// New LS to final solutions
public static void cluVRPLocalSearchs(CluVRPSolution cluVRPSolution, CluVRPInstance instance, Parameters parameters)
{
// Perform LS at cluster level
double oldTotalDistance = cluVRPSolution.totalCustomerRouteDistance;
var totalWatch = System.Diagnostics.Stopwatch.StartNew();
swapClusters(cluVRPSolution, instance, parameters);
cluVRPSolution.cluvrp_swapClusters_time += totalWatch.ElapsedMilliseconds;
totalWatch = System.Diagnostics.Stopwatch.StartNew();
swapVehicle(cluVRPSolution, instance, parameters);
cluVRPSolution.cluvrp_swapVehicle_time += totalWatch.ElapsedMilliseconds;
// If some cluster position change make LS at customer level
if (cluVRPSolution.totalCustomerRouteDistance < oldTotalDistance)
{
// Create a local search handler for cluster-level problem
CustomerStrongLocalSearch customerLocalSearch = new CustomerStrongLocalSearch(cluVRPSolution,
instance,
parameters.Customer_LS_TwoOpt_Iterations,
parameters.Customer_LS_Relocate_Iterations,
parameters.Customer_LS_Exchange_Iterations,
parameters.Customer_LS_SwapCustomers
);
// Perform one iteration of LS at customer level
customerLocalSearch.twoOpt();
customerLocalSearch.exchange();
customerLocalSearch.relocate();
customerLocalSearch.swapCustomers();
}
// End
return;
}
/*
*
* Constructor
*
*/
public CustomerStrongGRASP(CluVRPInstance instance, CluVRPSolution solution, Parameters parameters)
{
// Set variables
this.instance = instance;
this.solution = solution;
this.parameters = parameters;
// For local search execute order
if (parameters.Customer_LS_Order.Length == 0)
{
this.localSearchsOrder = new List <LocalSearch> {
LocalSearch.TwoOpt,
LocalSearch.Relocate, LocalSearch.Exchange, LocalSearch.SwapCustomers
};
}
else
{
this.localSearchsOrder = new List <LocalSearch>();
for (int i = 0; i < parameters.Customer_LS_Order.Length; i++)
{
localSearchsOrder.Add((LocalSearch)parameters.Customer_LS_Order[i]);
}
}
// End of constructor
}
/*
*
* Applies a list of local searchs
*
*/
private void localSearch(CluVRPSolution newSolution)
{
// Create a local search handler for cluster-level problem
CustomerStrongLocalSearch customerLocalSearch = new CustomerStrongLocalSearch(newSolution,
instance,
parameters.Customer_LS_TwoOpt_Iterations,
parameters.Customer_LS_Relocate_Iterations,
parameters.Customer_LS_Exchange_Iterations,
parameters.Customer_LS_SwapCustomers
);
// If random order for local searchs is activated
if (parameters.Customer_LS_Order.Length == 0)
{
Functions.Shuffle(new Random(), this.localSearchsOrder);
}
// Execute local search in the correct order
for (int i = 0; i < localSearchsOrder.Count; i++)
{
// Perform TwoOpt
if (localSearchsOrder[i] == LocalSearch.TwoOpt && parameters.Customer_LS_TwoOpt_Iterations != 0)
{
var totalWatch = System.Diagnostics.Stopwatch.StartNew();
customerLocalSearch.twoOpt();
customerLocalSearch.solution.customer_twoOpt_time += totalWatch.ElapsedMilliseconds;
}
// Perform Relocate
if (localSearchsOrder[i] == LocalSearch.Relocate && parameters.Customer_LS_Relocate_Iterations != 0)
{
var totalWatch = System.Diagnostics.Stopwatch.StartNew();
customerLocalSearch.relocate();
customerLocalSearch.solution.customer_relocate_time += totalWatch.ElapsedMilliseconds;
}
// Perform Exchange
if (localSearchsOrder[i] == LocalSearch.Exchange && parameters.Customer_LS_Exchange_Iterations != 0)
{
var totalWatch = System.Diagnostics.Stopwatch.StartNew();
customerLocalSearch.exchange();
customerLocalSearch.solution.customer_exchange_time += totalWatch.ElapsedMilliseconds;
}
// Perform Customer Swap
if (localSearchsOrder[i] == LocalSearch.SwapCustomers && parameters.Customer_LS_SwapCustomers != 0)
{
var totalWatch = System.Diagnostics.Stopwatch.StartNew();
customerLocalSearch.swapCustomers();
customerLocalSearch.solution.customer_swapCustomers_time += totalWatch.ElapsedMilliseconds;
}
}
// Set the solution
newSolution = customerLocalSearch.solution;
}
/*
*
* Create a complete Greedy Randomized Solution (with cluster and customers)
*
*/
private CluVRPSolution constructGreedyRandomizedSolution(List <int>[] customersOnVehicle, double alpha)
{
// Init variables
int vehiclesNumber = customersOnVehicle.Length;
List <int>[] customersCircuit = new List <int> [vehiclesNumber];
double[] vehiculeTotalDistance = new double[vehiclesNumber];
// For each vehicule cluster-route
for (int vehicle = 0; vehicle < customersCircuit.Length; vehicle++)
{
// Init customer circuit for i-vehicle
List <int> customersToVisit = customersOnVehicle[vehicle].ToList <int>();
customersCircuit[vehicle] = new List <int>();
// Add depot as first customer
customersCircuit[vehicle].Add(1);
// While exists customers to visit
while (customersToVisit.Count > 0)
{
// Last customer
int lastCustomer = customersCircuit[vehicle][customersCircuit[vehicle].Count - 1];
// Create RCL for customer
List <int> customerRCL = buildCustomerRCL(customersToVisit, lastCustomer, alpha);
// Select customer for RCL
int customerSelected = Functions.selectRandomElement(customerRCL);
// Add customer to the path
customersCircuit[vehicle].Add(customerSelected);
// Quit visited customer
customersToVisit.Remove(customerSelected);
}
// Add depot as final customer
customersCircuit[vehicle].Add(1);
// Calculte total inter-cluster distance
vehiculeTotalDistance[vehicle] = Functions.calculateCustomerTravelDistance(customersCircuit[vehicle], instance.customersDistanceMatrix);
}
// Set solution
CluVRPSolution newSolution = new CluVRPSolution(instance);
newSolution.setWeakCostumerSolution(customersCircuit, vehiculeTotalDistance);
// Return solution
return(newSolution);
}
// Constructor
public CustomerStrongLocalSearch(CluVRPSolution solution,
CluVRPInstance instance,
int maxIterationsWithoutImprovementTwoOpt = 100,
int maxIterationsWithoutImprovementRelocate = 100,
int maxIterationsWithoutImprovementExchange = 100,
int maxIterationsWithoutImprovementSwapCustomers = 100)
{
this.solution = solution;
this.instance = instance;
this.maxIterationsWithoutImprovementTwoOpt = maxIterationsWithoutImprovementTwoOpt;
this.maxIterationsWithoutImprovementRelocate = maxIterationsWithoutImprovementRelocate;
this.maxIterationsWithoutImprovementExchange = maxIterationsWithoutImprovementExchange;
