Solving Capacitated Vehicle Routing Problem through Clustering with Variant Sweep Algorithm and Route Optimization using Swarm Intelligence

Abstract

Capacitated Vehicle Routing Problem (CVRP) is a real life constraint satisfaction problem in which customers are optimally assigned to individual vehicles (considering their capacity) to keep total travel distance of the vehicles as minimum as possible while serving customers. Various methods are used 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 applied for this purpose. This study investigates a variant of Sweep algorithm for clustering nodes and different Swarm Intelligence (SI) based methods for route generation to get optimal CVRP solution. In conventional Sweep algorithm, cluster formation starts from 00 and consequently advance toward 3600 to consider all the nodes. In this study, a variant Sweep cluster is investigated from different starting angle. A heuristic based adaptive method is developed to select cluster formation starting angle. On the other hand, two well-known optimization methods (i.e., Genetic Algorithm and Ant Colony Optimization) and two recent SI based algorithms (i.e., Producer-Scrounger Method and Velocity Tentative Particle Swarm Optimization) are considered for route optimization. The experimental results on a large number of benchmark CVRPs revealed that different starting angles have positive effect on Sweep clustering and finally, VTPSO is able to produce better solution than other SI methods. Finally, the proposed mythology is found to achieve better CVRP solutions for several problems when compared with several prominent