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multiple_knapsack_sat.cc
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multiple_knapsack_sat.cc
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// Copyright 2010-2021 Google LLC
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
// [START program]
// Solves a multiple knapsack problem using the CP-SAT solver.
// [START import]
#include <map>
#include <numeric>
#include <tuple>
#include <vector>
#include "absl/strings/str_format.h"
#include "ortools/sat/cp_model.h"
// [END import]
namespace operations_research {
namespace sat {
void MultipleKnapsackSat() {
// [START data]
const std::vector<int> weights = {
{48, 30, 42, 36, 36, 48, 42, 42, 36, 24, 30, 30, 42, 36, 36}};
const std::vector<int> values = {
{10, 30, 25, 50, 35, 30, 15, 40, 30, 35, 45, 10, 20, 30, 25}};
const int num_items = static_cast<int>(weights.size());
std::vector<int> all_items(num_items);
std::iota(all_items.begin(), all_items.end(), 0);
const std::vector<int> bin_capacities = {{100, 100, 100, 100, 100}};
const int num_bins = static_cast<int>(bin_capacities.size());
std::vector<int> all_bins(num_bins);
std::iota(all_bins.begin(), all_bins.end(), 0);
// [END data]
// [START model]
CpModelBuilder cp_model;
// [END model]
// Variables.
// [START variables]
// x[i, b] = 1 if item i is packed in bin b.
std::map<std::tuple<int, int>, BoolVar> x;
for (int i : all_items) {
for (int b : all_bins) {
auto key = std::make_tuple(i, b);
x[key] = cp_model.NewBoolVar().WithName(absl::StrFormat("x_%d_%d", i, b));
}
}
// [END variables]
// Constraints.
// [START constraints]
// Each item is assigned to at most one bin.
for (int i : all_items) {
LinearExpr expr;
for (int b : all_bins) {
expr += x[std::make_tuple(i, b)];
}
cp_model.AddLessOrEqual(expr, 1);
}
// The amount packed in each bin cannot exceed its capacity.
for (int b : all_bins) {
LinearExpr bin_weight;
for (int i : all_items) {
bin_weight += x[std::make_tuple(i, b)] * weights[i];
}
cp_model.AddLessOrEqual(bin_weight, bin_capacities[b]);
}
// [END constraints]
// Objective.
// [START objective]
// Maximize total value of packed items.
LinearExpr objective;
for (int i : all_items) {
for (int b : all_bins) {
objective += x[std::make_tuple(i, b)] * values[i];
}
}
cp_model.Maximize(objective);
// [END objective]
// [START solve]
const CpSolverResponse response = Solve(cp_model.Build());
// [END solve]
// [START print_solution]
if (response.status() == CpSolverStatus::OPTIMAL ||
response.status() == CpSolverStatus::FEASIBLE) {
LOG(INFO) << "Total packed value: " << response.objective_value();
double total_weight = 0.0;
for (int b : all_bins) {
LOG(INFO) << "Bin " << b;
double bin_weight = 0.0;
double bin_value = 0.0;
for (int i : all_items) {
auto key = std::make_tuple(i, b);
if (SolutionIntegerValue(response, x[key]) > 0) {
LOG(INFO) << "Item " << i << " weight: " << weights[i]
<< " value: " << values[i];
bin_weight += weights[i];
bin_value += values[i];
}
}
LOG(INFO) << "Packed bin weight: " << bin_weight;
LOG(INFO) << "Packed bin value: " << bin_value;
total_weight += bin_weight;
}
LOG(INFO) << "Total packed weight: " << total_weight;
} else {
LOG(INFO) << "The problem does not have an optimal solution.";
}
// [END print_solution]
// Statistics.
// [START statistics]
LOG(INFO) << "Statistics";
LOG(INFO) << CpSolverResponseStats(response);
// [END statistics]
}
} // namespace sat
} // namespace operations_research
int main() {
operations_research::sat::MultipleKnapsackSat();
return EXIT_SUCCESS;
}
// [END program]