Vehicle routing problem¶
Principles learned¶
- Add multiple list decision variables
- Add a partition constraint
- Use a lambda expression to compute a sum with a variable number of terms
- Define a sequence of expressions
- Access a multi-dimensional array with an “at” operator
- Add multiple objectives
Problem¶
In the capacitated vehicle routing problem (CVRP), a fleet of delivery vehicles with uniform capacity must service customers with known demand for a single commodity. The vehicles start and end their routes at a common depot. Each customer can only be served by one vehicle. The objectives are to minimize the fleet size and assign a sequence of customers to each truck of the fleet minimizing the total distance traveled such that all customers are served and the total demand served by each truck does not exceed its capacity.
Download the exampleData¶
The instances provided come from the Augerat et al. Set A instances. They follow the TSPLib format.
The format of the data files is as follows:
- The number of nodes follows the keyword
DIMENSION
(there is one warehouse so the number of customers is the number of nodes minus 1). - The truck capacity follows the keyword
CAPACITY
. - The edge type follows
EDGE_WEIGHT_TYPE
. Note that in our model the only edge type accepted isEUC_2D
. - After the keyword
NODE_COORD_SECTION
, for each node is listed its id and the corresponding x and y coordinates. - After the keyword
DEMAND_SECTION
, for each node is listed its id and the corresponding demand. - Warehouses are listed after the keyword
DEPOT_SECTION
. Note that in our model only one warehouse is accepted.
The number of available trucks can be defined through the command line. If not,
it is deduced from the instance file name that follow this pattern: A-nXX-
kNBTRUCKS.vrp
.
Program¶
This LocalSolver model defines a route for each truck k as a list
variable (customersSequences[k]
). It
corresponds to the sequence of customers visited. To ensure that all customers
must be served, all the list variables are constrained to form a partition,
thanks to the “partition” operator.
The number of trucks used for the fleet is defined by the number of trucks that serve at least one customer (if their list variable has at least one element). The definition of these expressions is really straightforward thanks to the “count” and the “greater than” operators.
The quantity delivered on each visit is the demand on the node of this visit.
This expression is just defined with an “at” operator to access the array
demands
. The total quantity delivered by each truck is computed with a
function to apply the sum
operator over all customers visited by a truck. Note that the number of terms
in this sum varies automatically with the size of the list. This quantity is
constrained to be lower than the truck capacity.
For each truck, the distance traveled from the visit n-1 to the visit n is
accessed with an “at” operator to the multi-dimensional array
distanceMatrix
, with the first index. Here again we use a
function to sum distances along
each route.
The two objectives are defined in lexicographical order: we first minimize the number of trucks used, and then we minimize the total distance traveled by all the trucks.
If you are interested in the classical variant where time-windows are defined for each customer, you can now study our CVRPTW model.
- Execution:
- localsolver cvrp.lsp inFileName=instances/A-n32-k5.vrp [lsTimeLimit=] [solFileName=]
/********** cvrp.lsp **********/
use io;
/* Reads instance data. The input files follow the "Augerat" format. */
function input() {
usage = "\nUsage: localsolver cvrp.lsp " +
"inFileName=inputFile [solFileName=outputFile] [lsTimeLimit=timeLimit] [nbTrucks=value]\n";
if (inFileName == nil) throw usage;
readInputCvrp();
// The number of trucks is usually given in the name of the file
// nbTrucks can also be given in command line
if (nbTrucks == nil) nbTrucks = getNbTrucks();
// Compute distance matrix
computeDistanceMatrix();
}
/* Declares the optimization model. */
function model() {
// Sequence of customers visited by each truck.
customersSequences[k in 1..nbTrucks] <- list(nbCustomers);
// All customers must be visited by the trucks
constraint partition[k in 1..nbTrucks](customersSequences[k]);
for [k in 1..nbTrucks] {
local sequence <- customersSequences[k];
local c <- count(sequence);
// A truck is used if it visits at least one customer
trucksUsed[k] <- c > 0;
// The quantity needed in each route must not exceed the truck capacity
routeQuantity <- sum(0..c-1, i => demands[sequence[i]]);
constraint routeQuantity <= truckCapacity;
// Distance traveled by truck k
routeDistances[k] <- sum(1..c-1, i => distanceMatrix[sequence[i - 1]][sequence[i]])
+ (c > 0 ? (distanceWarehouse[sequence[0]] + distanceWarehouse[sequence[c - 1]]) : 0);
}
nbTrucksUsed <- sum[k in 1..nbTrucks](trucksUsed[k]);
// Total distance traveled
totalDistance <- sum[k in 1..nbTrucks](routeDistances[k]);
// Objective: minimize the number of trucks used, then minimize the distance traveled
minimize nbTrucksUsed;
minimize totalDistance;
}
/* Parameterizes the solver. */
function param() {
if (lsTimeLimit == nil) lsTimeLimit = 20;
}
/* Writes the solution in a file with the following format:
- number of trucks used and total distance
- for each truck the nodes visited (omitting the start/end at the depot) */
function output() {
if (solFileName == nil) return;
local outfile = io.openWrite(solFileName);
outfile.println(nbTrucksUsed.value, " ", totalDistance.value);
for [k in 1..nbTrucks] {
if (trucksUsed[k].value != 1) continue;
// Values in sequence are in [0..nbCustomers-1].