this.maxIterationsWithoutImprovementSwapCustomers = maxIterationsWithoutImprovementSwapCustomers;
}
// Constructor
public ClusterLocalSearch(CluVRPSolution solution,
CluVRPInstance instance,
int maxIterationsWithoutImprovementTwoOpt = 100,
int maxIterationsWithoutImprovementRelocate = 100,
int maxIterationsWithoutImprovementExchange = 100,
//int maxIterationsWithoutImprovementIVRS = 100,
//int maxIterationsWithoutImprovementIVRC = 100,
int maxIterationsWithoutImprovementIV = 100,
int maxIterationsWithoutImprovementSV = 100,
int maxIterationsWithoutImprovementSC = 100
)
{
this.solution = solution;
this.instance = instance;
this.maxIterationsWithoutImprovementTwoOpt = maxIterationsWithoutImprovementTwoOpt;
this.maxIterationsWithoutImprovementRelocate = maxIterationsWithoutImprovementRelocate;
this.maxIterationsWithoutImprovementExchange = maxIterationsWithoutImprovementExchange;
//this.maxIterationsWithoutImprovementIVRS = maxIterationsWithoutImprovementIVRS;
//this.maxIterationsWithoutImprovementIVRC = maxIterationsWithoutImprovementIVRC;
this.maxIterationsWithoutImprovementIV = maxIterationsWithoutImprovementIV;
this.maxIterationsWithoutImprovementSV = maxIterationsWithoutImprovementSV;
this.maxIterationsWithoutImprovementSC = maxIterationsWithoutImprovementSC;
}
internal void logTimeAndIterations(CluVRPSolution solution, string instance)
{
string line = instance + '\t' +
solution.cluVRPIterations.ToString() + '\t' +
//solution.clusterLevelIterations.ToString() + '\t' +
//solution.customerLevelIterations.ToString() + '\t' +
//solution.LSCycleterations.ToString() + '\t' +
(solution.clusterLevelTime).ToString() + '\t' +
(solution.customerLevelTime).ToString() + '\t' +
solution.cluster_LSCycle_iterations.ToString() + '\t' +
solution.customer_LSCycle_iterations.ToString() + '\t' +
(solution.cluster_LSCycleTime).ToString() + '\t' +
(solution.customer_LSCycleTime).ToString() + '\t' +
solution.cluvrp_swapClusters_iterations.ToString() + '\t' +
solution.cluvrp_swapClusters_time.ToString() + '\t' +
solution.cluvrp_swapVehicle_iterations.ToString() + '\t' +
solution.cluvrp_swapVehicle_time.ToString() + '\t' +
solution.cluster_twoOpt_iterations.ToString() + '\t' +
(solution.cluster_twoOpt_time).ToString() + '\t' +
solution.cluster_relocate_iterations.ToString() + '\t' +
(solution.cluster_relocate_time).ToString() + '\t' +
solution.cluster_exchange_iterations.ToString() + '\t' +
(solution.cluster_exchange_time).ToString() + '\t' +
solution.cluster_swapClusters_iterations.ToString() + '\t' +
(solution.cluster_swapClusters_time).ToString() + '\t' +
solution.cluster_swapVehicle_iterations.ToString() + '\t' +
(solution.cluster_swapVehicle_time).ToString() + '\t' +
solution.cluster_insertVehicle_iterations.ToString() + '\t' +
(solution.cluster_insertVehicle_time).ToString() + '\t' +
solution.customer_twoOpt_iterations.ToString() + '\t' +
(solution.customer_twoOpt_time).ToString() + '\t' +
solution.customer_relocate_iterations.ToString() + '\t' +
(solution.customer_relocate_time).ToString() + '\t' +
solution.customer_exchange_iterations.ToString() + '\t' +
(solution.customer_exchange_time).ToString() + '\t' +
solution.customer_swapCustomers_iterations.ToString() + '\t' +
(solution.customer_swapCustomers_time).ToString();
try
{
using (System.IO.StreamWriter file = System.IO.File.AppendText(logFilePath + "_time"))
{
file.WriteLine(line);
if (verbose)
{
Console.WriteLine(line);
}
}
}
catch (Exception e)
{
Console.WriteLine(e.ToString());
}
}
/*
*
* Grasp():
* M <- calculateCustomerDistance
* BestSolution = 0
* While(StopCondition)
* Solution <- ConstructGreedySolution()
* NewSolution <- LocalSearch(solution)
* if NewSolution isBetterThan BestSolution
* BestSolution = NewSolution
* return BestSolution
*
*/
override public void Grasp()
{
// Set iterator
int iterator = 0;
int totalIterations = parameters.Customer_GRASPIterations;
double alpha = parameters.Customer_Alpha;
// Main cycle
while (iterator < totalIterations)
{
// For random alpha
if (parameters.Customer_Alpha == -1)
{
Random rnd = new Random();
alpha = rnd.Next(0, 11) / 10.0;
}
// Calculate new initial solution
CluVRPSolution newSolution = constructGreedyRandomizedSolution(alpha);
// Local search
var totalLSWatch = System.Diagnostics.Stopwatch.StartNew();
double routeDistance = newSolution.totalCustomerRouteDistance;
for (int i = 0; i < 1; i++)
{
// Perform local searchs
this.localSearch(newSolution);
// For control of best iteration number
if (newSolution.totalCustomerRouteDistance < routeDistance)
{
newSolution.cluster_LSCycle_iterations = i;
routeDistance = newSolution.totalCustomerRouteDistance;
}
}
newSolution.customer_LSCycleTime += totalLSWatch.ElapsedMilliseconds;
// Update Best solution
if (newSolution.totalCustomerRouteDistance < solution.totalCustomerRouteDistance)
{
solution.setCostumerSolution(newSolution.customersPaths, newSolution.vehiculeRouteDistance);
solution.bestCustomerLSOrder = localSearchsOrder;
solution.customerLevelIterations = iterator;
solution.customer_LSCycle_iterations = newSolution.customer_LSCycle_iterations;
solution.customer_twoOpt_iterations = newSolution.customer_twoOpt_iterations;
solution.customer_relocate_iterations = newSolution.customer_relocate_iterations;
solution.customer_exchange_iterations = newSolution.customer_exchange_iterations;
solution.customer_swapCustomers_iterations = newSolution.customer_swapCustomers_iterations;
solution.customer_LSCycleTime = newSolution.customer_LSCycleTime;
solution.customer_twoOpt_time = newSolution.customer_twoOpt_time;
solution.customer_relocate_time = newSolution.customer_relocate_time;
solution.customer_exchange_time = newSolution.customer_exchange_time;
solution.customer_swapCustomers_time = newSolution.customer_swapCustomers_time;
}
// Increace iterator
iterator++;
}
//End
return;
}
/*
*
* Create a complete Greedy Randomized Solution (with cluster and customers)
*
*/
private CluVRPSolution constructGreedyRandomizedSolution(double alpha)
{
// Init variables
int[][] originalClusters = instance.clusters;
List <int>[] clusterRoute = solution.clusterRouteForVehicule;
List <int>[][] customersCircuit = new List <int> [clusterRoute.Length][];
Tuple <int, int> customersConnectClusters = new Tuple <int, int>(0, 0);
