Hybrid particle swarm optimization for vehicle routing problem with time windows

Vehicle routing problem with Time Window (VRPTW) has received much attention by researchers in solving many scheduling applications for transportation and logistics. The objective of VRPTW is to use a fleet of vehicles with specific capacity to serve a number of customers with various demands and time window constraints. As a non-polynomial (NP) hard problem, the VRPTW is complex and time consuming, especially when it involves a large number of customers and constraints. This paper presents a hybrid approach between Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) for solving VRPTW. The reason for hybridization is to overcome the problem of premature convergence that exists in standard PSO. Premature convergence often yields partially optimized solutions because of particles stagnation. The proposed hybrid PSO implements a mechanism that automatically trigger swarm condition which will liberate particles from sub-optimal solutions hence enabling progress toward the maximum best solution. A computational experiment has been carried out by running the hybrid PSO with the VRPTW benchmark data set. The results indicate that the algorithm can produce some improvement when compared to the original PSO.