluator_index)\n", - "\n", - " # Add Distance constraint.\n", - " add_distance_dimension(routing, distance_evaluator_index)\n", - "\n", - " # Setting first solution heuristic.\n", - " search_parameters = pywrapcp.DefaultRoutingSearchParameters()\n", - " search_parameters.first_solution_strategy = (\n", - " routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC\n", - " )\n", - "\n", - " # Solve the problem.\n", - " solution = routing.SolveWithParameters(search_parameters)\n", - "\n", - " # Print solution on console.\n", - " if solution:\n", - " print_solution(data, manager, routing, solution)\n", - " else:\n", - " print(\"No solution found !\")\n", - "\n", - "\n", - "main()\n", - "\n" - ] - } - ], - "metadata": {}, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/notebook/linear_solver/assignment_mb.ipynb b/examples/notebook/linear_solver/assignment_mb.ipynb index a8343636d0..34b7ac0a9d 100644 --- a/examples/notebook/linear_solver/assignment_mb.ipynb +++ b/examples/notebook/linear_solver/assignment_mb.ipynb @@ -119,7 +119,7 @@ " data = pd.read_table(io.StringIO(data_str), sep=r\"\\s+\")\n", "\n", " # Create the model.\n", - " model = model_builder.ModelBuilder()\n", + " model = model_builder.Model()\n", "\n", " # Variables\n", " # x[i, j] is an array of 0-1 variables, which will be 1\n", @@ -139,7 +139,9 @@ " model.minimize(data.cost.dot(x))\n", "\n", " # Create the solver with the CP-SAT backend, and solve the model.\n", - " solver = model_builder.ModelSolver(\"sat\")\n", + " solver = model_builder.Solver(\"sat\")\n", + " if not solver.solver_is_supported():\n", + " return\n", " status = solver.solve(model)\n", "\n", " # Print solution.\n", diff --git a/examples/notebook/linear_solver/bin_packing_mb.ipynb b/examples/notebook/linear_solver/bin_packing_mb.ipynb index f1c83bc0ef..cab95ab24b 100644 --- a/examples/notebook/linear_solver/bin_packing_mb.ipynb +++ b/examples/notebook/linear_solver/bin_packing_mb.ipynb @@ -128,7 +128,7 @@ " items, bins = create_data_model()\n", "\n", " # Create the model.\n", - " model = model_builder.ModelBuilder()\n", + " model = model_builder.Model()\n", "\n", " # Variables\n", " # x[i, j] = 1 if item i is packed in bin j.\n", @@ -157,7 +157,9 @@ " model.minimize(y.sum())\n", "\n", " # Create the solver with the CP-SAT backend, and solve the model.\n", - " solver = model_builder.ModelSolver(\"sat\")\n", + " solver = model_builder.Solver(\"sat\")\n", + " if not solver.solver_is_supported():\n", + " return\n", " status = solver.solve(model)\n", "\n", " if status == model_builder.SolveStatus.OPTIMAL:\n", diff --git a/examples/notebook/linear_solver/clone_model_mb.ipynb b/examples/notebook/linear_solver/clone_model_mb.ipynb index 25b390123d..17478cf4ab 100644 --- a/examples/notebook/linear_solver/clone_model_mb.ipynb +++ b/examples/notebook/linear_solver/clone_model_mb.ipynb @@ -90,7 +90,7 @@ "\n", "def main():\n", " # Create the model.\n", - " model = model_builder.ModelBuilder()\n", + " model = model_builder.Model()\n", "\n", " # x and y are integer non-negative variables.\n", " x = model.new_int_var(0.0, math.inf, \"x\")\n", @@ -123,7 +123,9 @@ " c2_copy.add_term(z_copy, 2.0)\n", "\n", " # Create the solver with the SCIP backend, and solve the model.\n", - " solver = model_builder.ModelSolver(\"scip\")\n", + " solver = model_builder.Solver(\"scip\")\n", + " if not solver.solver_is_supported():\n", + " return\n", " status = solver.solve(model_copy)\n", "\n", " if status == model_builder.SolveStatus.OPTIMAL:\n", diff --git a/examples/notebook/linear_solver/simple_lp_program_mb.ipynb b/examples/notebook/linear_solver/simple_lp_program_mb.ipynb index a091b7d397..2d434121d8 100644 --- a/examples/notebook/linear_solver/simple_lp_program_mb.ipynb +++ b/examples/notebook/linear_solver/simple_lp_program_mb.ipynb @@ -90,7 +90,7 @@ "\n", "def main():\n", " # Create the model.\n", - " model = model_builder.ModelBuilder()\n", + " model = model_builder.Model()\n", "\n", " # Create the variables x and y.\n", " x = model.new_num_var(0.0, math.inf, \"x\")\n", @@ -110,7 +110,9 @@ " model.maximize(x + 10 * y)\n", "\n", " # Create the solver with the GLOP backend, and solve the model.\n", - " solver = model_builder.ModelSolver(\"glop\")\n", + " solver = model_builder.Solver(\"glop\")\n", + " if not solver.solver_is_supported():\n", + " return\n", " status = solver.solve(model)\n", "\n", " if status == model_builder.SolveStatus.OPTIMAL:\n", diff --git a/examples/notebook/linear_solver/simple_mip_program_mb.ipynb b/examples/notebook/linear_solver/simple_mip_program_mb.ipynb index 5171ba87bb..ef4151b636 100644 --- a/examples/notebook/linear_solver/simple_mip_program_mb.ipynb +++ b/examples/notebook/linear_solver/simple_mip_program_mb.ipynb @@ -90,7 +90,7 @@ "\n", "def main():\n", " # Create the model.\n", - " model = model_builder.ModelBuilder()\n", + " model = model_builder.Model()\n", "\n", " # x and y are integer non-negative variables.\n", " x = model.new_int_var(0.0, math.inf, \"x\")\n", @@ -110,7 +110,9 @@ " model.maximize(x + 10 * y)\n", "\n", " # Create the solver with the SCIP backend, and solve the model.\n", - " solver = model_builder.ModelSolver(\"scip\")\n", + " solver = model_builder.Solver(\"scip\")\n", + " if not solver.solver_is_supported():\n", + " return\n", " status = solver.solve(model)\n", "\n", " if status == model_builder.SolveStatus.OPTIMAL:\n",