Capacitated Vehicle Routing Problem Solving through Adaptive Sweep Based Clustering plus Swarm Intelligence based Route Optimization

Abstract

Capacitated Vehicle Routing Problem (CVRP) is anoptimization task where customers are assigned to vehicles aiming that combined travel distances of all the vehicles as minimum as possible while serving customers. A popular way among various methods of CVRP is solving it in two phases: grouping or clustering customers into feasible routes of individual vehicles and then finding their optimal routes. Sweep is well studied clustering algorithm for grouping customers and different traveling salesman problem (TSP) solving methods are commonly used to generate optimal routes of individual vehicles. This study investigates effective CVRP solving method based on recently developed adaptive Sweep and prominent Swarm Intelligence (SI) based TSP optimization methods. The adaptive Sweep cluster is a heuristic based adaptive method to select appropriate cluster formation starting angle of Sweep. Three prominent SI based TSP optimization methods are investigated which are Ant Colony Optimization, Producer-Scrounger Method and Velocity Tentative Particle Swarm Optimization (VTPSO). Genetic Algorithm is also considered since it is the pioneer and well-known population based method. The experimental results on two suites of benchmark CVRPs identified the effectiveness of adaptive Sweep plus SI methods in solving CVRP. Finally, adaptive Sweep plus the VTPSO is found better than other tested methods in this study as well as several other prominent existing methods.