// +2 is to put it back in [2..nbCustomers+1]
// as in the data files (1 being the depot)
for [customer in customersSequences[k].value]
outfile.print(customer + 2, " ");
outfile.println();
}
}
function readInputCvrp() {
local inFile = io.openRead(inFileName);
local nbNodes = 0;
while (true) {
local str = inFile.readString();
if (str.startsWith("DIMENSION")) {
if (!str.endsWith(":")) str = inFile.readString();
nbNodes = inFile.readInt();
nbCustomers = nbNodes - 1;
} else if ((str.startsWith("CAPACITY"))) {
if (!str.endsWith(":")) str = inFile.readString();
truckCapacity = inFile.readInt();
} else if ((str.startsWith("EDGE_WEIGHT_TYPE"))) {
if (!str.endsWith(":")) str = inFile.readString();
local weightType = inFile.readString();
if (weightType != "EUC_2D") throw ("Edge Weight Type " + weightType + " is not supported (only EUC_2D)");
} else if (str.startsWith("NODE_COORD_SECTION")) {
break;
} else {
local dump = inFile.readln();
}
}
//nodeX and nodeY are indexed by original data indices (1 for depot)
for[n in 1..nbNodes] {
if (n != inFile.readInt()) throw "Unexpected index";
nodesX[n] = round(inFile.readDouble());
nodesY[n] = round(inFile.readDouble());
}
dump = inFile.readln();
if (!dump.startsWith("DEMAND_SECTION")) throw "Expected keyword DEMAND_SECTION";
for[n in 1..nbNodes] {
if (n != inFile.readInt()) throw "Unexpected index";
// demands must start at 0 to be accessed by an "at" operator. Thus
// node ids will start at 0 in the model.
local demand = inFile.readInt();
if (n == 1) {
if (demand != 0) throw "expected demand for depot is 0";
} else {
demands[n - 2] = demand; // demands is indexed by customers
}
}
dump = inFile.readln();
if (!dump.startsWith("DEPOT_SECTION")) throw "Expected keyword DEPOT_SECTION";
local warehouseId = inFile.readInt();
if (warehouseId != 1) throw "Warehouse id is supposed to be 1";
local endOfDepotSection = inFile.readInt();
if (endOfDepotSection != -1) throw "Expecting only one warehouse, more than one found";
}
/* Compute the distance between each node */
function computeDistanceMatrix() {
for[i in 0..nbCustomers-1] {
distanceMatrix[i][i] = 0;
for[j in i+1..nbCustomers-1] {
// +2 because computeDist expected original data indices,
// with customers in 2..nbNodes (1 being the depot)
local localDistance = computeDist(i + 2, j + 2);
distanceMatrix[j][i] = localDistance;
distanceMatrix[i][j] = localDistance;
}
}
for[i in 0..nbCustomers-1] {
local localDistance = computeDist(1, i+2);
distanceWarehouse[i] = localDistance;
}
}
function computeDist(i, j) {
local exactDist = sqrt(pow((nodesX[i] - nodesX[j]), 2) + pow((nodesY[i] - nodesY[j]), 2));
return round(exactDist);
}
function getNbTrucks() {
local splitted = inFileName.split("-k");
if (count(splitted) >= 2) {
local numvrp = splitted[count(splitted) - 1];
splitted = numvrp.split(".");
if (count(splitted) == 2) return splitted[0].toInt();
} else {
println("Error: nbTrucks could not be read from the file name. Enter it from the command line");
throw usage;
}
}
- Execution (Windows)
- set PYTHONPATH=%LS_HOME%\bin\python cvrp.py instances\A-n32-k5.vrp
- Execution (Linux)
- export PYTHONPATH=/opt/localsolver_9_5/bin/python cvrp.py instances/A-n32-k5.vrp
########## cvrp.py ##########
import localsolver
import sys
import math
def read_elem(filename):
with open(filename) as f:
return [str(elem) for elem in f.read().split()]
def main(instance_file, str_time_limit, sol_file, str_nb_trucks):
nb_trucks = int(str_nb_trucks)
#
# Reads instance data
#
(nb_customers, truck_capacity, distance_matrix, distance_warehouses, demands) = read_input_cvrp(instance_file)
# The number of trucks is usually given in the name of the file
# nb_trucks can also be given in command line
if nb_trucks == 0:
nb_trucks = get_nb_trucks(instance_file)
with localsolver.LocalSolver() as ls:
#
# Declares the optimization model
#
model = ls.model
# Sequence of customers visited by each truck.
customers_sequences = [model.list(nb_customers) for k in range(nb_trucks)]
# All customers must be visited by the trucks
model.constraint(model.partition(customers_sequences))
# Create demands as an array to be able to access it with an "at" operator
demands_array = model.array(demands)
# Create distance as an array to be able to acces it with an "at" operator
distance_array = model.array()
for n in range(nb_customers):
distance_array.add_operand(model.array(distance_matrix[n]))
distance_warehouse_array = model.array(distance_warehouses)
route_distances = [None] * nb_trucks
# A truck is used if it visits at least one customer
trucks_used = [(model.count(customers_sequences[k]) > 0) for k in range(nb_trucks)]
nb_trucks_used = model.sum(trucks_used)
for k in range(nb_trucks):
sequence = customers_sequences[k]
c = model.count(sequence)
# Quantity in each truck
demand_selector = model.lambda_function(lambda i: demands_array[sequence[i]])
route_quantity = model.sum(model.range(0, c), demand_selector)
model.constraint(route_quantity <= truck_capacity)
# Distance traveled by each truck
dist_selector = model.lambda_function(lambda i: model.at(distance_array, sequence[i-1], sequence[i]))
route_distances[k] = model.sum(model.range(1, c), dist_selector) + \
model.iif(c > 0, distance_warehouse_array[sequence[0]] + distance_warehouse_array[sequence[c-1]], 0)
# Total distance traveled
total_distance = model.sum(route_distances)
# Objective: minimize the number of trucks used, then minimize the distance traveled
model.minimize(nb_trucks_used)
model.minimize(total_distance)
model.close()
#
# Parameterizes the solver
#
ls.param.time_limit = int(str_time_limit)
ls.solve()
#
# Writes the solution in a file with the following format:
# - number of trucks used and total distance
# - for each truck the nodes visited (omitting the start/end at the depot)
#
if len(sys.argv) >= 3:
with open(sol_file, 'w') as f:
f.write("%d %d\n" % (nb_trucks_used.value, total_distance.value))
for k in range(nb_trucks):
if trucks_used[k].value != 1: continue
# Values in sequence are in [0..nbCustomers-1]. +2 is to put it back in [2..nbCustomers+1]
# as in the data files (1 being the depot)
for customer in customers_sequences[k].value:
f.write("%d " % (customer + 2))
f.write("\n")
