A HYBRID ALGORITM WITH GENETIC ALGORITHM AND ANT COLONY OPTIMIZATION FOR SOLVING MULTI-DEPOT VEHICLE ROUTING PROBLEMS

Abstract

Vehicle routing problems are very important issue for logistics sector. Vehicle routing problems have various types such as time windows, multiple-depot, stochastic demand, backhauls, simultaneous delivery and pick up, distance constraint. etc. The objectives of all these problems are to design optimal routes minimizing total traveled distance, minimizing number of vehicles which are used for the solution that satisfy corresponding constraints. In this study, for the solution of the multi-depot vehicle routing problem, a new hybrid metaheuristic structure is proposed with ant colony optimization and genetic algorithm. The aim of the problem is to minimize the total traveled distance by the all vehicles. The metaheuristic structure of the multi-depot vehicle routing problem solution consists of two phases. In the first phase for grouping Thangiah and Salhi's (2001) genetic clustering method is developed and in the second phase for routing Gambardella and Dorigo's (1997) ant colony system approach is used. The proposed metaheuristic method is tested with the Cordeau et al.'s (1997) problem sets and the results are compared with the other solution techniques in the literature.