Comparing different metaheuristic methods in solving a real-world application of vehicle routing problem

Abstract

This research applied an extension of the Vehicle Routing Problem (VRP) to optimize the distribution processes of a supermarket chain. This model is a combination of CVRP (Capacitated VRP), MDVRP (Multi-depot VRP), and HFVRP (Heterogenous fleet VRP). The applied model aims to minimize the distribution cost of the selected supermarket chain. All the constraints of the VRP model were defined based on the operational practices of the application. The research aimed to compare the performances of three metaheuristic methods, Simulated Annealing (SA), Tabu Search (TS), and Guided Local Search (GLS) in optimizing the real-world application. Results highlighted that GLS outperformed in terms of the quality of the solutions and the computation time in optimizing the selected distribution network. This research is significant because it tests both the VRP model and the three metaheuristic methods using a real-world industry application.