International Journal of Research and Engineering ISSN: 2348-7860 (O) | 2348-7852 (P) | Vol. 5 No. 9 | September-October 2018 | PP. 500-507 Digital Object Identifier DOI® http://dx.doi.org/10.21276/ijre.2018.5.9.2 Copyright © 2018 by authors and International Journal of Research and Engineering This work is licensed under the Creative Commons Attribution International License (CC BY). creativecommons.org/licenses/by/4.0 | | ORIGINAL ARTICLE Solving Vehicle Routing Problem using Ant Colony Optimisation (ACO) Algorithm Author(s): 1 *Wan Amir Fuad Wajdi Othman, 1 Aeizaal Azman Abd Wahab 1 Syed Sahal Nazli Alhady, 1 Haw Ngie Wong Affiliation(s): 1 School of Electrical & Electronic Engineering, Universiti Sains Malaysia, Malaysia *Corresponding Author: wafw_othman@usm.my Abstract - Engineering field usually requires having the best design for an optimum performance, thus optimization plays an important part in this field. The vehicle routing problem (VRP) has been an important problem in the field of distribution and logistics since at least the early 1960s. Hence, this study was about the application of ant colony optimization (ACO) algorithm to solve vehicle routing problem (VRP). Firstly, this study constructed the model of the problem to be solved through this research. The study was then focused on the Ant Colony Optimization (ACO). The objective function of the algorithm was studied and applied to VRP. The effectiveness of the algorithm was increased with the minimization of stopping criteria. The control parameters were studied to find the best value for each control parameter. After the control parameters were identified, the evaluation of the performance of ACO on VRP was made. The good performance of the algorithm reflected on the importance of its parameters, which were number of ants (nAnt), alpha (α), beta (β) and rho (ρ). Alpha represents the relative importance of trail, beta represents the importance of visibility and rho represents the parameter governing pheromone decay. The route results of different iterations were compared and analyzed the performance of the algorithm. The best set of control parameters obtained is with 20 ants, α = 1, β = 1 and ρ = 0.05. The average cost and standard deviation from the 20 runtimes with best set of control parameters were also evaluated, with 1057.839 km and 25.913 km respectively. Last but not least, a conclusion is made to summarize the achievement of the study. Keywords: Vehicle Routing Problem, Ant Colony Optimization, ACO, VRP, Swarm Algorithm I. INTRODUCTION The study is about solving vehicle routing problem (VRP) using ant colony optimization (ACO) algorithm. This is a software-based project. VRP generalizes the well-known travelling salesman problem (TSP). The study can be divided into two parts, vehicle routing problem (VRP) and ant colony optimization (ACO) algorithm. The vehicle routing problem (VRP) has been an important problem in the field of distribution and logistics since at least the early 1960s [1]. VRP research accelerated during the 1990s [2]. Researchers could develop and implement more complex search algorithms due to the improvement of microcomputer capability and availability. During this era the term meta-heuristics was introduced to name a number of search algorithms for solving these VRPs as well as other combinatorial optimization problems [3]. The technical definition of vehicle routing problem (VRP) states that m vehicles initially located at a depot are to deliver discrete quantities of goods to n customers. The aim of a VRP is to determine the optimal route used by a group of vehicles when serving a group of users. The objective of VRP is to minimize the overall transportation cost. The solution of the classical VRP is a set of routes which all begin and end in the depot, and which satisfies the constraint that all the customers are served only once. The transportation cost can be improved by reducing the total travelled distance and by reducing the number of the required vehicles. Two important classes of population-based optimization algorithms are evolutionary algorithms and swarm intelligence-based algorithms [3]. In this research, swarm intelligence-based algorithm is chosen to be applied on VRP.