# The input files follow the "Augerat" format.
def read_input_cvrp(filename):
file_it = iter(read_elem(sys.argv[1]))
nb_nodes = 0
while(1):
token = next(file_it)
if token == "DIMENSION":
next(file_it) # Removes the ":"
nb_nodes = int(next(file_it))
nb_customers = nb_nodes - 1
elif token == "CAPACITY":
next(file_it) # Removes the ":"
truck_capacity = int(next(file_it))
elif token == "EDGE_WEIGHT_TYPE":
next(file_it) # Removes the ":"
token = next(file_it)
if token != "EUC_2D":
print ("Edge Weight Type " + token + " is not supported (only EUD_2D)")
sys.exit(1)
elif token == "NODE_COORD_SECTION":
break
customers_x = [None]*nb_customers
customers_y = [None]*nb_customers
depot_x = 0
depot_y = 0
for n in range(nb_nodes):
node_id = int(next(file_it))
if node_id != n+1:
print ("Unexpected index")
sys.exit(1)
if node_id == 1:
depot_x = int(next(file_it))
depot_y = int(next(file_it))
else:
# -2 because orginal customer indices are in 2..nbNodes
customers_x[node_id-2] = int(next(file_it))
customers_y[node_id-2] = int(next(file_it))
# Compute distance matrix
distance_matrix = compute_distance_matrix(customers_x, customers_y)
distance_warehouses = compute_distance_warehouses(depot_x, depot_y, customers_x, customers_y)
token = next(file_it)
if token != "DEMAND_SECTION":
print ("Expected token DEMAND_SECTION")
sys.exit(1)
demands = [None]*nb_customers
for n in range(nb_nodes):
node_id = int(next(file_it))
if node_id != n+1:
print ("Unexpected index")
sys.exit(1)
if node_id == 1:
if int(next(file_it)) != 0:
print ("Demand for depot should be 0")
sys.exit(1)
else:
# -2 because orginal customer indices are in 2..nbNodes
demands[node_id-2] = int(next(file_it))
token = next(file_it)
if token != "DEPOT_SECTION":
print ("Expected token DEPOT_SECTION")
sys.exit(1)
warehouse_id = int(next(file_it))
if warehouse_id != 1:
print ("Warehouse id is supposed to be 1")
sys.exit(1)
end_of_depot_section = int(next(file_it))
if end_of_depot_section != -1:
print ("Expecting only one warehouse, more than one found")
sys.exit(1)
return (nb_customers, truck_capacity, distance_matrix, distance_warehouses, demands)
# Computes the distance matrix
def compute_distance_matrix(customers_x, customers_y):
nb_customers = len(customers_x)
distance_matrix = [[None for i in range(nb_customers)] for j in range(nb_customers)]
for i in range(nb_customers):
distance_matrix[i][i] = 0
for j in range(nb_customers):
dist = compute_dist(customers_x[i], customers_x[j], customers_y[i], customers_y[j])
distance_matrix[i][j] = dist
distance_matrix[j][i] = dist
return distance_matrix
# Computes the distances to warehouse
def compute_distance_warehouses(depot_x, depot_y, customers_x, customers_y):
nb_customers = len(customers_x)
distance_warehouses = [None] * nb_customers
for i in range(nb_customers):
dist = compute_dist(depot_x, customers_x[i], depot_y, customers_y[i])
distance_warehouses[i] = dist
return distance_warehouses
def compute_dist(xi, xj, yi, yj):
exact_dist = math.sqrt(math.pow(xi - xj, 2) + math.pow(yi - yj, 2))
return int(math.floor(exact_dist + 0.5))
def get_nb_trucks(filename):
begin = filename.rfind("-k")
if begin != -1:
begin += 2
end = filename.find(".", begin)
return int(filename[begin:end])
print ("Error: nb_trucks could not be read from the file name. Enter it from the command line")
sys.exit(1)
if __name__ == '__main__':
if len(sys.argv) < 2:
print ("Usage: python cvrp.py input_file [output_file] [time_limit] [nb_trucks]")
sys.exit(1)
instance_file = sys.argv[1]
sol_file = sys.argv[2] if len(sys.argv) > 2 else None
str_time_limit = sys.argv[3] if len(sys.argv) > 3 else "20"
str_nb_trucks = sys.argv[4] if len(sys.argv) > 4 else "0"
main(instance_file, str_time_limit, sol_file, str_nb_trucks)
- Compilation / Execution (Windows)
- cl /EHsc cvrp.cpp -I%LS_HOME%\include /link %LS_HOME%\bin\localsolver95.libcvrp instances\A-n32-k5.vrp
- Compilation / Execution (Linux)
- g++ cvrp.cpp -I/opt/localsolver_9_5/include -llocalsolver95 -lpthread -o cvrp./cvrp instances/A-n32-k5.vrp
/********** cvrp.cpp **********/
#include <iostream>
#include <fstream>
#include <vector>
#include <cstring>
#include <cmath>
#include "localsolver.h"
using namespace localsolver;
using namespace std;
class Cvrp {
public:
// LocalSolver
LocalSolver localsolver;
// Number of customers
int nbCustomers;
// Capacity of the trucks
int truckCapacity;
// Demand on each node
vector<lsint> demands;
// Distance matrix
vector<vector<lsint> > distanceMatrix;
// Distance to depot
vector<lsint> distanceWarehouses;
// Number of trucks
int nbTrucks;
// Decision variables
vector<LSExpression> customersSequences;
// Are the trucks actually used
vector<LSExpression> trucksUsed;
// Number of trucks used in the solution
LSExpression nbTrucksUsed;
// Distance traveled by all the trucks
LSExpression totalDistance;
// Constructor
Cvrp(const char* strNbTrucks) {
nbTrucks = atoi(strNbTrucks);
}
// Reads instance data.