double[] vehiculeTotalDistance = new double[instance.vehicles];
// Start from depot
int startingCustomer = 1;
int[][][] clusterRouteWithCustomers = this.clusterRouteWithCustomers(originalClusters, clusterRoute);
// For each vehicule cluster-route
for (int vehicle = 0; vehicle < clusterRoute.Length; vehicle++)
{
// For each cluster in the i-vehicle route
int numbersOfClusters = clusterRoute[vehicle].Count;
int[][] clustersOneVehicle = clusterRouteWithCustomers[vehicle];
customersCircuit[vehicle] = new List <int> [numbersOfClusters];
// If vehicle has not travel
if (clusterRoute[vehicle].Count == 2)
{
customersCircuit[vehicle] = new List <int> [2];
customersCircuit[vehicle][0] = new List <int>();
customersCircuit[vehicle][1] = new List <int>();
customersCircuit[vehicle][0].Add(1);
customersCircuit[vehicle][1].Add(1);
vehiculeTotalDistance[vehicle] = 0;
continue;
}
// Visit all cluster for the i-vehicle
for (int i = 0; i < numbersOfClusters; i++)
{
// Add actual (initial of cluster) customer
customersCircuit[vehicle][i] = new List <int>();
customersCircuit[vehicle][i].Add(startingCustomer);
// For best customer to conect actual cluster to the next
// If the cluster only has 1 customer, you only have to know the
// customer for the next cluster
if (i + 1 < numbersOfClusters && clustersOneVehicle[i].Length > 1)
{
customersConnectClusters = bestCustomersBetween2Clusters(clustersOneVehicle[i], clustersOneVehicle[i + 1], startingCustomer);
}
else if (i + 1 < numbersOfClusters && clustersOneVehicle[i].Length == 1)
{
int nextCustomer = bestNextCustomer(startingCustomer, clustersOneVehicle[i + 1]);
customersConnectClusters = new Tuple <int, int>(startingCustomer, nextCustomer);
}
// Convert array to list
List <int> customersToVisit = clustersOneVehicle[i].OfType <int>().ToList();
// Remove initial and final customers
customersToVisit.Remove(startingCustomer);
customersToVisit.Remove(customersConnectClusters.Item1);
// While exists customers to visit
while (customersToVisit.Count > 0)
{
// Create RCL for customer
List <int> customerRCL = buildCustomerRCL(startingCustomer, customersToVisit, alpha);
// Select customer for RCL
int customerSelected = Functions.selectRandomElement(customerRCL);
// Add customer to the path
customersCircuit[vehicle][i].Add(customerSelected);
// Quit visited customer
customersToVisit.Remove(customerSelected);
// Set new actual customer
startingCustomer = customerSelected;
}
// Add final customer that connect to i+1 cluster
// In the final cluster the next customer is 0
// the it has not be added
if (clustersOneVehicle[i].Length > 1 && i + 1 < numbersOfClusters)
{
customersCircuit[vehicle][i].Add(customersConnectClusters.Item1);
}
// Next customer of next cluster
startingCustomer = customersConnectClusters.Item2;
}
// Calculte total inter-cluster distance
vehiculeTotalDistance[vehicle] = Functions.calculateTotalTravelDistance(customersCircuit, instance.customersDistanceMatrix, vehicle);
}
// Set solution
CluVRPSolution newSolution = new CluVRPSolution(instance);
newSolution.setCostumerSolution(customersCircuit, vehiculeTotalDistance);
// Return solution
return(newSolution);
}
// Main Function
static void GraspProcedure(string parametersFilePath, string instanceSetFilePath, string logFilePath, double[] solutionToCompare, CluVRPVersion cluVRPVersion)
{
// Star watch to calculate total process time
var totalWatch = System.Diagnostics.Stopwatch.StartNew();
// For watch for each instance execution
long elapsedMs = 0;
// Number of instances
int instancesNumber = solutionToCompare.Length;
// For best results
Dictionary <CluVRPInstance, CluVRPSolution> bestSolutionForInstance = new Dictionary <CluVRPInstance, CluVRPSolution>();
CluVRPSolution solution;
double[] bestSolutionTotalDistance = new double[instancesNumber];
double[] bestSolutionPropDistance = new double[instancesNumber];
double[] bestSolutionTime = new double[instancesNumber];
Functions.Populate(bestSolutionTotalDistance, double.MaxValue);
string[] bestSolutionParameters = new string[instancesNumber];
// For Average results
double[] totalDistance = new double[instancesNumber];
double[] totalTime = new double[instancesNumber];
double[] solutionAvgPropDistance = new double[instancesNumber];
int[] instanceOKsolutions = new int[instancesNumber];
// Get parameters to run instances
List <Parameters> parametersList = Parameters.parseParameterFile(parametersFilePath);
// Get logger
Logger logger = Logger.GetInstance();
// To logger verbose on/off
logger.setVerbose(false);
logger.setLogFilePath(logFilePath);
// Get instances
CluVRPInstance[] instancias = InstanceParser.loadGVRPSetOfInstances(instanceSetFilePath);
// Log run
logger.logLine("*****************************************************" + '\n' +
"* STARTING TEST:" + '\n' +
"* DATE -> " + DateTime.Now.ToString() + '\n' +
"* CONFIG -> " + parametersFilePath + '\n' +
"* SET INSTANCE -> " + instanceSetFilePath + '\n' +
"* LOG FILE -> " + logger.getLogFilePath() + '\n' +
"*****************************************************" + '\n');
// For each instance
int instanceIterator = 0;
foreach (CluVRPInstance instance in instancias)
{
// For this instance
double distance;
// Create new solution
solution = new CluVRPSolution(instance);
// For each parameter configuration
foreach (Parameters parameters in parametersList)
{
// Star watch to calculate time
var watch = System.Diagnostics.Stopwatch.StartNew();
// If CluVRP_Version in parameters is None(3) it has to be taken from command line
if (parameters.CluVRP_Version == CluVRPVersion.None)
{
parameters.CluVRP_Version = cluVRPVersion;
}
// Actual Parameter
string actualParameters = Functions.parametersToString(parameters);
//logger.logLine(actualParameters);
// Calculate solution for normal CluVRP or weak cluster constrains
solution = CluVRPGrasp.Grasp(instance, parameters);
// If not possible solution
if (solution.clusterRouteForVehicule == null)
{
continue;
}
// Increase instance OK solution iteration
instanceOKsolutions[instanceIterator]++;
// For this instance solution
distance = solution.totalCustomerRouteDistance;
// Izquierdo instances results are not integers
if (instance.instance_type == Instance.GoldenIzquierdo)
{
distance = Math.Truncate(100 * distance) / 100;
}
else
{
