//Apllica l'algoritmo Simulated Annealing per ottenere un'ipotetica soluzione migliore
public static Plan SA(Instance instance, Plan best_plan, double t_max, int max_it)
{
Console.WriteLine("--------------------------------");
Console.WriteLine("SIMULATED ANNEALING");
double best_obj = best_plan.QualityPlan().tot_rank;
double t0 = best_obj * t_max;
double tf = t0 * t_max;
double t = t0;
double c = Math.Pow(tf / t0, 1 / (double)max_it);
Plan current_plan = Plan.Copy(best_plan);
for (int i = 0; i < max_it; i++)
{
Plan neighbor_plan = Plan.Copy(current_plan);
neighbor_plan = Heuristics.Ruin(instance, neighbor_plan, k_ruin);
neighbor_plan = Heuristics.Recreate(instance, neighbor_plan, noise);
List <Plan> ps = Heuristics.CompareSA(best_plan, neighbor_plan, current_plan, t);
best_plan = (ps[0] != null) ? Plan.Copy(ps[0]) : best_plan;
current_plan = (ps[1] != null) ? Plan.Copy(ps[1]) : current_plan;
t *= c;
}
return(best_plan);
}
public Plan SA(Instance inst, int k, int noise, double temp = 0.01, int max_it = 100)
{
Plan best_plan = Heuristics.CreateInitialPlan(inst, noise, max_it);
double best_obj = best_plan.QualityPlan().tot_rank;
double t0 = best_obj * temp;
double tf = t0 * temp;
double t = t0;
double c = Math.Pow(tf / t0, 1 / (double)max_it);
Plan current_plan = Plan.Copy(best_plan);
for (int i = 0; i < max_it; i++)
{
Plan neighbor_plan = Plan.Copy(current_plan);
neighbor_plan = Heuristics.Ruin(inst, neighbor_plan, k);
neighbor_plan = Heuristics.Recreate(inst, neighbor_plan, noise);
List <Plan> ps = Heuristics.CompareSA(best_plan, neighbor_plan, current_plan, t);
best_plan = (ps[0] != null) ? Plan.Copy(ps[0]) : best_plan;
current_plan = (ps[1] != null) ? Plan.Copy(ps[1]) : current_plan;
t *= c;
}
return(best_plan);
}