An Improved Particle Swarm Optimization Algorithm for Vehicle Routing Problem with Time Windows

Vehicle routing problem with time windows (VRPTW) is of crucial importance in today's industries, accounting for a significant portion of many distribution and transportation systems. In this paper, we present a computational-efficient VRPTW algorithm, which is based on the principles of particle swarm optimization (PSO). PSO follows a collaborative population-based search, which models over the social behavior of bird flocking and fish schooling. PSO system combines local search methods (through self experience) with global search methods (through neighboring experience), attempting to balance exploration and exploitation. We discuss the adaptation and implementation of the PSO search strategy to VRPTW and provide a numerical experiment to show the effectiveness of the heuristic. Experimental results indicate that the new PSO algorithm can effectively and quickly get optimal resolution of VRPTW.