A Hybrid Differential Evolution -iterative Greedy Search Algorithm for Capacitated Vehicle Routing Problem

This paper presents a novel and efficient approach to solve the capacitated vehicle routing problem (CVRP) including distance constraints. CVRP is an optimization problem with an objective to determine the optimal set of routes for a set of vehicles to deliver goods to the customers in order to minimize the total cost while satisfying various operational constraints. The decision variables of this problem are first encoded as a string called decision vector. The decision vector of the proposed algorithm consists of a sequence of integer numbers representing the customers for each vehicle in CVRP. The proposed method is developed in such a way that differential evolution is acting as a main optimizer to identify the optimal set of customers for a fleet of homogeneous vehicles. The destruction and construction phases of iterative greedy algorithm are incorporated to identify the optimal sequence of visit for each vehicle in the proposed algorithm which improves the fitness of each vector in the population of differential evolution algorithm. A computational study is carried out on Christofide and Eilon benchmark problems to illustrate the performance of the proposed method. The feasibility of the proposed method is demonstrated and compared with reported algorithm in the literature. The results are promising and show the effectiveness of the proposed method.