2025-01-10 11:35:44 +01:00
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// Copyright 2010-2025 Google LLC
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2011-11-16 17:31:41 +00:00
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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2014-07-09 15:18:27 +00:00
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2011-11-16 17:31:41 +00:00
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// Pickup and Delivery Problem with Time Windows.
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// The overall objective is to minimize the length of the routes delivering
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// quantities of goods between pickup and delivery locations, taking into
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// account vehicle capacities and node time windows.
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// Given a set of pairs of pickup and delivery nodes, find the set of routes
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// visiting all the nodes, such that
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// - corresponding pickup and delivery nodes are visited on the same route,
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// - the pickup node is visited before the corresponding delivery node,
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// - the quantity picked up at the pickup node is the same as the quantity
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// delivered at the delivery node,
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// - the total quantity carried by a vehicle at any time is less than its
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// capacity,
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// - each node must be visited within its time window (time range during which
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// the node is accessible).
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// The maximum number of vehicles used (i.e. the number of routes used) is
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2018-10-31 16:18:18 +01:00
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// specified in the data but can be overridden using the --pdp_force_vehicles
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2011-11-16 17:31:41 +00:00
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// flag.
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//
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// A further description of the problem can be found here:
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2013-12-12 14:43:41 +00:00
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// http://en.wikipedia.org/wiki/Vehicle_routing_problem
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2011-11-16 17:31:41 +00:00
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// http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.123.9965&rep=rep1&type=pdf.
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// Reads data in the format defined by Li & Lim
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2016-01-21 20:00:39 +01:00
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// (https://www.sintef.no/projectweb/top/pdptw/li-lim-benchmark/documentation/).
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2011-11-16 17:31:41 +00:00
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2022-09-09 16:49:24 +02:00
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#include <algorithm>
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2021-04-23 14:55:51 +02:00
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#include <cstdint>
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2024-11-15 14:42:52 +01:00
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#include <cstdlib>
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#include <limits>
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#include <memory>
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#include <optional>
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2022-09-09 16:49:24 +02:00
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#include <string>
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2018-10-31 16:18:18 +01:00
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#include <utility>
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2011-11-16 17:31:41 +00:00
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#include <vector>
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2024-11-15 14:42:52 +01:00
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#include "absl/algorithm/container.h"
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2025-02-25 16:03:40 +01:00
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#include "absl/base/log_severity.h"
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2021-01-14 10:48:19 +01:00
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#include "absl/flags/flag.h"
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2024-11-15 14:42:52 +01:00
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#include "absl/log/check.h"
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2025-02-25 16:03:40 +01:00
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#include "absl/log/globals.h"
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2025-03-04 21:10:09 +01:00
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#include "absl/log/log.h"
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2018-10-31 16:18:18 +01:00
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#include "absl/strings/str_format.h"
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2024-11-15 14:42:52 +01:00
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#include "absl/strings/string_view.h"
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2018-10-31 16:18:18 +01:00
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#include "google/protobuf/text_format.h"
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2022-02-25 09:47:52 +01:00
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#include "ortools/base/init_google.h"
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2017-04-26 17:30:25 +02:00
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#include "ortools/base/mathutil.h"
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2019-01-05 12:34:50 +01:00
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#include "ortools/base/timer.h"
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2017-04-26 17:30:25 +02:00
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#include "ortools/constraint_solver/routing.h"
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#include "ortools/constraint_solver/routing_enums.pb.h"
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2018-10-31 16:18:18 +01:00
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#include "ortools/constraint_solver/routing_index_manager.h"
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#include "ortools/constraint_solver/routing_parameters.h"
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#include "ortools/constraint_solver/routing_parameters.pb.h"
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2024-11-17 17:29:23 +01:00
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#include "ortools/routing/parsers/lilim_parser.h"
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#include "ortools/routing/parsers/simple_graph.h"
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2011-11-16 17:31:41 +00:00
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2020-10-23 11:50:14 +02:00
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ABSL_FLAG(std::string, pdp_file, "",
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"File containing the Pickup and Delivery Problem to solve.");
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ABSL_FLAG(int, pdp_force_vehicles, 0,
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"Force the number of vehicles used (maximum number of routes.");
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ABSL_FLAG(bool, reduce_vehicle_cost_model, true,
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"Overrides the homonymous field of "
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"DefaultRoutingModelParameters().");
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ABSL_FLAG(std::string, routing_search_parameters,
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"first_solution_strategy:ALL_UNPERFORMED",
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"Text proto RoutingSearchParameters (possibly partial) that will "
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"override the DefaultRoutingSearchParameters()");
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2024-11-15 14:42:52 +01:00
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ABSL_FLAG(std::string, routing_model_parameters, "",
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"Text proto RoutingModelParameters (possibly partial) that will "
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"override the DefaultRoutingModelParameters()");
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2011-11-16 17:31:41 +00:00
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namespace operations_research {
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2018-10-31 16:18:18 +01:00
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// Returns the list of variables to use for the Tabu metaheuristic.
