/*
*
* 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);
}
/*
*
* 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;
// Create a customer path for each vehicle
List <int>[] customersOnVehicle = buildCustomersOnVehicle();
// 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(customersOnVehicle, alpha);
// Local search
var totalLSWatch = System.Diagnostics.Stopwatch.StartNew();
double routeDistance = newSolution.totalCustomerRouteDistance;
for (int i = 0; i < 1; i++)
{
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.setWeakCostumerSolution(newSolution.customersWeakRoute, newSolution.vehiculeRouteDistance);
solution.bestCustomerLSOrder = localSearchsOrder;
solution.customerLevelIterations = iterator;
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;
}
