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capacitated_vehicle_routing_main.cpp
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/**
* Capacitated vehicle routing problem
*
* Problem description:
* See https://github.com/fontanf/orproblems/blob/main/orproblems/capacitated_vehicle_routing.hpp
*
* The linear programming formulation of the problem based on Dantzig–Wolfe
* decomposition is written as follows:
*
* Variables:
* - yᵏ ∈ {0, 1} representing a feasible route.
* yᵏ = 1 iff the corresponding route is selected.
* dᵏ the length of route yᵏ.
* xⱼᵏ = 1 iff customer j is visited in route yᵏ.
*
* Program:
*
* min ∑ₖ dᵏ yᵏ
*
* 1 <= ∑ₖ xⱼᵏ yᵏ <= 1 for all customers j
* (each customer is visited exactly once)
* Dual variables: vⱼ
*
* The pricing problem consists in finding a variable of negative reduced cost.
* The reduced cost of a variable yᵏ is given by:
* rc(yᵏ) = dᵏ - ∑ⱼ xⱼᵏ vⱼ
*
* Therefore, finding a variable of minimum reduced cost reduces to solving
* an Elementary Shortest Path Problems with Resource Constraints.
*
*/
#include "read_args.hpp"
#include "pricingsolver/espprc.hpp"
#include "columngenerationsolver/commons.hpp"
#include "orproblems/routing/capacitated_vehicle_routing.hpp"
#include "treesearchsolver/iterative_beam_search.hpp"
#include "optimizationtools/utils/utils.hpp"
using namespace orproblems::capacitated_vehicle_routing;
using Value = columngenerationsolver::Value;
using ColIdx = columngenerationsolver::ColIdx;
using RowIdx = columngenerationsolver::RowIdx;
template <typename Distances>
class PricingSolver: public columngenerationsolver::PricingSolver
{
public:
PricingSolver(
const Instance& instance,
const Distances& distances):
instance_(instance),
distances_(distances),
visited_customers_(instance.number_of_locations(), 0)
{ }
virtual inline std::vector<std::shared_ptr<const columngenerationsolver::Column>> initialize_pricing(
const std::vector<std::pair<std::shared_ptr<const columngenerationsolver::Column>, Value>>& fixed_columns);
virtual inline PricingOutput solve_pricing(
const std::vector<Value>& duals);
void set_beam_search_size_of_the_queue(treesearchsolver::NodeId bs_size_of_the_queue) { bs_size_of_the_queue_ = bs_size_of_the_queue; }
private:
/** Instance. */
const Instance& instance_;
/** Distances. */
const Distances& distances_;
std::vector<Demand> visited_customers_;
std::vector<LocationId> espp2vrp_;
treesearchsolver::NodeId bs_size_of_the_queue_ = 128;
};
template <typename Distances>
inline columngenerationsolver::Model get_model(
const Distances& distances,
const Instance& instance)
{
columngenerationsolver::Model model;
model.objective_sense = optimizationtools::ObjectiveDirection::Minimize;
// Rows.
for (LocationId location_id = 1;
location_id < instance.number_of_locations();
++location_id) {
columngenerationsolver::Row row;
row.lower_bound = 1;
row.upper_bound = 1;
row.coefficient_lower_bound = 0;
row.coefficient_upper_bound = 1;
model.rows.push_back(row);
}
// Pricing solver.
model.pricing_solver = std::unique_ptr<columngenerationsolver::PricingSolver>(
new PricingSolver<Distances>(instance, distances));
return model;
}
template <typename Distances>
std::vector<std::shared_ptr<const columngenerationsolver::Column>> PricingSolver<Distances>::initialize_pricing(
const std::vector<std::pair<std::shared_ptr<const columngenerationsolver::Column>, Value>>& fixed_columns)
{
std::fill(visited_customers_.begin(), visited_customers_.end(), 0);
for (const auto& p: fixed_columns) {
const columngenerationsolver::Column& column = *(p.first);
Value value = p.second;
if (value < 0.5)
continue;
for (const columngenerationsolver::LinearTerm& element: column.elements) {
if (element.coefficient < 0.5)
continue;
// row_index + 1 since there is not constraint for location 0 which
// is the depot.
visited_customers_[element.row + 1] = 1;
}
}
return {};
}
struct ColumnExtra
{
std::vector<LocationId> route;
};
template <typename Distances>
typename PricingSolver<Distances>::PricingOutput PricingSolver<Distances>::solve_pricing(
const std::vector<Value>& duals)
{
PricingOutput output;
// Build subproblem instance.
