Title: A novel artificial bee colony algorithm for solving the supply chain network design under disruption scenarios

Authors: Ting-gui Chen; Chun-hua Ju

Addresses: College of Computer Science & Information Engineering, Zhejiang Gongshang University, Hangzhou, China ' College of Computer Science & Information Engineering, Zhejiang Gongshang University, Hangzhou, China; Contemporary Business and Trade Research Centre, Zhejiang Gongshang University, Hangzhou, China

Abstract: Today, many supply chain design decisions, such as facility location, are strategic in nature and very expensive to change. However, facilities in real world are always vulnerable to partial or complete disruptions. Owing to this reason, we firstly formulate the supply chain network design problem under disruption scenarios as a mixed-integer nonlinear program which maximises the total profit for the whole system. After that, in order to obtain near-optimal solutions with reasonable computational requirements for large problem instances, we have proposed a novel artificial bee colony (ABC) algorithm for solving this problem. Unlike other continuous optimisation problems, the supply chain network design problem under disruption scenarios is a classical discrete NP-hard one. Therefore, the proposed ABC algorithm applies discrete operators to generate new neighbouring food sources for the employed bees, onlookers and scouts. Subsequently, the computational simulations reveal very promising results in terms of the quality of solution.

Keywords: artificial bee colony; supply chain networks; supply chain design; supply chain disruption; discrete NP-hard problems; supply chain management; SCM; facility location; mixed-integer nonlinear programming; bee colony algorithm; simulation; swarm optimisation.

DOI: 10.1504/IJCAT.2013.054361

International Journal of Computer Applications in Technology, 2013 Vol.47 No.2/3, pp.289 - 296

Published online: 05 Jun 2013 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article