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// The current list is:
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// - Total cost of the solution,
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// - Number of used vehicles,
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// - Total schedule duration.
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// TODO(user): add total waiting time.
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2020-10-29 14:25:39 +01:00
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std::vector<IntVar*> GetTabuVars(std::vector<IntVar*> existing_vars,
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RoutingModel* routing) {
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2020-10-29 14:25:39 +01:00
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Solver* const solver = routing->solver();
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std::vector<IntVar*> vars(std::move(existing_vars));
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2018-10-31 16:18:18 +01:00
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vars.push_back(routing->CostVar());
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2020-10-29 14:25:39 +01:00
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IntVar* used_vehicles = solver->MakeIntVar(0, routing->vehicles());
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std::vector<IntVar*> is_used_vars;
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2018-10-31 16:18:18 +01:00
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// Number of vehicle used
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is_used_vars.reserve(routing->vehicles());
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for (int v = 0; v < routing->vehicles(); v++) {
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is_used_vars.push_back(solver->MakeIsDifferentCstVar(
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routing->NextVar(routing->Start(v)), routing->End(v)));
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}
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solver->AddConstraint(
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solver->MakeEquality(solver->MakeSum(is_used_vars), used_vehicles));
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vars.push_back(used_vehicles);
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return vars;
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2011-11-16 17:31:41 +00:00
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}
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2024-11-15 14:42:52 +01:00
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// Scaling factor from callback.
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template <typename C>
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double ComputeScalingFactorFromCallback(const C& callback, int size) {
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double max_value = 0;
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for (int i = 0; i < size; ++i) {
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for (int j = 0; j < size; ++j) {
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max_value = std::max(max_value, callback(i, j));
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2011-11-16 17:31:41 +00:00
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}
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}
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const double max_scaled_total_distance =
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(1LL << (std::numeric_limits<double>::digits - 2)) - 1;
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const double max_scaled_distance = max_scaled_total_distance / size;
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return max_scaled_distance / max_value;
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2011-11-16 17:31:41 +00:00
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}
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2024-11-17 17:29:23 +01:00
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void SetupModel(const routing::LiLimParser& parser,
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const RoutingIndexManager& manager, RoutingModel* model,
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RoutingSearchParameters* search_parameters) {
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2024-11-15 14:42:52 +01:00
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const int64_t kPenalty = 100000000;
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const int64_t kFixedCost = 100000;
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const int num_nodes = parser.NumberOfNodes();
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const int64_t horizon =
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2024-11-17 17:29:23 +01:00
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absl::c_max_element(parser.time_windows(),
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[](const routing::SimpleTimeWindow<int64_t>& a,
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const routing::SimpleTimeWindow<int64_t>& b) {
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return a.end < b.end;
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})
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2024-11-15 14:42:52 +01:00
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->end;
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const double scaling_factor = ComputeScalingFactorFromCallback(
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[&parser](int64_t i, int64_t j) -> double {
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const int depot = parser.Depot();
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double fixed_cost = 0;
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if (i == depot && j != depot) {
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fixed_cost = kFixedCost;
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} else if (i == j && i != depot) {
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return kPenalty;
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}
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return fixed_cost + parser.GetTravelTime(i, j);
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},
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manager.num_nodes());
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search_parameters->set_log_cost_scaling_factor(1.0 / scaling_factor);
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const int vehicle_cost = model->RegisterTransitCallback(
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[&parser, &manager, scaling_factor](int64_t i, int64_t j) {
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2025-02-25 16:03:40 +01:00
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return MathUtil::Round<int64_t>(
