Biological System Behaviours and Natural-inspired Methods and Their Applications to Supply Chain Management

Fan, Xuemei, Zhang, Shujun ORCID: 0000-0001-5699-2676, Hapeshi, Kevin and Ynag, Y (2014) Biological System Behaviours and Natural-inspired Methods and Their Applications to Supply Chain Management. Applied Mechanics and Materials, 461. pp. 942-958. doi:10.4028/www.scientific.net/AMM.461.942

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Abstract

People have learnt from biological system behaviours and structures to design and develop a number of different kinds of optimisation algorithms that have been widely used in both theoretical study and practical applications in engineering and business management. An efficient supply chain is very important for companies to survive in global competitive market. An effective SCM (supply chain management) is the key for implement an efficient supply chain. Though there have been considerable amount of study of SCM, there have been very limited publications of applying the findings from the biological system study into SCM. In this paper, through systematic literature review, various SCM issues and requirements are discussed and some typical biological system behaviours and natural-inspired algorithms are evaluated for the purpose of SCM. Then the principle and possibility are presented on how to learn the biological systems' behaviours and natural-inspired algorithms for SCM and a framework is proposed as a guide line for users to apply the knowledge learnt from the biological systems for SCM. In the framework, a number of the procedures have been presented for using XML to represent both SCM requirement and bio-inspiration data. To demonstrate the proposed framework, a case study has been presented for users to find the bio-inspirations for some particular SCM problems in automotive industry.

Item Type:	Article
Article Type:	Article
Uncontrolled Keywords:	learn from nature, biological system behaviours, supply chain management, ACO, Bees Algorithm, Genetic Algorithm, Firefly Algorithm
Subjects:	H Social Sciences > HF Commerce
Divisions:	Schools and Research Institutes > School of Business, Computing and Social Sciences
Research Priority Areas:	Applied Business & Technology
Depositing User:	Ineke Tijsma
Date Deposited:	17 Mar 2015 16:31
Last Modified:	31 Aug 2023 08:01
URI:	https://eprints.glos.ac.uk/id/eprint/1511

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