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

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 distribu...
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Autor*in:	Niu, Ben [verfasserIn] Chan, Felix T. S Xie, Ting Liu, Yanmin

Format:	Artikel
Sprache:	Englisch

Erschienen:	2017

Rechteinformationen:	Nutzungsrecht: © 2016 Taylor & Francis 2016

Schlagwörter:	bacterial colony algorithm three-echelon supply chain optimisation

Übergeordnetes Werk:	Enthalten in: International journal of computer integrated manufacturing - London [u.a.] : Taylor & Francis, 1988, 30(2017), 2-3, Seite 305-319
Übergeordnetes Werk:	volume:30 ; year:2017 ; number:2-3 ; pages:305-319

Links:	Volltext Link aufrufen

DOI / URN:	10.1080/0951192X.2016.1145809

Katalog-ID:	OLC1993098054

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title_sort	guided chemotaxis-based bacterial colony algorithm for three-echelon supply chain optimisation
title_auth	Guided chemotaxis-based bacterial colony algorithm for three-echelon supply chain optimisation
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.
abstractGer	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.
abstract_unstemmed	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.
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title_short	Guided chemotaxis-based bacterial colony algorithm for three-echelon supply chain optimisation
url	http://dx.doi.org/10.1080/0951192X.2016.1145809 http://www.tandfonline.com/doi/abs/10.1080/0951192X.2016.1145809
remote_bool	false
author2	Chan, Felix T. S Xie, Ting Liu, Yanmin
author2Str	Chan, Felix T. S Xie, Ting Liu, Yanmin
ppnlink	129267643
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hochschulschrift_bool	false
author2_role	oth oth oth
doi_str	10.1080/0951192X.2016.1145809
up_date	2024-07-03T13:27:32.121Z
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