espp2vrp_.clear();
espp2vrp_.push_back(0);
for (LocationId location_id = 1;
location_id < instance_.number_of_locations();
++location_id) {
if (visited_customers_[location_id] == 1)
continue;
espp2vrp_.push_back(location_id);
}
LocationId espp_number_of_locations = espp2vrp_.size();
if (espp_number_of_locations == 1)
return output;
columngenerationsolver::espprc::InstanceBuilder espp_instance_builder(espp_number_of_locations);
for (LocationId espp_location_id = 0;
espp_location_id < espp_number_of_locations;
++espp_location_id) {
LocationId location_id = espp2vrp_[espp_location_id];
espp_instance_builder.set_demand(
espp_location_id,
instance_.demand(location_id));
espp_instance_builder.set_profit(
espp_location_id,
((location_id != 0)? duals[location_id - 1]: 0));
for (LocationId espp_location_id_2 = 0;
espp_location_id_2 < espp_number_of_locations;
++espp_location_id_2) {
if (espp_location_id_2 == espp_location_id)
continue;
LocationId location_id_2 = espp2vrp_[espp_location_id_2];
espp_instance_builder.set_distance(
espp_location_id,
espp_location_id_2,
distances_.distance(location_id, location_id_2));
}
}
columngenerationsolver::espprc::Instance espp_instance = espp_instance_builder.build();
// Solve subproblem instance.
columngenerationsolver::espprc::BranchingScheme branching_scheme(espp_instance);
treesearchsolver::IterativeBeamSearchParameters<columngenerationsolver::espprc::BranchingScheme> espp_parameters;
espp_parameters.maximum_size_of_the_solution_pool = 1;
espp_parameters.minimum_size_of_the_queue = bs_size_of_the_queue_;
espp_parameters.maximum_size_of_the_queue = bs_size_of_the_queue_;
espp_parameters.verbosity_level = 0;
auto espp_output = treesearchsolver::iterative_beam_search(
branching_scheme, espp_parameters);
// Retrieve column.
for (const std::shared_ptr<columngenerationsolver::espprc::BranchingScheme::Node>& node:
espp_output.solution_pool.solutions()) {
if (node->last_location_id == 0)
continue;
std::vector<LocationId> solution; // Without the depot.
for (auto node_tmp = node;
node_tmp->parent != nullptr;
node_tmp = node_tmp->parent) {
solution.push_back(espp2vrp_[node_tmp->last_location_id]);
}
std::reverse(solution.begin(), solution.end());
columngenerationsolver::Column column;
LocationId location_id_prev = 0;
for (LocationId location_id: solution) {
columngenerationsolver::LinearTerm element;
element.row = location_id - 1;
element.coefficient = 1;
column.elements.push_back(element);
column.objective_coefficient += distances_.distance(location_id_prev, location_id);
location_id_prev = location_id;
}
column.objective_coefficient += distances_.distance(location_id_prev, 0);
ColumnExtra extra {solution};
column.extra = std::shared_ptr<void>(new ColumnExtra(extra));
output.columns.push_back(std::shared_ptr<const columngenerationsolver::Column>(new columngenerationsolver::Column(column)));
}
return output;
}
inline void write_solution(
const columngenerationsolver::Solution& solution,
const std::string& certificate_path)
{
std::ofstream file(certificate_path);
if (!file.good()) {
throw std::runtime_error(
"Unable to open file \"" + certificate_path + "\".");
}
file << solution.columns().size() << std::endl;
for (auto colval: solution.columns()) {
std::shared_ptr<ColumnExtra> extra
= std::static_pointer_cast<ColumnExtra>(colval.first->extra);
file << extra->route.size() << " ";
for (LocationId location_id: extra->route)
file << " " << location_id;
file << std::endl;
}
}
int main(int argc, char *argv[])
{
// Setup options.
boost::program_options::options_description desc = columngenerationsolver::setup_args();
desc.add_options()
//("guide,g", boost::program_options::value<GuideId>(), "")
;
boost::program_options::variables_map vm;
boost::program_options::store(boost::program_options::parse_command_line(argc, argv, desc), vm);
if (vm.count("help")) {
std::cout << desc << std::endl;;
throw "";
}
try {
boost::program_options::notify(vm);
} catch (const boost::program_options::required_option& e) {
std::cout << desc << std::endl;;
throw "";
}
// Create instance.
InstanceBuilder instance_builder;
instance_builder.read(
vm["input"].as<std::string>(),
vm["format"].as<std::string>());
const Instance instance = instance_builder.build();
// Create model.
columngenerationsolver::Model model = FUNCTION_WITH_DISTANCES(
get_model,
instance.distances(),
instance);
// Solve.
auto output = run(model, write_solution, vm);
// Run checker.
if (vm.count("certificate")
&& vm["print-checker"].as<int>() > 0) {
std::cout << std::endl
<< "Checker" << std::endl
<< "-------" << std::endl;
instance.check(
vm["certificate"].as<std::string>(),
std::cout,
vm["print-checker"].as<int>());
}
return 0;
}