Utilizing ant colony optimization and intelligent water drop for solving multi depot vehicle routing problem

Abstract

Multi-depot vehicle routing problem (MDVRP) is a real-world variant of the vehicle routing problem (VRP). MDVRP falls under NP-hard problem where trouble in identifying the routes for the vehicles from multiple depots to the customers and then, returning to the similar depot. The challenging task in solving MDVRP is to identify optimal routes for the fleet of vehicles located at the depots to transport customers' demand efficiently. In this paper, two metaheuristic methods have been tested for MDVRP which are Ant Colony Optimization (ACO) and Intelligent Water Drop (IWD). The proposed algorithms are validated using six MDVRP Cordeau's data sets which are P01, P03, P07, P10, P15 and P21 with 50, 75, 100, 249, 160 and 360 customers, respectively. Thus, the results using the proposed algorithm solving MDVRP, five out of six problem data sets showed that IWD is more capable and efficient compared to ACO algorithm.