Guided chemotaxis-based bacterial colony algorithm for three-echelon supply chain optimisation

Ben Niu, Tung Sun Chan, Ting Xie, Yanmin Liu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

5 Citations (Scopus)

Abstract

On the base of the existing research study, a multi-period, multi-product, multi-supplier, single-manufacture, and multi-distributor supply chain model is considered in the paper. In the three-echelon model, a variety of decision-making activities involved in the procurement, production and distribution process are integrated at the operational level, giving rise to the non-deterministic polynomial-time hard computational complexity for model optimisation. For tackling the difficult model, this paper proposes a new optimisation method called guided chemotaxis-based bacterial colony algorithm, characterised by centre learning communication mechanism. More specifically, centre learning communication mechanism, where all the bacteria are enforced to learn towards the centre position of the swarm, is designed for the global exploration ability of algorithm. Chemotaxis, which guides the bacterium to fine-tune the solution in an increasingly favourable fitness landscape, is used to enhance the local exploitation ability of algorithm. Numerical experiments on a variety of simulated scenarios show the effectiveness and efficiency of the proposed algorithm in terms of both quality solution and computational time, by comparing it with some existing state-of-the-art solution approaches.
Original languageEnglish
Pages (from-to)305-319
Number of pages15
JournalInternational Journal of Computer Integrated Manufacturing
Volume30
Issue number2-3
DOIs
Publication statusPublished - 4 Mar 2017

Keywords

  • bacterial colony algorithm
  • three-echelon supply chain optimisation

ASJC Scopus subject areas

  • Aerospace Engineering
  • Mechanical Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

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