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2024-11-15 14:42:52 +01:00
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scaling_factor *
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parser.GetDistance(manager.IndexToNode(i).value(),
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manager.IndexToNode(j).value()));
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2020-10-22 23:36:58 +02:00
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});
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2024-11-15 14:42:52 +01:00
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model->SetArcCostEvaluatorOfAllVehicles(vehicle_cost);
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model->SetFixedCostOfAllVehicles(
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2025-02-25 16:03:40 +01:00
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MathUtil::Round<int64_t>(kFixedCost * scaling_factor));
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2024-11-15 14:42:52 +01:00
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RoutingTransitCallback2 demand_evaluator =
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[&parser, &manager](int64_t from_index, int64_t /*to_index*/) {
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return parser.demands()[manager.IndexToNode(from_index).value()];
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};
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model->AddDimension(model->RegisterTransitCallback(demand_evaluator), 0,
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parser.capacity(), /*fix_start_cumul_to_zero=*/true,
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"demand");
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RoutingTransitCallback2 time_evaluator = [&parser, &manager, scaling_factor](
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int64_t from_index,
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int64_t to_index) {
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2025-02-25 16:03:40 +01:00
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int64_t value = MathUtil::Round<int64_t>(
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2024-11-15 14:42:52 +01:00
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scaling_factor *
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parser.GetTravelTime(manager.IndexToNode(from_index).value(),
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manager.IndexToNode(to_index).value()));
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return value;
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2020-10-22 23:36:58 +02:00
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};
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2024-11-15 14:42:52 +01:00
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model->AddDimension(model->RegisterTransitCallback(time_evaluator),
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MathUtil::FastInt64Round(scaling_factor * horizon),
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MathUtil::FastInt64Round(scaling_factor * horizon),
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/*fix_start_cumul_to_zero=*/true, "time");
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const RoutingDimension& time_dimension = model->GetDimensionOrDie("time");
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Solver* const solver = model->solver();
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2018-10-31 16:18:18 +01:00
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for (int node = 0; node < num_nodes; ++node) {
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2021-04-02 14:58:16 +02:00
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const int64_t index =
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2018-10-31 16:18:18 +01:00
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manager.NodeToIndex(RoutingIndexManager::NodeIndex(node));
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2024-11-15 14:42:52 +01:00
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if (const std::optional<int> delivery = parser.GetDelivery(node);
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delivery.has_value()) {
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const int64_t delivery_index =
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manager.NodeToIndex(RoutingIndexManager::NodeIndex(delivery.value()));
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2016-10-07 17:40:41 +02:00
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solver->AddConstraint(solver->MakeEquality(
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2024-11-15 14:42:52 +01:00
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model->VehicleVar(index), model->VehicleVar(delivery_index)));
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2016-10-07 17:40:41 +02:00
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solver->AddConstraint(
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solver->MakeLessOrEqual(time_dimension.CumulVar(index),
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time_dimension.CumulVar(delivery_index)));
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2024-11-15 14:42:52 +01:00
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model->AddPickupAndDelivery(index, delivery_index);
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2011-11-16 17:31:41 +00:00
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}
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2020-10-29 14:25:39 +01:00
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IntVar* const cumul = time_dimension.CumulVar(index);
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2024-11-17 17:29:23 +01:00
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const routing::SimpleTimeWindow<int64_t>& window =
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parser.time_windows()[node];
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2025-02-25 16:03:40 +01:00
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cumul->SetMin(MathUtil::Round<int64_t>(scaling_factor * window.start));
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cumul->SetMax(MathUtil::Round<int64_t>(scaling_factor * window.end));
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2018-10-31 16:18:18 +01:00
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}
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2024-11-15 14:42:52 +01:00
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if (search_parameters->local_search_metaheuristic() ==
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2018-10-31 16:18:18 +01:00
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LocalSearchMetaheuristic::GENERIC_TABU_SEARCH) {
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// Create variable for the total schedule time of the solution.
