Novel Encoding and Routing Balance Insertion Based Particle Swarm Optimization with Application to Optimal CVRP Depot Location Determination

Ruey-Maw Chen,Yin-Mou Shen

doi:10.1155/2015/743507

Abstract

A depot location has a significant effect on the transportation cost in vehicle routing problems. This study proposes a hierarchical particle swarm optimization (PSO) including inner and outer layers to obtain the best location to establish a depot and the corresponding optimal vehicle routes using the determined depot location. The inner layer PSO is applied to obtain optimal vehicle routes while the outer layer PSO is to acquire the depot location. A novel particle encoding is suggested for the inner layer PSO, the novel PSO encoding facilitates solving the customer assignment and the visiting order determination simultaneously to greatly lower processing efforts and hence reduce the computation complexity. Meanwhile, a routing balance insertion (RBI) local search is designed to improve the solution quality. The RBI local search moves the nearest customer from the longest route to the shortest route to reduce the travel distance. Vehicle routing problems from an operation research library were tested and an average of 16% total routing distance improvement between having and not having planned the optimal depot locations is obtained. A real world case for finding the new plant location was also conducted and significantly reduced the cost by about 29%.

Highlights

The vehicle routing problem (VRP) is a scheduling problem encountered in logistic arrangement, an extension of the traveling salesman problem
Many researchers have come up with a variety of heuristic and metaheuristic methods in recent years to cope with vehicle routing problems, including the evolution computation, memetic algorithm, genetic algorithm (GA), local search metaheuristic, artificial bee colony algorithm, ant colony optimization (ACO), and particle swarm optimization (PSO)
The new local search tactic named routing balance insertion (RBI) local search is applied in the inner layer PSO, which is inspired from the well-used nearest neighborhood heuristic in TSP

Summary

Introduction

The vehicle routing problem (VRP) is a scheduling problem encountered in logistic arrangement, an extension of the traveling salesman problem. With problems of a larger scale, the amount and time of calculation required make it impossible to obtain optimal solutions with exact algorithms within a limited time For this reason, many researchers have come up with a variety of heuristic and metaheuristic methods in recent years to cope with vehicle routing problems, including the evolution computation, memetic algorithm, genetic algorithm (GA), local search metaheuristic, artificial bee colony algorithm, ant colony optimization (ACO), and particle swarm optimization (PSO). Abido [18] employed PSO to solve the optimal setting of power flow Kang and He [19] proposed a novel discrete particle swarm optimization algorithm for meta-task assignment in heterogeneous computing systems and used a migration mechanism to escape from possible local optimum. Besides fine vehicle route planning, good choice of depot locations is an important issue to reduce business costs and increase profits.

Problem Description

Particle Swarm Optimization with Proposed Designs

H Route-2

Experimental Results

Inner-Layer PSO

Conclusions