void readInstance(const string& fileName) {
readInputCvrp(fileName);
// The number of trucks is usually given in the name of the file
// nbTrucks can also be given in command line
if (nbTrucks == 0) nbTrucks = getNbTrucks(fileName);
}
void solve(int limit) {
// Declares the optimization model.
LSModel model = localsolver.getModel();
// Sequence of customers visited by each truck.
customersSequences.resize(nbTrucks);
for (int k = 0; k < nbTrucks; k++) {
customersSequences[k] = model.listVar(nbCustomers);
}
// All customers must be visited by the trucks
model.constraint(model.partition(customersSequences.begin(), customersSequences.end()));
// Create demands as an array to be able to access it with an "at" operator
LSExpression demandsArray = model.array(demands.begin(), demands.end());
// Create distance as an array to be able to acces it with an "at" operator
LSExpression distanceArray = model.array();
for (int n = 0; n < nbCustomers; n++) {
distanceArray.addOperand(model.array(distanceMatrix[n].begin(), distanceMatrix[n].end()));
}
LSExpression distanceWarehousesArray = model.array(distanceWarehouses.begin(), distanceWarehouses.end());
trucksUsed.resize(nbTrucks);
vector<LSExpression> routeDistances(nbTrucks);
for (int k = 0; k < nbTrucks; k++) {
LSExpression sequence = customersSequences[k];
LSExpression c = model.count(sequence);
// A truck is used if it visits at least one customer
trucksUsed[k] = c > 0;
// The quantity needed in each route must not exceed the truck capacity
LSExpression demandSelector = model.createLambdaFunction([&](LSExpression i) { return demandsArray[sequence[i]]; });
LSExpression routeQuantity = model.sum(model.range(0, c), demandSelector);
model.constraint(routeQuantity <= truckCapacity);
// Distance traveled by truck k
LSExpression distSelector = model.createLambdaFunction([&](LSExpression i) { return model.at(distanceArray, sequence[i - 1], sequence[i]); });
routeDistances[k] = model.sum(model.range(1, c), distSelector) +
model.iif(c > 0, distanceWarehousesArray[sequence[0]] + distanceWarehousesArray[sequence[c - 1]], 0);
}
// Total nb trucks used
nbTrucksUsed = model.sum(trucksUsed.begin(), trucksUsed.end());
// Total distance traveled
totalDistance = model.sum(routeDistances.begin(), routeDistances.end());
// Objective: minimize the number of trucks used, then minimize the distance traveled
model.minimize(nbTrucksUsed);
model.minimize(totalDistance);
model.close();
// Parameterizes the solver.
localsolver.getParam().setTimeLimit(limit);
localsolver.solve();
}
// Writes the solution in a file with the following format:
// - number of trucks used and total distance
// - for each truck the nodes visited (omitting the start/end at the depot)
void writeSolution(const string& fileName) {
ofstream outfile;
outfile.exceptions(ofstream::failbit | ofstream::badbit);
outfile.open(fileName.c_str());
outfile << nbTrucksUsed.getValue() << " " << totalDistance.getValue() << endl;
for (int k = 0; k < nbTrucks; k++) {
if (trucksUsed[k].getValue() != 1) continue;
// Values in sequence are in [0..nbCustomers-1]. +2 is to put it back in [2..nbCustomers+1]
// as in the data files (1 being the depot)
LSCollection customersCollection = customersSequences[k].getCollectionValue();
for (lsint i = 0; i < customersCollection.count(); i++) {
outfile << customersCollection[i] + 2 << " ";
}
outfile << endl;
}
}
private:
// The input files follow the "Augerat" format.
