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A multi-objective supply chain optimisation using enhanced Bees algorithm with adaptive neighbourhood search and site abandonment strategy

Yuce, Baris, Mastrocinque, Ernesto, Lambiase, Alfredo, Packianather, Michael Sylvester and Truong Pham, Duc 2014. A multi-objective supply chain optimisation using enhanced Bees algorithm with adaptive neighbourhood search and site abandonment strategy. Swarm and Evolutionary Computation 18 , pp. 71-82. 10.1016/j.swevo.2014.04.002

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Abstract

In this paper, an enhanced version of the Bees Algorithm is proposed in dealing with multi-objective supply chain model to find the optimum configuration of a given supply chain problem in order to minimise the total cost and the total lead-time. The new Bees Algorithm includes an adaptive neighbourhood size change and site abandonment (ANSSA) strategy which is an enhancement to the basic Bees Algorithm. The supply chain case study utilised in this work is taken from literature and several experiments have been conducted in order to show the performances, the strength, the weaknesses of the proposed method and the results have been compared to those achieved by the basic Bees Algorithm and Ant Colony optimisation. The results show that the proposed ANSSA-based Bees Algorithm is able to achieve better Pareto solutions for the supply chain problem.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Centre for Advanced Manufacturing Systems At Cardiff (CAMSAC)
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Uncontrolled Keywords: Supply Chain management; Multi-Objective Optimisation; Swarm-based optimization; Bees Algorithm; Adaptive neighbourhood search; Site abandonment
Publisher: Elsevier
ISSN: 2210-6502
Last Modified: 16 Jun 2019 00:43
URI: http://orca-mwe.cf.ac.uk/id/eprint/59310

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