Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/17441
Title: Fuzzy rule sets for enhancing performance in a supply chain network
Authors: Ho, GTS
Lau, HCW
Chung, SH
Fung, RYK
Chan, TM
Lee, CKM
Keywords: Advanced manufacturing technologies
Fuzzy logic
Quality
Quality improvement
Supply chain management
Issue Date: 2008
Publisher: Emerald Group Publishing Limited
Source: Industrial management and data systems, 2008, v. 108, no. 7, p. 947-972 How to cite?
Journal: Industrial management and data systems 
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.
URI: http://hdl.handle.net/10397/17441
ISSN: 0263-5577
EISSN: 1758-5783
DOI: 10.1108/02635570810898017
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

19
Last Week
0
Last month
0
Citations as of Sep 15, 2017

WEB OF SCIENCETM
Citations

12
Last Week
0
Last month
1
Citations as of Sep 21, 2017

Page view(s)

39
Last Week
1
Last month
Checked on Sep 17, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.