Fuzzy rule sets for enhancing performance in a supply chain network

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

26 Scopus Citations
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Author(s)

  • G. T S Ho
  • H. C W Lau
  • S. H. Chung
  • T. M. Chan
  • C. K M Lee

Detail(s)

Original languageEnglish
Pages (from-to)947-972
Journal / PublicationIndustrial Management and Data Systems
Volume108
Issue number7
Publication statusPublished - 2008

Abstract

Purpose - This paper aims to develop a genetic algorithm (GA)-based process knowledge integration system (GA-PKIS) for generalizing a set of nearly optimal fuzzy rules in quality enhancement based on the extracted fuzzy association rules in a supply chain network. Design/methodology/approach - The proposed methodology provides all levels of employees with the ability to formulate nearly optimal sets of fuzzy rules to identify possible solutions for eliminating the number of defect items. Findings - The application of the proposed methodology in the slider manufacturer has been studied. After performing the spatial analysis, the results obtained indicate that it is capable of ensuring the finished products with promising quality. Research limitations/implications - In order to demonstrate the feasibility of the proposed approach, only some processes within the supply chain are chosen. Future studies can advance this research by applying the proposed approach in different industries and processes. Originality/value - Because of the complexity of the logistics operations along the supply chain, the traditional quality improvement approaches cannot address all the quality problems automatically and effectively. This newly developed GA-based approach can help to optimize the process parameters along the supply chain network. © Emerald Group Publishing Limited.

Research Area(s)

  • Advanced manufacturing technologies, Fuzzy logic, Quality, Quality improvement, Supply chain management

Citation Format(s)

Fuzzy rule sets for enhancing performance in a supply chain network. / Ho, G. T S; Lau, H. C W; Chung, S. H.; Fung, R. Y K; Chan, T. M.; Lee, C. K M.

In: Industrial Management and Data Systems, Vol. 108, No. 7, 2008, p. 947-972.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review