void readInputCvrp(const string& fileName) {
ifstream infile(fileName.c_str());
if (!infile.is_open()) {
throw std::runtime_error("File cannot be opened.");
}
string str;
char *pch;
char* line;
int nbNodes;
while (true) {
getline(infile, str);
line = strdup(str.c_str());
pch = strtok(line, " :");
if (strcmp(pch, "DIMENSION") == 0) {
pch = strtok(NULL, " :");
nbNodes = atoi(pch);
nbCustomers = nbNodes - 1;
} else if (strcmp(pch, "CAPACITY") == 0) {
pch = strtok(NULL, " :");
truckCapacity = atoi(pch);
} else if (strcmp(pch, "EDGE_WEIGHT_TYPE") == 0) {
pch = strtok(NULL, " :");
if (strcmp(pch, "EUC_2D") != 0) {
throw std::runtime_error("Only Edge Weight Type EUC_2D is supported");
}
} else if (strcmp(pch, "NODE_COORD_SECTION") == 0) {
break;
}
}
vector<int> customersX(nbCustomers);
vector<int> customersY(nbCustomers);
int depotX, depotY;
for (int n = 1; n <= nbNodes; n++) {
int id;
infile >> id;
if (id != n) {
throw std::runtime_error("Unexpected index");
}
if (n == 1) {
infile >> depotX;
infile >> depotY;
} else {
// -2 because orginal customer indices are in 2..nbNodes
infile >> customersX[n-2];
infile >> customersY[n-2];
}
}
// Compute distance matrix
computeDistanceMatrix(depotX, depotY, customersX, customersY);
getline(infile, str); // End the last line
getline(infile, str);
line = strdup(str.c_str());
pch = strtok(line, " :");
if (strcmp(pch, "DEMAND_SECTION") != 0) {
throw std::runtime_error("Expected keyword DEMAND_SECTION");
}
demands.resize(nbCustomers);
for (int n = 1; n <= nbNodes; n++) {
int id;
infile >> id;
if (id != n) {
throw std::runtime_error("Unexpected index");
}
int demand;
infile >> demand;
if (n == 1) {
if (demand != 0) {
throw std::runtime_error("Demand for depot should be O");
}
} else {
// -2 because orginal customer indices are in 2..nbNodes
demands[n-2] = demand;
}
}
getline(infile, str); // End the last line
getline(infile, str);
line = strdup(str.c_str());
pch = strtok(line, " :");
if (strcmp(pch, "DEPOT_SECTION") != 0) {
throw std::runtime_error("Expected keyword DEPOT_SECTION");
}
int warehouseId;
infile >> warehouseId;
if (warehouseId != 1) {
throw std::runtime_error("Warehouse id is supposed to be 1");
}
int endOfDepotSection;
infile >> endOfDepotSection;
if (endOfDepotSection != -1) {
throw std::runtime_error("Expecting only one warehouse, more than one found");
}
infile.close();
}
// Computes the distance matrix
void computeDistanceMatrix(int depotX, int depotY, const vector<int>& customersX, const vector<int>& customersY) {
distanceMatrix.resize(nbCustomers);
for (int i = 0; i < nbCustomers; i++) {
distanceMatrix[i].resize(nbCustomers);
}
for (int i = 0; i < nbCustomers; i++) {
distanceMatrix[i][i] = 0;
for (int j = i + 1; j < nbCustomers; j++) {
lsint distance = computeDist(customersX[i], customersX[j], customersY[i], customersY[j]);
distanceMatrix[i][j] = distance;
distanceMatrix[j][i] = distance;
}
}
distanceWarehouses.resize(nbCustomers);
for (int i = 0; i < nbCustomers; ++i) {
distanceWarehouses[i] = computeDist(depotX, customersX[i], depotY, customersY[i]);
}
}
lsint computeDist(int xi, int xj, int yi, int yj) {
double exactDist = sqrt(pow((double) xi - xj, 2) + pow((double) yi - yj, 2));
return floor(exactDist + 0.5);
}
int getNbTrucks(const string& fileName) {
size_t pos = fileName.rfind("-k");
if (pos != string::npos) {
string nbTrucksStr = fileName.substr(pos + 2);
pos = nbTrucksStr.find(".");
if (pos != string::npos) {
return atoi(nbTrucksStr.substr(0, pos).c_str());
}
}
throw std::runtime_error("Error: nbTrucks could not be read from the file name. Enter it from the command line");
return -1;
}
};
int main(int argc, char** argv) {
if (argc < 2) {
cerr << "Usage: cvrp inputFile [outputFile] [timeLimit] [nbTrucks]" << endl;
return 1;
}
const char* instanceFile = argv[1];
const char* solFile = argc > 2 ? argv[2] : NULL;
const char* strTimeLimit = argc > 3 ? argv[3] : "20";
const char* strNbTrucks = argc > 4 ? argv[4] : "0";
try {
Cvrp model(strNbTrucks);
model.readInstance(instanceFile);
model.solve(atoi(strTimeLimit));
if (solFile != NULL) model.writeSolution(solFile);
return 0;
} catch (const exception& e) {
cerr << "An error occurred: " << e.what() << endl;
return 1;
}
}
- Compilation / Execution (Windows)
- copy %LS_HOME%\bin\localsolvernet.dll .csc Cvrp.cs /reference:localsolvernet.dllCvrp instances\A-n32-k5.vrp
/********** Cvrp.cs **********/
using System;
using System.IO;
using localsolver;
public class Cvrp : IDisposable
{
// Solver
LocalSolver localsolver;
// Number of customers (= number of nodes minus 1)
int nbCustomers;
// Capacity of the trucks
int truckCapacity;
// Demand on each customer
long[] demands;
// Distance matrix between customers
long[][] distanceMatrix;
// Distances between customers and warehouse
long[] distanceWarehouses;
// Number of trucks
int nbTrucks;
// Decision variables
LSExpression[] customersSequences;
// Are the trucks actually used
LSExpression[] trucksUsed;
// Distance traveled by each truck
LSExpression[] routeDistances;
// Number of trucks used in the solution
LSExpression nbTrucksUsed;
// Distance traveled by all the trucks
LSExpression totalDistance;
public Cvrp(string strNbTrucks)
{
localsolver = new LocalSolver();
nbTrucks = int.Parse(strNbTrucks);
}
// Reads instance data.
void ReadInstance(string fileName)
{
ReadInputCvrp(fileName);
// The number of trucks is usually given in the name of the file
// nbTrucks can also be given in command line
if (nbTrucks == 0) nbTrucks = GetNbTrucks(fileName);
}
public void Dispose()
{
if (localsolver != null)
localsolver.Dispose();
}
void Solve(int limit)
{
// Declares the optimization model.