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// This will be used as one of the Tabu criteria.
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// This is done here and not in GetTabuVarsCallback as it requires calling
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// AddVariableMinimizedByFinalizer and this method must be called early.
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2020-10-29 14:25:39 +01:00
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std::vector<IntVar*> end_cumuls;
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std::vector<IntVar*> start_cumuls;
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2024-11-15 14:42:52 +01:00
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for (int i = 0; i < model->vehicles(); ++i) {
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end_cumuls.push_back(time_dimension.CumulVar(model->End(i)));
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start_cumuls.push_back(time_dimension.CumulVar(model->Start(i)));
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2018-10-31 16:18:18 +01:00
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}
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2020-10-29 14:25:39 +01:00
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IntVar* total_time = solver->MakeIntVar(0, 99999999, "total");
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2018-10-31 16:18:18 +01:00
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solver->AddConstraint(solver->MakeEquality(
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solver->MakeDifference(solver->MakeSum(end_cumuls),
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solver->MakeSum(start_cumuls)),
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total_time));
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2024-11-15 14:42:52 +01:00
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model->AddVariableMinimizedByFinalizer(total_time);
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2018-10-31 16:18:18 +01:00
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RoutingModel::GetTabuVarsCallback tabu_var_callback =
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2020-10-29 14:25:39 +01:00
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[total_time](RoutingModel* model) {
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2020-10-22 23:36:58 +02:00
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return GetTabuVars({total_time}, model);
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};
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2024-11-15 14:42:52 +01:00
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model->SetTabuVarsCallback(tabu_var_callback);
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2011-11-16 17:31:41 +00:00
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}
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2018-10-31 16:18:18 +01:00
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2011-11-16 17:31:41 +00:00
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// Adding penalty costs to allow skipping orders.
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2024-11-15 14:42:52 +01:00
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for (RoutingIndexManager::NodeIndex order(1); order < model->nodes();
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2018-10-31 16:18:18 +01:00
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++order) {
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2021-04-02 14:58:16 +02:00
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std::vector<int64_t> orders(1, manager.NodeToIndex(order));
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2024-11-15 14:42:52 +01:00
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model->AddDisjunction(orders,
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MathUtil::Round<int64_t>(scaling_factor * kPenalty));
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}
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}
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// Outputs a solution to the current model in a string.
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std::string VerboseOutput(const RoutingModel& model,
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const RoutingIndexManager& manager,
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const Assignment& assignment,
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2024-11-17 17:29:23 +01:00
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const routing::LiLimParser& parser,
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double scaling_factor) {
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2024-11-15 14:42:52 +01:00
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std::string output;
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const RoutingDimension& time_dimension = model.GetDimensionOrDie("time");
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const RoutingDimension& load_dimension = model.GetDimensionOrDie("demand");