distance = Math.Truncate(distance);
}
// Stop timer watchers
watch.Stop();
// Set execution time
elapsedMs = watch.ElapsedMilliseconds;
double elapsedSeconds = Math.Round(elapsedMs * 1.0 / 1000, 2);
// Update solution results
totalDistance[instanceIterator] += distance;
totalTime[instanceIterator] += elapsedSeconds;
// Update individual instance solution
if (distance < bestSolutionTotalDistance[instanceIterator])
{
bestSolutionTotalDistance[instanceIterator] = distance;
bestSolutionPropDistance[instanceIterator] = (distance - solutionToCompare[instanceIterator]) * 100 / solutionToCompare[instanceIterator];
bestSolutionPropDistance[instanceIterator] = Math.Truncate(100 * bestSolutionPropDistance[instanceIterator]) / 100;
bestSolutionParameters[instanceIterator] = actualParameters;
bestSolutionTime[instanceIterator] = elapsedSeconds;
bestSolutionForInstance[instance] = solution;
}
}
// Calculate averages
double averageDistance = totalDistance[instanceIterator] / instanceOKsolutions[instanceIterator];
double averageTime = totalTime[instanceIterator] / instanceOKsolutions[instanceIterator];
averageTime = Math.Round(averageTime, 2);
double averagePropDistance = (averageDistance - solutionToCompare[instanceIterator]) * 100 / solutionToCompare[instanceIterator];
averagePropDistance = Math.Truncate(100 * averagePropDistance) / 100;
bestSolutionPropDistance[instanceIterator] = Math.Truncate(100 * bestSolutionPropDistance[instanceIterator]) / 100;
solutionAvgPropDistance[instanceIterator] = averagePropDistance;
// For log solution
string s_distance;
string s_averageDistance;
string s_bestSolutionPropDistance = bestSolutionPropDistance[instanceIterator].ToString("0.00");
string s_bestSolutionTime = bestSolutionTime[instanceIterator].ToString("0.00");
string s_averagePropDistance = averagePropDistance.ToString("0.00");
string s_averageTime = averageTime.ToString("0.00");
string s_fileName = Path.GetFileName(instancias[instanceIterator].file_name);
if (instance.instance_type == Instance.GoldenIzquierdo)
{
s_distance = bestSolutionTotalDistance[instanceIterator].ToString("0.00");
s_averageDistance = averageDistance.ToString("0.00");
}
else
{
s_distance = bestSolutionTotalDistance[instanceIterator].ToString("0");
s_averageDistance = averageDistance.ToString("0");
}
// Print solution
string outLine = s_fileName + '\t' + '\t' + s_distance + '\t' + s_bestSolutionPropDistance + "%" + '\t' + s_bestSolutionTime + "s" + '\t' + '\t' + s_averageDistance + '\t' + s_averagePropDistance + "%" + '\t' + s_averageTime + "s";
logger.logLine(outLine);
logger.logTimeAndIterations(solution, instance.file_name);
// Increase distance counter
instanceIterator++;
}
// Stop timer watchers
totalWatch.Stop();
var totalElapsedseconds = totalWatch.ElapsedMilliseconds / 1000;
// Total values
string s_totalPropBestDistance = (bestSolutionPropDistance.Sum() / instancesNumber).ToString("0.00");
string s_totalPropAvgDistance = (solutionAvgPropDistance.Sum() / instancesNumber).ToString("0.00");
// Show parameters for best solution
logger.logLine("");
logger.logLine("*************************************");
logger.logLine("* PARAMETERS FOR BEST SOLUTIONS: *");
logger.logLine("*************************************");
logger.logLine("TOTAL TIME -> " + totalElapsedseconds + " seconds");
logger.logLine("TOTAL BEST PROP DISTANCE -> " + s_totalPropBestDistance + "%");
logger.logLine("TOTAL AVG PROP DISTANCE -> " + s_totalPropAvgDistance + "%");
logger.logLine("");
for (int i = 0; i < bestSolutionParameters.Length; i++)
{
logger.logLine("-----------------------------------------------------------------");
logger.logLine("");
logger.logLine("CONFIGURATION FOR INSTANCE " + i);
logger.logLine("*****************************");
logger.logLine(bestSolutionParameters[i].ToString());
}
// Draw PNG solution
foreach (CluVRPInstance instance in bestSolutionForInstance.Keys)
{
//CluVRPSolution.solutionDrawPythonCode(instance, bestSolutionForInstance[instance]);
}
// Pause
//System.Console.ReadKey();
// End
return;
}
// Main Function
static void GraspProcedureCycleParameters(string parametersFilePath, string instanceSetFilePath, string logFilePath, double[] solutionToCompare)
{
// Star watch to calculate total process time
var totalWatch = System.Diagnostics.Stopwatch.StartNew();
// For watch for each instance execution
long elapsedMs = 0;
// For best solution set
Dictionary <CluVRPInstance, CluVRPSolution> bestSolutionForInstance = new Dictionary <CluVRPInstance, CluVRPSolution>();
CluVRPSolution solution;
double[] bestIndividualTotalDistance = new double[solutionToCompare.Length];
double[] bestIndividualPropDistance = new double[solutionToCompare.Length];
double[] bestIndividualTime = new double[solutionToCompare.Length];
Functions.Populate(bestIndividualTotalDistance, double.MaxValue);
string[] bestIndividualParameteres = new string[solutionToCompare.Length];
double[] bestSolPropDistances = new double[solutionToCompare.Length];
Functions.Populate(bestSolPropDistances, double.MaxValue);
double[] bestSolDistances = new double[solutionToCompare.Length];
double[] bestSolTimes = new double[solutionToCompare.Length];
List <List <LocalSearch> > bestSolClusterLSOrder = new List <List <LocalSearch> >();
List <List <LocalSearch> > bestSolCustomerLSOrder = new List <List <LocalSearch> >();
string bestSolParameters = "";
double totalAvg = double.MaxValue;
double totalAvgTime = double.MaxValue;
// Get parameters to run instances
List <Parameters> parametersList = Parameters.parseParameterFile(parametersFilePath);
// Get logger
Logger logger = Logger.GetInstance();
// To logger verbose on
logger.setVerbose(true);
logger.setLogFilePath(logFilePath);
// Get instances
CluVRPInstance[] instancias = InstanceParser.loadGVRPSetOfInstances(instanceSetFilePath);
// Log run
logger.logLine("*****************************************************" + '\n' +
"* STARTING TEST:" + '\n' +
"* CONFIG -> " + parametersFilePath + '\n' +
"* SET INSTANCE -> " + instanceSetFilePath + '\n' +
"* LOG FILE -> " + logger.getLogFilePath() + '\n' +
"******************************************************" + '\n');
// For each parameter configuration
foreach (Parameters parameters in parametersList)
{
// String for parameter set and print
