Solving Capacitated Vehicle Routing Problem with route optimization using Swarm Intelligence

Abstract

Capacitated Vehicle Routing Problem (CVRP) is a real life constrain satisfaction problem in which customers are optimally assign to individual vehicles (considering their capacity) to keep total travel distance of the vehicles as minimum as possible while serving customers. Various methods are investigated to solve CVRP in last few decades, the most popular way of solving CVRP is splitting the task into two different phases: firstly, assigning customers under different vehicles and secondly, finding optimal route of each vehicle. Sweep clustering algorithm is well studied for clustering nodes. On the other hand, route optimization is simply a traveling salesman problem (TSP) and a number of TSP optimization methods are investigated for this purpose. This study investigates a variant of Sweep algorithm for clustering nodes and different SI based methods for route generation to get optimal CVRP solution. In conventional Sweep algorithm, cluster formation starts from 0° and consequently advance toward 360° to consider all the nodes. In this study, Sweep cluster are considered from different starting angle. On the other hand, two well-known Swarm Intelligence (SI) methods (i.e., Ant Colony Optimization and Particle Swarm Optimization (PSO)) and two recent SI based algorithms (i.e., Producer-Scrounger Method and Velocity Tentative PSO) are considered for route optimization. We have compared the performance of these methods to solve CVRP. The experimental results on a large number of benchmark CVRP reveal that different starting angle has positive effect on Sweep clustering and finally, VTPSO is able to produce better solution than other methods.