LSModel model = localsolver.GetModel();
trucksUsed = new LSExpression[nbTrucks];
customersSequences = new LSExpression[nbTrucks];
routeDistances = new LSExpression[nbTrucks];
// Sequence of customers visited by each truck.
for (int k = 0; k < nbTrucks; k++)
customersSequences[k] = model.List(nbCustomers);
// All customers must be visited by the trucks
model.Constraint(model.Partition(customersSequences));
// Create demands and distances as arrays to be able to access it with an "at" operator
LSExpression demandsArray = model.Array(demands);
LSExpression distanceWarehouseArray = model.Array(distanceWarehouses);
LSExpression distanceArray = model.Array(distanceMatrix);
for (int k = 0; k < nbTrucks; k++)
{
LSExpression sequence = customersSequences[k];
LSExpression c = model.Count(sequence);
// A truck is used if it visits at least one customer
trucksUsed[k] = c > 0;
// The quantity needed in each route must not exceed the truck capacity
LSExpression demandSelector = model.LambdaFunction(i => demandsArray[sequence[i]]);
LSExpression routeQuantity = model.Sum(model.Range(0, c), demandSelector);
model.Constraint(routeQuantity <= truckCapacity);
// Distance traveled by truck k
LSExpression distSelector = model.LambdaFunction(i => distanceArray[sequence[i - 1], sequence[i]]);
routeDistances[k] = model.Sum(model.Range(1, c), distSelector)
+ model.If(c > 0, distanceWarehouseArray[sequence[0]] + distanceWarehouseArray[sequence[c - 1]], 0);
}
nbTrucksUsed = model.Sum(trucksUsed);
totalDistance = model.Sum(routeDistances);
// Objective: minimize the number of trucks used, then minimize the distance traveled
model.Minimize(nbTrucksUsed);
model.Minimize(totalDistance);
model.Close();
// Parameterizes the solver.
localsolver.GetParam().SetTimeLimit(limit);
localsolver.Solve();
}
// Writes the solution in a file with the following format:
// - number of trucks used and total distance
// - for each truck the nodes visited (omitting the start/end at the depot)
void WriteSolution(string fileName)
{
using (StreamWriter output = new StreamWriter(fileName))
{
output.WriteLine(nbTrucksUsed.GetValue() + " " + totalDistance.GetValue());
for (int k = 0; k < nbTrucks; k++)
{
if (trucksUsed[k].GetValue() != 1) continue;
// Values in sequence are in [0..nbCustomers-1]. +2 is to put it back in [2..nbCustomers+1]
// as in the data files (1 being the depot)
LSCollection customersCollection = customersSequences[k].GetCollectionValue();
for (int i = 0; i < customersCollection.Count(); i++)
{
output.Write((customersCollection[i] + 2) + " ");
}
output.WriteLine();
}
}
}
public static void Main(string[] args)
{
if (args.Length < 1)
{
Console.WriteLine("Usage: Cvrp inputFile [solFile] [timeLimit] [nbTrucks]");
Environment.Exit(1);
}
string instanceFile = args[0];
string outputFile = args.Length > 1 ? args[1] : null;
string strTimeLimit = args.Length > 2 ? args[2] : "20";
string strNbTrucks = args.Length > 3 ? args[3] : "0";
using (Cvrp model = new Cvrp(strNbTrucks))
{
model.ReadInstance(instanceFile);
model.Solve(int.Parse(strTimeLimit));
if (outputFile != null)
model.WriteSolution(outputFile);
}
}
// The input files follow the "Augerat" format.
private void ReadInputCvrp(string fileName)
{
using (StreamReader input = new StreamReader(fileName))
{
int nbNodes = 0;
string[] splitted;
while (true)
{
splitted = input.ReadLine().Split(':');
if (splitted[0].Contains("DIMENSION"))
{
nbNodes = int.Parse(splitted[1]);
nbCustomers = nbNodes - 1;
}
else if (splitted[0].Contains("CAPACITY"))
{
truckCapacity = int.Parse(splitted[1]);
}
else if (splitted[0].Contains("EDGE_WEIGHT_TYPE"))
{
if (!splitted[1].Trim().Equals("EUC_2D"))
throw new Exception("Edge Weight Type " + splitted[1] + " is not supported (only EUC_2D)");
}
else if (splitted[0].Contains("NODE_COORD_SECTION"))
{
break;
}
}
int[] customersX = new int[nbCustomers];
int[] customersY = new int[nbCustomers];
int depotX = 0, depotY = 0;
for (int n = 1; n <= nbNodes; n++)
{
splitted = input.ReadLine().Split((char[])null, StringSplitOptions.RemoveEmptyEntries);
if (int.Parse(splitted[0]) != n)
throw new Exception("Unexpected index");
if (n == 1) {
depotX = int.Parse(splitted[1]);
depotY = int.Parse(splitted[2]);
} else {
// -2 because orginal customer indices are in 2..nbNodes
customersX[n-2] = int.Parse(splitted[1]);
customersY[n-2] = int.Parse(splitted[2]);
}
}
ComputeDistanceMatrix(depotX, depotY, customersX, customersY);
splitted = input.ReadLine().Split(':');
if (!splitted[0].Contains("DEMAND_SECTION"))
throw new Exception("Expected keyword DEMAND_SECTION");
demands = new long[nbCustomers];
for (int n = 1; n <= nbNodes; n++)
{
splitted = input.ReadLine().Split((char[])null, StringSplitOptions.RemoveEmptyEntries);
if (int.Parse(splitted[0]) != n)
throw new Exception("Unexpected index");
var demand = int.Parse(splitted[1]);
if (n == 1) {
if (demand != 0) throw new Exception("Warehouse demand is supposed to be 0");
} else {
// -2 because orginal customer indices are in 2..nbNodes