logger.logLine("=============================================" + '\n' +
"= EXECUTING NEW TEST CASE =" + '\n' +
"=============================================" + '\n');
string actualParameters = Functions.parametersToString(parameters);
logger.logLine(actualParameters);
// For this configuration run
double[] propDistances = new double[solutionToCompare.Length];
double[] distances = new double[solutionToCompare.Length];
double[] times = new double[solutionToCompare.Length];
List <List <LocalSearch> > LSClusterOrder = new List <List <LocalSearch> >();
List <List <LocalSearch> > LSCustomerOrder = new List <List <LocalSearch> >();
// For each instance
int instanceCounter = 0;
foreach (CluVRPInstance instance in instancias)
{
// Star watch to calculate time
var watch = System.Diagnostics.Stopwatch.StartNew();
// Set max distance value
double distance = 0;
string fitAlgoBestSol = "";
// Create a new solution
solution = new CluVRPSolution(instance);
// Calculate solution for normal CluVRP or weak cluster constrains
solution = CluVRPGrasp.Grasp(instance, parameters);
// If not possible solution
if (solution.clusterRouteForVehicule == null)
{
logger.logLine(instance.file_name + '\t' + "No solution for this instance");
propDistances[instanceCounter] = 10;
instanceCounter++;
continue;
}
// Sets strings to show - FOR DEBUG
string s1 = instance.file_name + '\t' + solution.totalCustomerRouteDistance + '\t' +
parameters.Cluster_AlphaCapacity + '\t' + parameters.Cluster_AlphaDistance + '\t';
//Console.WriteLine(s1);
// For this instance solution
distance = solution.totalCustomerRouteDistance;
//solution.customersPaths[0][5][0] = 20;
//solution.customersPaths[0][5][1] = 19;
//distance = Functions.calculateTotalTravelDistance(solution.customersPaths, instance.customersDistanceMatrix);
if (instance.instance_type == Instance.GoldenIzquierdo)
{
distance = Math.Truncate(100 * distance) / 100;
}
else
{
distance = Math.Truncate(distance);
}
LSClusterOrder.Add(solution.bestClusterLSOrder);
LSCustomerOrder.Add(solution.bestCustomerLSOrder);
// Stop timer watchers
watch.Stop();
// Set execution time
elapsedMs = watch.ElapsedMilliseconds;
double elapsedSeconds = Math.Round(elapsedMs * 1.0 / 1000, 2);
// Update solution results
distances[instanceCounter] = distance;
propDistances[instanceCounter] = (distance - solutionToCompare[instanceCounter]) * 100 / solutionToCompare[instanceCounter];
propDistances[instanceCounter] = Math.Truncate(100 * propDistances[instanceCounter]) / 100;
times[instanceCounter] = elapsedSeconds;
// Update individual instance solution
if (distances[instanceCounter] < bestIndividualTotalDistance[instanceCounter])
{
bestIndividualTotalDistance[instanceCounter] = distances[instanceCounter];
bestIndividualPropDistance[instanceCounter] = (distance - solutionToCompare[instanceCounter]) * 100 / solutionToCompare[instanceCounter];
bestIndividualPropDistance[instanceCounter] = Math.Truncate(100 * bestIndividualPropDistance[instanceCounter]) / 100;
bestIndividualParameteres[instanceCounter] = actualParameters;
bestIndividualTime[instanceCounter] = elapsedSeconds;
bestSolutionForInstance[instance] = solution;
}
// Log solution
string s_distance;
if (instance.instance_type == Instance.GoldenIzquierdo)
{
s_distance = distance.ToString("0.00");
}
else
{
s_distance = distance.ToString("0");
}
string outLine = instance.file_name + '\t' + s_distance + '\t' + propDistances[instanceCounter] + "%" + '\t' + (elapsedMs * 1.0 / 1000).ToString("0.00") + "s" + '\t' + fitAlgoBestSol;
logger.logLine(outLine);
// Increase distance counter
instanceCounter++;
}
// Show total final proporcional differente and times
totalAvg = Math.Truncate(100 * propDistances.Sum() / propDistances.Length) / 100;
totalAvgTime = Math.Truncate(100 * times.Sum() / times.Length) / 100;
logger.logLine("");
logger.logLine("####################################");
logger.logLine("# TOTAL PROP DIFFERENCE -> " + totalAvg);
logger.logLine("# TOTAL TIME -> " + times.Sum().ToString("0.00"));
logger.logLine("# TOTAL AVERAGE TIME -> " + totalAvgTime.ToString("0.00"));
logger.logLine("####################################");
// Compare best AVG
if (propDistances.Sum() < bestSolPropDistances.Sum())
{
// Update to new best set solution
bestSolPropDistances = propDistances;
bestSolParameters = actualParameters;
bestSolDistances = distances;
bestSolTimes = times;
bestSolClusterLSOrder = LSClusterOrder;
bestSolCustomerLSOrder = LSCustomerOrder;
totalAvg = Math.Truncate(100 * bestSolPropDistances.Sum() / bestSolPropDistances.Length) / 100;
totalAvgTime = Math.Truncate(100 * bestSolTimes.Sum() / bestSolTimes.Length) / 100;
bestSolTimes = times;
// Show AVG distance
logger.logLine("");
logger.logLine("-----------------------------------------------------------------------");
logger.logLine("-------------------------NEW BEST DISTANCE-----------------------------");
logger.logLine("-----------------------------------------------------------------------");
//logger.logLine("NEW PROPORTIONAL BEST SET DISTANCE -> " + bestSolPropDistances.Sum());
logger.logLine("DISTANCES -> " + Functions.arrayToString(bestSolDistances));
logger.logLine("PROP DISTANCES -> " + Functions.arrayToString(bestSolPropDistances));
logger.logLine("TOTAL AVERAGE DIFERENCE DISTANCE -> " + totalAvg);
logger.logLine("TOTAL AVERAGE TIME -> " + totalAvgTime);
logger.logLine("-----------------------------------------------------------------------");
logger.logLine("-----------------------------------------------------------------------");
logger.logLine("-----------------------------------------------------------------------");
}
// New line for new config
logger.logLine("");
}
// Stop timer watchers
totalWatch.Stop();
var totalElapsedseconds = totalWatch.ElapsedMilliseconds / 1000;
// Log best solution
logger.logLine("");
logger.logLine("******************");
logger.logLine("* BEST SOLUTION: *");
logger.logLine("******************");
logger.logLine("TOTAL TIME -> " + totalElapsedseconds + " seconds");
//logger.logLine("PROPORTIONAL BEST SET DISTANCE -> " + bestSolPropDistances.Sum());
//logger.logLine("DISTANCES -> " + Functions.arrayToString(bestSolDistances));
//logger.logLine("PROPORTIONA DIFF DISTANCES -> " + Functions.arrayToString(bestSolPropDistances));
