Grey Wolf Optimizer algorithm for solving the multi depot vehicle routing problem and its implementation

Abstract

Abstract Multi Depot Vehicle Routing Problem (MDVRP) is one of Vehicle Routing Problem (VRP) variants. MDVRP is a VRP that uses more than one depot for the distribution process. In this paper, the MDVRP will be solved using the Grey Wolf Optimizer (GWO). This algorithm is inspired by hunting technique and hierarchy of grey wolves. The completion of MDVRP consists of two stages, they are grouping and routing. In the grouping stage, customers are grouped to nearest depot. In the routing stage, the route is determined at each depot using “route-first, cluster-second”. In the route-first phase a giant tour is formed by forming population array. In the cluster-second phase, a split will be conducted on the giant tour, so that a number of routes are formed with the total demand for each route does not exceed the vehicle capacity. The implementation of GWO algorithm for MDVRP was designed in Borland Delphi 7.0 programming language. The algorithm was tested using 2 depots and 9 customers formed 2 routes at each depot with a total distance of 833 km. Another test was carried out by comparing the GWO with the ACO using the Cordeau dataset. GWO gives the best result (shortest distance) than ACO.