demands[n-2] = demand;
}
}
splitted = input.ReadLine().Split(':');
if (!splitted[0].Contains("DEPOT_SECTION"))
throw new Exception("Expected keyword DEPOT_SECTION");
int warehouseId = int.Parse(input.ReadLine());
if (warehouseId != 1)
throw new Exception("Warehouse id is supposed to be 1");
int endOfDepotSection = int.Parse(input.ReadLine());
if (endOfDepotSection != -1)
throw new Exception("Expecting only one warehouse, more than one found");
}
}
// Computes the distance matrix
private void ComputeDistanceMatrix(int depotX, int depotY, int[] customersX, int[] customersY)
{
distanceMatrix = new long[nbCustomers][];
for (int i = 0; i < nbCustomers; i++)
distanceMatrix[i] = new long[nbCustomers];
for (int i = 0; i < nbCustomers; i++)
{
distanceMatrix[i][i] = 0;
for (int j = i + 1; j < nbCustomers; j++)
{
long dist = ComputeDist(customersX[i], customersX[j], customersY[i], customersY[j]);
distanceMatrix[i][j] = dist;
distanceMatrix[j][i] = dist;
}
}
distanceWarehouses = new long[nbCustomers];
for (int i = 0; i < nbCustomers; ++i) {
distanceWarehouses[i] = ComputeDist(depotX, customersX[i], depotY, customersY[i]);
}
}
private long ComputeDist(int xi, int xj, int yi, int yj)
{
double exactDist = Math.Sqrt(Math.Pow(xi - xj, 2) + Math.Pow(yi - yj, 2));
return Convert.ToInt64(Math.Round(exactDist));
}
private int GetNbTrucks(string fileName)
{
string[] splitted = fileName.Split(new[] { '-', 'k' }, StringSplitOptions.RemoveEmptyEntries);
if (splitted.Length >= 2)
{
string toSplit = splitted[splitted.Length - 1];
splitted = toSplit.Split(new[] { '.' }, StringSplitOptions.RemoveEmptyEntries);
return int.Parse(splitted[0]);
}
throw new Exception("Error: nbTrucks could not be read from the file name. Enter it from the command line");
}
}
- Compilation / Execution (Windows)
- javac Cvrp.java -cp %LS_HOME%\bin\localsolver.jarjava -cp %LS_HOME%\bin\localsolver.jar;. Cvrp instances\A-n32-k5.vrp
- Compilation / Execution (Linux)
- javac Cvrp.java -cp /opt/localsolver_9_5/bin/localsolver.jarjava -cp /opt/localsolver_9_5/bin/localsolver.jar:. Cvrp instances/A-n32-k5.vrp
/********** Cvrp.java **********/
import java.util.*;
import java.io.*;
import localsolver.*;
public class Cvrp {
// Solver
private final LocalSolver localsolver;
// Number of customers (= number of nodes minus 1)
int nbCustomers;
// Capacity of the trucks
private int truckCapacity;
// Demand on each node
private long[] demands;
// Distance matrix
private long[][] distanceMatrix;
// Distances between customers and warehouse
private long[] distanceWarehouses;
// Number of trucks
private int nbTrucks;
// Decision variables
private LSExpression[] customersSequences;
// Are the trucks actually used
private LSExpression[] trucksUsed;
// Distance traveled by each truck
LSExpression[] routeDistances;
// Number of trucks used in the solution
private LSExpression nbTrucksUsed;
// Distance traveled by all the trucks
private LSExpression totalDistance;
private Cvrp(LocalSolver localsolver) {
this.localsolver = localsolver;
}
private void solve(int limit) {
// Declares the optimization model.
LSModel model = localsolver.getModel();
trucksUsed = new LSExpression[nbTrucks];
customersSequences = new LSExpression[nbTrucks];
routeDistances = new LSExpression[nbTrucks];
// Sequence of customers visited by each truck.
for (int k = 0; k < nbTrucks; k++)
customersSequences[k] = model.listVar(nbCustomers);
// All customers must be visited by the trucks
model.constraint(model.partition(customersSequences));
// Create demands and distances as arrays to be able to access it with an "at" operator
LSExpression demandsArray = model.array(demands);
LSExpression distanceWarehouseArray = model.array(distanceWarehouses);
LSExpression distanceArray = model.array(distanceMatrix);
for (int k = 0; k < nbTrucks; k++) {
LSExpression sequence = customersSequences[k];
LSExpression c = model.count(sequence);
// A truck is used if it visits at least one customer
trucksUsed[k] = model.gt(c, 0);
// The quantity needed in each route must not exceed the truck capacity
LSExpression demandSelector = model.lambdaFunction(i -> model.at(demandsArray, model.at(sequence, i)));
LSExpression routeQuantity = model.sum(model.range(0, c), demandSelector);
model.constraint(model.leq(routeQuantity, truckCapacity));
// Distance traveled by truck k
LSExpression distSelector = model.lambdaFunction(i -> model.at(
distanceArray,
model.at(sequence, model.sub(i, 1)),
model.at(sequence, i)));
routeDistances[k] = model.sum(model.sum(model.range(1, c), distSelector),
model.iif(model.gt(c, 0), model.sum(
model.at(distanceWarehouseArray, model.at(sequence, 0)),
model.at(distanceWarehouseArray, model.at(sequence, model.sub(c, 1)))), 0));
}
nbTrucksUsed = model.sum(trucksUsed);
totalDistance = model.sum(routeDistances);
// Objective: minimize the number of trucks used, then minimize the distance traveled
model.minimize(nbTrucksUsed);
model.minimize(totalDistance);
model.close();
// Parameterizes the solver.