totalAvg = Math.Truncate(100 * bestSolPropDistances.Sum() / bestSolPropDistances.Length) / 100;
totalAvgTime = Math.Truncate(100 * bestSolTimes.Sum() / bestSolTimes.Length) / 100;
logger.logLine("TOTAL AVERAGE DIFERENCE DISTANCE -> " + totalAvg);
logger.logLine("TOTAL AVERAGE TIME -> " + totalAvgTime);
logger.logLine("");
logger.logLine("LIST OF RESULTS");
logger.logLine("***************");
for (int i = 0; i < bestSolDistances.Length; i++)
{
string outLine = instancias[i].file_name + '\t' + bestSolDistances[i] + '\t' + bestSolPropDistances[i] + "%" + '\t' + bestSolTimes[i];
logger.logLine(outLine);
}
logger.logLine("");
logger.logLine("PARAMETERS: ");
logger.logLine("***********");
logger.logLine(bestSolParameters);
logger.logLine("CLUSTER LS ORDER:");
logger.logLine("*****************");
for (int i = 0; i < bestSolClusterLSOrder.Count; i++)
{
logger.logLine((Functions.arrayToString(bestSolClusterLSOrder[i])));
}
logger.logLine("");
logger.logLine("CUSTOMER LS ORDER:");
logger.logLine("******************");
for (int i = 0; i < bestSolCustomerLSOrder.Count; i++)
{
logger.logLine((Functions.arrayToString(bestSolCustomerLSOrder[i])));
}
logger.logLine("");
logger.logLine("");
logger.logLine("============================");
logger.logLine("= BEST INDIVIDUAL RESULTS =:");
logger.logLine("============================");
totalAvg = Math.Truncate(100 * bestIndividualPropDistance.Sum() / bestIndividualPropDistance.Length) / 100;
totalAvgTime = Math.Truncate(100 * bestIndividualTime.Sum() / bestIndividualTime.Length) / 100;
//logger.logLine("DISTANCES -> " + Functions.arrayToString(bestIndividualTotalDistance));
//logger.logLine("PROPORTIONA DIFF DISTANCES -> " + Functions.arrayToString(bestIndividualPropDistance));
//logger.logLine("PROPORTIONAL TOTAL DISTANCE -> " + bestIndividualPropDistance.Sum().ToString("00.00"));
logger.logLine("TOTAL AVERAGE DIFERENCE DISTANCE -> " + totalAvg);
logger.logLine("TOTAL AVERAGE TIME -> " + totalAvgTime);
logger.logLine("");
logger.logLine("LIST OF RESULTS");
logger.logLine("***************");
for (int i = 0; i < bestIndividualTotalDistance.Length; i++)
{
string outLine = instancias[i].file_name + '\t' + bestIndividualTotalDistance[i] + '\t' + bestIndividualPropDistance[i] + "%" + '\t' + bestIndividualTime[i];
logger.logLine(outLine);
}
logger.logLine("");
for (int i = 0; i < bestSolClusterLSOrder.Count; i++)
{
logger.logLine("-----------------------------------------------------------------");
logger.logLine("");
logger.logLine("CONFIGURATION FOR INSTANCE " + i);
logger.logLine("*****************************");
logger.logLine(bestIndividualParameteres[i].ToString());
}
foreach (CluVRPInstance instance in bestSolutionForInstance.Keys)
{
CluVRPSolution.solutionDrawPythonCode(instance, bestSolutionForInstance[instance]);
}
// Pause
//System.Console.ReadKey();
// End
return;
}
// Write a python script to draw the solution
public static void solutionDrawPythonCode(CluVRPInstance instance, CluVRPSolution solution)
{
// Lines of file
List <string> lines = new List <string>();
// Add initial lines to script
lines.Add("import matplotlib.pyplot as plt");
lines.Add("plt.figure(num=None, figsize=(24, 16), dpi=120, facecolor='w', edgecolor='k')");
// For python vars
string[] abc = new string[] { "a", "b", "c", "d", "f", "g", "h", "i", "j", "k", "l", "m", "k", "r", "t", "u", "v", "w", "y", "z",
"aa", "ab", "ac", "ad", "af", "ag", "ah", "ai", "aj", "ak", "al", "am", "ak", "ar", "at", "au", "av", "aw", "ay", "az",
"ca", "cb", "cc", "cd", "cf", "cg", "ch", "ci", "cj", "ck", "cl", "cm", "ck", "cr", "ct", "cu", "cv", "cw", "cy", "cz" };
// Array of colors
string[] colors = new string[] { "aqua", "bisque", "black", "blanchedalmond", "blue", "blueviolet", "brown", "burlywood", "cadetblue", "chartreuse", "chocolate", "coral", "cornflowerblue", "cornsilk", "crimson", "cyan", "darkblue", "darkcyan", "darkgoldenrod", "darkgray", "darkgreen", "darkgrey", "darkkhaki", "darkmagenta", "darkolivegreen", "darkorange", "darkorchid", "darkred", "darksalmon", "darkseagreen", "darkslateblue", "darkslategray", "darkslategrey", "darkturquoise", "darkviolet", "deeppink", "deepskyblue", "dimgray", "dimgrey", "dodgerblue", "firebrick", "floralwhite", "forestgreen", "fuchsia", "gainsboro", "ghostwhite", "gold", "goldenrod", "gray", "green", "greenyellow", "grey", "honeydew", "hotpink", "indianred", "indigo", "ivory", "khaki", "lavender", "lavenderblush", "lawngreen", "lemonchiffon", "lightblue", "lightcoral", "lightcyan", "lightgoldenrodyellow", "lightgray", "lightgreen", "lightgrey", "lightpink", "lightsalmon", "lightseagreen", "lightskyblue", "lightslategray", "lightslategrey", "lightsteelblue", "lightyellow", "lime", "limegreen", "linen", "magenta", "maroon", "mediumaquamarine", "mediumblue", "mediumorchid", "mediumpurple", "mediumseagreen", "mediumslateblue", "mediumspringgreen", "mediumturquoise", "mediumvioletred", "midnightblue", "mintcream", "mistyrose", "moccasin", "navajowhite", "navy", "oldlace", "olive", "olivedrab", "orange", "orangered", "orchid", "palegoldenrod", "palegreen", "paleturquoise", "palevioletred", "papayawhip", "peachpuff", "peru", "pink", "plum", "powderblue", "purple", "rebeccapurple", "red", "rosybrown", "royalblue", "saddlebrown", "salmon", "sandybrown", "seagreen", "seashell", "sienna", "silver", "skyblue", "slateblue", "slategray", "slategrey", "snow", "springgreen", "steelblue", "tan", "teal", "thistle", "tomato", "turquoise", "violet", "wheat", "white", "whitesmoke", "yellow", "yellowgreen" };
string[] vehicle_colors = new string[] { "red", "green", "blue", "orange", "purple", "cyan", "magenta", "lime", "pink", "teal", "lavender", "brown", "beige", "maroon", "mint", "olive", "coral", "navy", "grey", "white", "black" };
// Variables
int colorIt = 0;
int var1 = 0;
int var2 = 1;
string outFileName = instance.file_name + ".py";
// Code for draw the customers points
// All customers of one cluster have the same color
string array1 = abc[var1] + "= [" + instance.nodes[0].x.ToString().Replace(',', '.') + "]";
string array2 = abc[var2] + "= [" + instance.nodes[0].y.ToString().Replace(',', '.') + "]";
string array3 = "plt.plot(" + abc[0] + "," + abc[1] + ",'ro', markersize= 44, color = '" + colors[0] + "')";
