No abstract

This paper addresses the periodic heterogeneous vehicle routing problem (PHVRP), an extension of the classical vehicle routing problems (VRP). This problem is known to be confined to various real-world instances where each customer's demand should be served within a specific time horizon and a maximum demand quantity that can be delivered at each visit. The heterogeneous capacitated vehicles are available to perform the services for each customer. This paper aims to minimize the total traveling time of routes for all vehicles over the time horizon so that the customers' demands can be delivered. Thus, a novel coding scheme is also proposed to directly convert a random sequence of integers into a feasible solution, which is then embedded into algorithms. Furthermore, this paper also compares the performance of the Genetic Algorithm (GA) with the particle swarm optimization algorithm (PSO). The numerical results of the experiments show that the proposed GA is superior to PSO. However, the computation time of PSO is faster than GA.