localsolver.getParam().setTimeLimit(limit);
localsolver.solve();
}
// Writes the solution in a file with the following format:
// - number of trucks used and total distance
// - for each truck the nodes visited (omitting the start/end at the depot)
private void writeSolution(String fileName) throws IOException {
try (PrintWriter output = new PrintWriter(fileName)) {
output.println(nbTrucksUsed.getValue() + " " + totalDistance.getValue());
for (int k = 0; k < nbTrucks; k++) {
if (trucksUsed[k].getValue() != 1) continue;
// Values in sequence are in [0..nbCustomers-1]. +2 is to put it back in [2..nbCustomers+1]
// as in the data files (1 being the depot)
LSCollection customersCollection = customersSequences[k].getCollectionValue();
for (int i = 0; i < customersCollection.count(); i++) {
output.print((customersCollection.get(i) + 2) + " ");
}
output.println();
}
}
}
// The input files follow the "Augerat" format.
private void readInstance(int customNbTrucks, String fileName) throws IOException {
// The number of trucks is usually given in the name of the file
// nbTrucks can also be given in command line
nbTrucks = customNbTrucks <= 0 ? extractNbTrucksFromFileName(fileName) : customNbTrucks;
if (nbTrucks <= 0) {
throw new RuntimeException("Error: nbTrucks is incorrect or could not be read from the file name. "
+ "Enter a strictly positive number from the command line");
}
try (Scanner input = new Scanner(new File(fileName))) {
int nbNodes = 0;
String[] splitted;
while (true) {
splitted = input.nextLine().split(":");
if (splitted[0].contains("DIMENSION")) {
nbNodes = Integer.parseInt(splitted[1].trim());
nbCustomers = nbNodes - 1;
} else if (splitted[0].contains("CAPACITY")) {
truckCapacity = Integer.parseInt(splitted[1].trim());
} else if (splitted[0].contains("EDGE_WEIGHT_TYPE")) {
if (splitted[1].trim().compareTo("EUC_2D") != 0) {
throw new RuntimeException("Edge Weight Type " + splitted[1] + " is not supported (only EUC_2D)");
}
} else if (splitted[0].contains("NODE_COORD_SECTION")) {
break;
}
}
int[] customersX = new int[nbCustomers];
int[] customersY = new int[nbCustomers];
int depotX = 0, depotY = 0;
for (int n = 1; n <= nbNodes; n++) {
int id = input.nextInt();
if (id != n) throw new IOException("Unexpected index");
if (n == 1) {
depotX = input.nextInt();
depotY = input.nextInt();
} else {
// -2 because orginal customer indices are in 2..nbNodes
customersX[n - 2] = input.nextInt();
customersY[n - 2] = input.nextInt();
}
}
computeDistanceMatrix(depotX, depotY, customersX, customersY);;
splitted = input.nextLine().split(":"); // End the last line
splitted = input.nextLine().split(":");
if (!splitted[0].contains("DEMAND_SECTION")) {
throw new RuntimeException("Expected keyword DEMAND_SECTION");
}
demands = new long[nbCustomers];
for (int n = 1; n <= nbNodes; n++) {
int id = input.nextInt();
if (id != n) throw new IOException("Unexpected index");
int demand = input.nextInt();
if (n == 1) {
if (demand != 0) throw new IOException("Warehouse demand is supposed to be 0");
} else {
// -2 because orginal customer indices are in 2..nbNodes
demands[n - 2] = demand;
}
}
splitted = input.nextLine().split(":"); // End the last line
splitted = input.nextLine().split(":");
if (!splitted[0].contains("DEPOT_SECTION")) {
throw new RuntimeException("Expected keyword DEPOT_SECTION");
}
int warehouseId = input.nextInt();
if (warehouseId != 1) throw new IOException("Warehouse id is supposed to be 1");
int endOfDepotSection = input.nextInt();
if (endOfDepotSection != -1) {
throw new RuntimeException("Expecting only one warehouse, more than one found");
}
}
}
// Computes the distance matrix
private void computeDistanceMatrix(int depotX, int depotY, int[] customersX, int[] customersY) {
distanceMatrix = new long[nbCustomers][nbCustomers];
for (int i = 0; i < nbCustomers; i++) {
distanceMatrix[i][i] = 0;
for (int j = i + 1; j < nbCustomers; j++) {
long dist = computeDist(customersX[i], customersX[j], customersY[i], customersY[j]);
distanceMatrix[i][j] = dist;
distanceMatrix[j][i] = dist;
}
}
distanceWarehouses = new long[nbCustomers];
for (int i = 0; i < nbCustomers; ++i) {
distanceWarehouses[i] = computeDist(depotX, customersX[i], depotY, customersY[i]);
}
}
private long computeDist(int xi, int xj, int yi, int yj) {
double exactDist = Math.sqrt(Math.pow(xi - xj, 2) + Math.pow(yi - yj, 2));
return Math.round(exactDist);
}
private int extractNbTrucksFromFileName(String fileName) {
int begin = fileName.lastIndexOf("-k");
if (begin != -1) {
int end = fileName.indexOf(".", begin + 2);
return Integer.parseInt(fileName.substring(begin + 2, end));
} else {
return -1;
}
}
public static void main(String[] args) {
if (args.length < 1) {
System.err.println("Usage: java Cvrp inputFile [outputFile] [timeLimit] [nbTrucks]");
System.exit(1);
}
try (LocalSolver localsolver = new LocalSolver()) {
String instanceFile = args[0];
String outputFile = args.length > 1 ? args[1] : null;
String strTimeLimit = args.length > 2 ? args[2] : "20";
String strNbTrucks = args.length > 3 ? args[3] : "0";
Cvrp model = new Cvrp(localsolver);
model.readInstance(Integer.parseInt(strNbTrucks), instanceFile);
model.solve(Integer.parseInt(strTimeLimit));
if (outputFile != null) {
model.writeSolution(outputFile);
}
} catch(Exception ex) {
System.err.println(ex);
ex.printStackTrace();
System.exit(1);
}
}
}