lines.Add(array1);
lines.Add(array2);
lines.Add(array3);
var1 = var1 + 2;
var2 = var2 + 2;
colorIt++;
for (int clusterIt = 1; clusterIt < instance.clusters.Length; clusterIt++)
{
array1 = abc[var1] + "= [";
array2 = abc[var2] + "= [";
for (int customerIt = 0; customerIt < instance.clusters[clusterIt].Length; customerIt++)
{
int customer = instance.clusters[clusterIt][customerIt] - 1;
array1 += instance.nodes[customer].x.ToString().Replace(',', '.') + ",";
array2 += instance.nodes[customer].y.ToString().Replace(',', '.') + ",";
}
StringBuilder sb = new StringBuilder(array1);
sb[sb.Length - 1] = ']';
array1 = sb.ToString();
sb = new StringBuilder(array2);
sb[sb.Length - 1] = ']';
array2 = sb.ToString();
array3 = "plt.plot(" + abc[var1] + "," + abc[var2] + ",'ro', color = '" + colors[colorIt] + "')";
// For centroides
var1 = (var1 + 2) % abc.Length;
var2 = (var1 + 2) % abc.Length;
string array4 = abc[var1] + "= [";
string array5 = abc[var2] + "= [";
array4 += instance.clustersCentroid[clusterIt].Item1.ToString().Replace(',', '.') + "]";
array5 += instance.clustersCentroid[clusterIt].Item2.ToString().Replace(',', '.') + "]";
string array6 = "plt.plot(" + abc[var1] + "," + abc[var2] + ",'ro',markersize=22, color = '" + colors[colorIt] + "')";
// Write python code for clusters and customers
lines.Add(array1);
lines.Add(array2);
lines.Add(array3);
lines.Add(array4);
lines.Add(array5);
lines.Add(array6);
// Change color and variables name for next cluster
var1 = (var1 + 2) % abc.Length;
var2 = (var2 + 2) % abc.Length;
colorIt = (colorIt + 1) % colors.Length;
}
// Draw vehicle travel
var1 = 0;
var2 = 1;
colorIt = 0;
for (int vehicle = 0; vehicle < solution.customersPaths.Length; vehicle++)
{
array1 = abc[var1] + "= [";
array2 = abc[var2] + "= [";
for (int clusterIt = 0; clusterIt < solution.customersPaths[vehicle].Length; clusterIt++)
{
for (int customerIt = 0; customerIt < solution.customersPaths[vehicle][clusterIt].Count; customerIt++)
{
int customer = solution.customersPaths[vehicle][clusterIt][customerIt] - 1;
array1 += instance.nodes[customer].x.ToString().Replace(',', '.') + ",";
array2 += instance.nodes[customer].y.ToString().Replace(',', '.') + ",";
}
}
StringBuilder sb = new StringBuilder(array1);
sb[sb.Length - 1] = ']';
array1 = sb.ToString();
sb = new StringBuilder(array2);
sb[sb.Length - 1] = ']';
array2 = sb.ToString();
array3 = "plt.plot(" + abc[var1] + "," + abc[var2] + ", color = '" + vehicle_colors[colorIt] + "')";
// Write python code for clusters and customers
lines.Add(array1);
lines.Add(array2);
lines.Add(array3);
// Change color and variables name for next cluster
var1 = (var1 + 2) % abc.Length;
var2 = (var2 + 2) % abc.Length;
colorIt = (colorIt + 1) % vehicle_colors.Length;
}
// Add final line
lines.Add("plt.savefig('" + instance.file_name + ".png')");
// Write file
try
{
System.IO.File.WriteAllLines(outFileName, lines);
}
catch (Exception e)
{
Console.WriteLine(e.ToString());
}
// Excute script
System.Diagnostics.Process.Start(PYTHON_PATH, outFileName);
}
// Try to swap all the clusters (one bye one) for all vehicles (one by one)
public static void swapVehicle(CluVRPSolution solution, CluVRPInstance instance, Parameters parameters)
{
// Cluster demand
int[] clusterDemand = instance.clusters_demand;
// More than 1 vehicle is needed
if (solution.clusterRouteForVehicule.Length < 2)
{
return;
}
// Main cycle
int iterations = 0;
while (iterations < parameters.CluVRP_LS_SwapVehicle)
{
// For random order on iterations
List <int> rndPosition = new List <int>();
for (int i = 0; i < solution.clusterRouteForVehicule.Length; i++)
{
rndPosition.Add(i);
}
Functions.Shuffle <int>(new Random(), rndPosition);
// For each vehicle 1
for (int vehicleIt1 = 0; vehicleIt1 < solution.clusterRouteForVehicule.Length; vehicleIt1++)
{
// For each vehicle 2
for (int vehicleIt2 = 0; vehicleIt2 < solution.clusterRouteForVehicule.Length; vehicleIt2++)
{
// When is not the same vehicle
if (vehicleIt1 != vehicleIt2)
{
// Select vehicle index from random position
int vehicle1 = rndPosition[vehicleIt1];
int vehicle2 = rndPosition[vehicleIt2];
// For each cluster 1 on vehicle 1
for (int cluster1 = 1; cluster1 < solution.clusterRouteForVehicule[vehicle1].Count; cluster1++)
{
// For each cluster 2 on vehicle 2
for (int cluster2 = 1; cluster2 < solution.clusterRouteForVehicule[vehicle2].Count; cluster2++)
{
// Calculate the space on vehicle if make a swap
int clusterSwappedV1 = solution.clusterRouteForVehicule[vehicle1][cluster1];
int clusterSwappedV2 = solution.clusterRouteForVehicule[vehicle2][cluster2];
int newSpaceV1 = solution.vehicleRemSpace[vehicle1] + clusterDemand[clusterSwappedV1] - clusterDemand[clusterSwappedV2];
int newSpaceV2 = solution.vehicleRemSpace[vehicle2] + clusterDemand[clusterSwappedV2] - clusterDemand[clusterSwappedV1];
// If swap is possible
if (newSpaceV1 > 0 && newSpaceV2 > 0 && clusterSwappedV1 != 0 && clusterSwappedV2 != 0 && clusterSwappedV1 != clusterSwappedV2)
{
int lastClusterSwappedV1 = solution.clusterRouteForVehicule[vehicle1][cluster1 - 1];
int lastClusterSwappedV2 = solution.clusterRouteForVehicule[vehicle2][cluster2 - 1];
int nextClusterSwappedV1 = solution.clusterRouteForVehicule[vehicle1][cluster1 + 1];
int nextClusterSwappedV2 = solution.clusterRouteForVehicule[vehicle2][cluster2 + 1];
// Calculate old distances for each vehicle
double oldDistanceVehicle1 = Functions.calculateCustomerTotalTravelDistanceForVehicle(solution.customersPaths[vehicle1], instance.customersDistanceMatrix, instance);
double oldDistanceVehicle2 = Functions.calculateCustomerTotalTravelDistanceForVehicle(solution.customersPaths[vehicle2], instance.customersDistanceMatrix, instance);
// Swap clusters
List <int> cluster1Swp = solution.customersPaths[vehicle1][cluster1];
List <int> cluster2Swp = solution.customersPaths[vehicle2][cluster2];
solution.customersPaths[vehicle1][cluster1] = cluster2Swp;
solution.customersPaths[vehicle2][cluster2] = cluster1Swp;
// Calculate new distances for each vehicle
double newDistanceVehicle1 = Functions.calculateCustomerTotalTravelDistanceForVehicle(solution.customersPaths[vehicle1], instance.customersDistanceMatrix, instance);