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for (int i = 0; i < model.vehicles(); ++i) {
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absl::StrAppendFormat(&output, "Vehicle %d: ", i);
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int64_t index = model.Start(i);
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if (model.IsEnd(assignment.Value(model.NextVar(index)))) {
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output.append("empty");
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} else {
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while (!model.IsEnd(index)) {
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absl::StrAppendFormat(&output, "%d ",
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manager.IndexToNode(index).value());
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const IntVar* vehicle = model.VehicleVar(index);
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absl::StrAppendFormat(&output, "Vehicle(%d) ",
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assignment.Value(vehicle));
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const IntVar* arrival = time_dimension.CumulVar(index);
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absl::StrAppendFormat(
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&output, "Time(%d..%d) ",
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2025-02-25 16:03:40 +01:00
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MathUtil::Round<int64_t>(assignment.Min(arrival) * scaling_factor),
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MathUtil::Round<int64_t>(assignment.Max(arrival) * scaling_factor));
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2024-11-15 14:42:52 +01:00
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const IntVar* load = load_dimension.CumulVar(index);
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absl::StrAppendFormat(&output, "Load(%d..%d) ", assignment.Min(load),
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assignment.Max(load));
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const int64_t next_index = assignment.Value(model.NextVar(index));
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absl::StrAppendFormat(
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&output, "Transit(%f) ",
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parser.GetTravelTime(manager.IndexToNode(index).value(),
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manager.IndexToNode(next_index).value()));
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index = next_index;
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}
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output.append("Route end ");
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const IntVar* vehicle = model.VehicleVar(index);
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absl::StrAppendFormat(&output, "Vehicle(%d) ", assignment.Value(vehicle));
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const IntVar* arrival = time_dimension.CumulVar(index);
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absl::StrAppendFormat(
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&output, "Time(%d..%d) ",
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2025-02-25 16:03:40 +01:00
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MathUtil::Round<int64_t>(assignment.Min(arrival) * scaling_factor),
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MathUtil::Round<int64_t>(assignment.Max(arrival) * scaling_factor));
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2024-11-15 14:42:52 +01:00
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const IntVar* load = load_dimension.CumulVar(index);
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absl::StrAppendFormat(&output, "Load(%d..%d) ", assignment.Min(load),
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assignment.Max(load));
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}
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output.append("\n");
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}
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return output;
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}
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// Builds and solves a model from a file in the format defined by Li & Lim
|
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|
|
// (https://www.sintef.no/projectweb/top/pdptw/li-lim-benchmark/documentation/).
|
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|
|
bool LoadAndSolve(absl::string_view pdp_file,
|
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const RoutingModelParameters& model_parameters,
|
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|
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RoutingSearchParameters& search_parameters) {
|
2024-11-17 17:29:23 +01:00
|
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|
