A Multi-Objective Optimization for Supply Chain Network Using the Bees Algorithm

Ernesto Mastrocinque, B. Yuce, A. Lambiase, M. S. Packianather

Research output: Contribution to journalArticle

35 Citations (Scopus)
24 Downloads (Pure)

Abstract

A supply chain is a complex network which involves the products, services and information flows between suppliers and customers. A typical supply chain is composed of different levels, hence, there is a need to optimize the supply chain by finding the optimum configuration of the network in order to get a good compromise between the multi-objectives such as cost minimization and lead-time minimization. There are several multi-objective optimization methods which have been applied to find the optimum solutions set based on the Pareto front line. In this study, a swarm-based optimization method, namely, the bees algorithm is proposed in dealing with the multi-objective supply chain model to find the optimum configuration of a given supply chain problem which minimizes the total cost and the total lead-time. The supply chain problem utilized in this study is taken from literature and several experiments have been conducted in order to show the performance of the proposed model; in addition, the results have been compared to those achieved by the ant colony optimization method. The results show that the proposed bees algorithm is able to achieve better Pareto solutions for the supply chain problem.
Original languageEnglish
Article number38
JournalInternational Journal of Engineering Business Management
Volume5
DOIs
Publication statusPublished - 23 Aug 2013

Bibliographical note

This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Keywords

  • Supply Chain Management
  • Multi-Objective Optimization
  • Swarm-based Optimization
  • The Bees Algorithm
  • Artificial Intelligence

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