double newDistanceVehicle2 = Functions.calculateCustomerTotalTravelDistanceForVehicle(solution.customersPaths[vehicle2], instance.customersDistanceMatrix, instance);
// Calculate new total distance
double newDistance = solution.totalClusterRouteDistance - (oldDistanceVehicle1 + oldDistanceVehicle2) + (newDistanceVehicle1 + newDistanceVehicle2);
// If new distance is short
if (newDistance + 0.0001 < solution.totalClusterRouteDistance)
{
// Update distance and space remaining
solution.totalClusterRouteDistance = newDistance;
solution.vehicleRemSpace[vehicle1] = newSpaceV1;
solution.vehicleRemSpace[vehicle2] = newSpaceV2;
solution.clusterRouteForVehicule[vehicle1][cluster1] = clusterSwappedV2;
solution.clusterRouteForVehicule[vehicle2][cluster1] = clusterSwappedV1;
// Reset iterator
if (solution.cluvrp_swapVehicle_iterations <= iterations)
{
solution.cluster_insertVehicle_iterations = iterations;
}
iterations = 0;
// DEBUG
Logger.GetInstance().logLine("DEBUG - Improve on swapVehicle");
}
// If new distance is not short
else
{
// Undo swap
solution.customersPaths[vehicle1][cluster1] = cluster1Swp;
solution.customersPaths[vehicle2][cluster2] = cluster2Swp;
// Increase iterator
iterations++;
}
}
else
{
iterations++;
}
// End if swap is possible
} // End for cluster 2
} // End for cluster 1
} // End if is not the same vehicle
} // End for vehicle 2
} // End for vehicle 1
}
//End
return;
}
// Main GRASP handle main iteration depending if use Complete version or TwoPhase
public static CluVRPSolution Grasp(CluVRPInstance instance, Parameters parameters)
{
// For best solution
CluVRPSolution bestSolution = new CluVRPSolution(instance);
// Main cycle
int iterator = 0;
while (iterator < parameters.CluVRP_GRASPIterations)
{
// New Grasp for Cluster level instance
ClusterGRASP clusterGrasp = new ClusterGRASP(instance, parameters);
// Execute Grasp procedure
var totalWatch = System.Diagnostics.Stopwatch.StartNew();
CluVRPSolution cluVRPSolution = clusterGrasp.Grasp();
cluVRPSolution.clusterLevelTime = totalWatch.ElapsedMilliseconds;
// If solutions is not available continue with next iteration
if (cluVRPSolution.clusterRouteForVehicule == null)
{
iterator++;
continue;
}
// Verify if cluster solution is correct
cluVRPSolution.verifyClusterSolution(instance);
// New Grasp for Cluster level instance
CustomerLevel customerGrasp = null;
if (parameters.CluVRP_Version == CluVRPVersion.Strong)
{
customerGrasp = new CustomerStrongGRASP(instance, cluVRPSolution, parameters);
}
else if (parameters.CluVRP_Version == CluVRPVersion.Weak)
{
customerGrasp = new CustomerWeakGRASP(instance, cluVRPSolution, parameters);
}
// Execute Grasp procedure
totalWatch = System.Diagnostics.Stopwatch.StartNew();
customerGrasp.Grasp();
cluVRPSolution.customerLevelTime = totalWatch.ElapsedMilliseconds;
//if solution is not available continue with next iteration
if (cluVRPSolution.customersPaths == null && cluVRPSolution.customersWeakRoute == null)
{
continue;
}
// Perform LS
if ((parameters.CluVRP_LS_SwapClusters > 0 || parameters.CluVRP_LS_SwapVehicle > 0) && parameters.CluVRP_Version == CluVRPVersion.Strong)
{
cluVRPLocalSearchs(cluVRPSolution, instance, parameters);
}
// Verify if customer solution is correct
if (parameters.CluVRP_Version == CluVRPVersion.Strong)
{
cluVRPSolution.verifyCustomerSolution(instance);
}
else if (parameters.CluVRP_Version == CluVRPVersion.Weak)
{
cluVRPSolution.verifyCustomerWeakSolution(instance);
}
// Update best solution
if (cluVRPSolution.totalCustomerRouteDistance < bestSolution.totalCustomerRouteDistance)
{
bestSolution = cluVRPSolution;
bestSolution.cluVRPIterations = iterator;
}
// Increase iterator
iterator++;
}
// Return best solution
return(bestSolution);
}
// Swap Cluster heuristic (diff to the cluster levels because the customers paths has been builded)
public static void swapClusters(CluVRPSolution solution, CluVRPInstance instance, Parameters parameters)
{
// Calculate old distances for each vehicle
double[] bestVehicleDistance = new double[solution.clusterRouteForVehicule.Length];
for (int vehicleIt = 0; vehicleIt < bestVehicleDistance.Length; vehicleIt++)
{
bestVehicleDistance[vehicleIt] = Functions.calculateTotalTravelDistance(solution.customersPaths, instance.customersDistanceMatrix, vehicleIt);
}
// For each vehicle
for (int vehicle = 0; vehicle < solution.customersPaths.Length; vehicle++)
{
// Main cycle
while (true)
{
// If solution improves try with new iteration
bool solutionImproves = false;
// For each cluster1
for (int clusterIt1 = 1; clusterIt1 + 1 < solution.customersPaths[vehicle].Length; clusterIt1++)
{
// For each cluster2
for (int clusterIt2 = 1; clusterIt2 + 1 < solution.customersPaths[vehicle].Length; clusterIt2++)
{
// If is not the same cluster
if (clusterIt1 != clusterIt2)
{
// Calculate last and next cluster for cluster1
List <int> cluster1Last = solution.customersPaths[vehicle][clusterIt1 - 1];
List <int> cluster1 = solution.customersPaths[vehicle][clusterIt1];
List <int> cluster1Next = solution.customersPaths[vehicle][clusterIt1 + 1];
// Calculate last and next cluster for cluster2
List <int> cluster2Last = solution.customersPaths[vehicle][clusterIt2 - 1];
List <int> cluster2 = solution.customersPaths[vehicle][clusterIt2];
List <int> cluster2Next = solution.customersPaths[vehicle][clusterIt2 + 1];
// Perform swap
solution.customersPaths[vehicle][clusterIt1] = cluster2;
solution.customersPaths[vehicle][clusterIt2] = cluster1;
Functions.Swap(solution.clusterRouteForVehicule[vehicle], clusterIt1, clusterIt2);
// Calculate new distance
double newDistance = Functions.calculateTotalTravelDistance(solution.customersPaths, instance.customersDistanceMatrix, vehicle);
// If new distance is not better
if (solution.vehiculeRouteDistance[vehicle] <= newDistance + 0.00001)
{
// Back the changes
solution.customersPaths[vehicle][clusterIt1] = cluster1;
solution.customersPaths[vehicle][clusterIt2] = cluster2;
Functions.Swap(solution.clusterRouteForVehicule[vehicle], clusterIt1, clusterIt2);
}
else
{
// If new solution is better update distance
solution.vehiculeRouteDistance[vehicle] = newDistance;
solutionImproves = true;
}
}
}
}
// If solution not improves jump to next vehicle
if (!solutionImproves)
{
break;
}
}
}
// End
return;
}