routing::LiLimParser parser;
|
2024-11-15 14:42:52 +01:00
|
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|
if (!parser.LoadFile(pdp_file)) {
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return false;
|
2011-11-16 17:31:41 +00:00
|
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|
}
|
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|
2024-11-15 14:42:52 +01:00
|
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|
// Build pickup and delivery model.
|
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|
|
const int num_nodes = parser.NumberOfNodes();
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const int num_vehicles = parser.NumberOfVehicles();
|
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|
const RoutingIndexManager::NodeIndex depot =
|
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|
|
RoutingIndexManager::NodeIndex(parser.Depot());
|
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|
|
RoutingIndexManager manager(num_nodes, num_vehicles, depot);
|
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|
|
RoutingModel model(manager, model_parameters);
|
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|
|
|
SetupModel(parser, manager, &model, &search_parameters);
|
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|
|
|
|
2011-11-16 17:31:41 +00:00
|
|
|
// Solve pickup and delivery problem.
|
2018-10-31 16:18:18 +01:00
|
|
|
SimpleCycleTimer timer;
|
|
|
|
|
timer.Start();
|
2024-11-15 14:42:52 +01:00
|
|
|
const Assignment* assignment = model.SolveWithParameters(search_parameters);
|
2018-10-31 16:18:18 +01:00
|
|
|
timer.Stop();
|
2024-11-15 14:42:52 +01:00
|
|
|
LOG(INFO) << model.solver()->LocalSearchProfile();
|
2017-04-26 17:30:25 +02:00
|
|
|
if (nullptr != assignment) {
|
2024-11-15 14:42:52 +01:00
|
|
|
const double scaling_factor = search_parameters.log_cost_scaling_factor();
|
|
|
|
|
LOG(INFO) << VerboseOutput(model, manager, *assignment, parser,
|
|
|
|
|
scaling_factor);
|
|
|
|
|
const int64_t cost = assignment->ObjectiveValue();
|
|
|
|
|
LOG(INFO) << absl::StrFormat("Cost: %f", cost * scaling_factor);
|
|
|
|
|
int num_used_vehicles = 0;
|
|
|
|
|
int64_t total_fixed_cost = 0;
|
|
|
|
|
for (int v = 0; v < model.vehicles(); v++) {
|
|
|
|
|
if (model.IsVehicleUsed(*assignment, v)) {
|
|
|
|
|
num_used_vehicles++;
|
|
|
|
|
total_fixed_cost += model.GetFixedCostOfVehicle(v);
|
|
|
|
|
}
|
|
|
|
|
}
|
2018-10-31 16:18:18 +01:00
|
|
|
int skipped_nodes = 0;
|
2024-11-15 14:42:52 +01:00
|
|
|
int64_t total_penalty = 0;
|
|
|
|
|
for (int node = 0; node < model.Size(); node++) {
|
|
|
|
|
if (!model.IsEnd(node) && !model.IsStart(node) &&
|
|
|
|
|
assignment->Value(model.NextVar(node)) == node) {
|
2018-10-31 16:18:18 +01:00
|
|
|
skipped_nodes++;
|
2024-11-15 14:42:52 +01:00
|
|
|
for (RoutingModel::DisjunctionIndex disjunction :
|
|
|
|
|
model.GetDisjunctionIndices(node)) {
|
|
|
|
|
total_penalty += model.GetDisjunctionPenalty(disjunction);
|
|
|
|
|
}
|
2018-10-31 16:18:18 +01:00
|
|
|
}
|
|
|
|
|
}
|
2024-11-15 14:42:52 +01:00
|
|
|
LOG(INFO) << absl::StrFormat(
|
|
|
|
|
"Distance: %.2f",
|
|
|
|
|
(cost - total_fixed_cost - total_penalty) * scaling_factor);
|
2018-10-31 16:18:18 +01:00
|
|
|
LOG(INFO) << "Number of skipped nodes: " << skipped_nodes;
|
|
|
|
|
LOG(INFO) << "Number of used vehicles: " << num_used_vehicles;
|
|
|
|
|
LOG(INFO) << "Time: " << timer.Get();
|
2011-11-16 17:31:41 +00:00
|
|
|
return true;
|
|
|
|
|
}
|
|
|
|
|
return false;
|
|
|
|
|
}
|
|
|
|
|
|
2020-10-22 23:36:58 +02:00
|
|
|
} // namespace operations_research
|
2011-11-16 17:31:41 +00:00
|
|
|
|
2020-10-29 14:25:39 +01:00
|
|
|
int main(int argc, char** argv) {
|
2025-02-25 16:03:40 +01:00
|
|
|
absl::SetStderrThreshold(absl::LogSeverityAtLeast::kInfo);
|
2022-02-25 09:47:52 +01:00
|
|
|
InitGoogle(argv[0], &argc, &argv, true);
|
2018-10-31 16:18:18 +01:00
|
|
|
operations_research::RoutingModelParameters model_parameters =
|
|
|
|
|
operations_research::DefaultRoutingModelParameters();
|
|
|
|
|
model_parameters.set_reduce_vehicle_cost_model(
|
2020-10-21 00:21:54 +02:00
|
|
|
absl::GetFlag(FLAGS_reduce_vehicle_cost_model));
|
2024-11-15 14:42:52 +01:00
|
|
|
CHECK(google::protobuf::TextFormat::MergeFromString(
|
|
|
|
|
absl::GetFlag(FLAGS_routing_model_parameters), &model_parameters));
|
2018-10-31 16:18:18 +01:00
|
|
|
operations_research::RoutingSearchParameters search_parameters =
|
|
|
|
|
operations_research::DefaultRoutingSearchParameters();
|
|
|
|
|
CHECK(google::protobuf::TextFormat::MergeFromString(
|
2020-10-21 00:21:54 +02:00
|
|
|
absl::GetFlag(FLAGS_routing_search_parameters), &search_parameters));
|
2024-11-17 17:29:23 +01:00
|
|
|
if (!operations_research::LoadAndSolve(absl::GetFlag(FLAGS_pdp_file),
|
|
|
|
|
model_parameters, search_parameters)) {
|
2020-10-21 00:21:54 +02:00
|
|
|
LOG(INFO) << "Error solving " << absl::GetFlag(FLAGS_pdp_file);
|
2011-11-16 17:31:41 +00:00
|
|
|
}
|
2018-11-07 09:52:37 +01:00
|
|
|
return EXIT_SUCCESS;
|
2011-11-16 17:31:41 +00:00
|
|
|
}
|
