DocumentCode :
766147
Title :
Fuzzy operator allocation for balance control of assembly lines in apparel manufacturing
Author :
Hui, Patrick Chi-Leung ; Chan, Keith C C ; Yeung, K.W. ; Ng, Frency Sau-Fun
Author_Institution :
Inst. of Textiles & Clothing, Hong Kong Polytech. Univ., Kowloon, China
Volume :
49
Issue :
2
fYear :
2002
fDate :
5/1/2002 12:00:00 AM
Firstpage :
173
Lastpage :
180
Abstract :
Production processes in apparel manufacturing typically involve hybrid assembly lines. In order to perform at predetermined production rates, careful balance control of the sewing operations on these assembly lines is very important. The right number of operators to be moved in and out of a sewing section has to be accurately determined. In this paper, the authors extend the literature on fuzzy logic applications to control systems by proposing a simple, yet effective, rule-based system that captures the knowledge of experienced supervisors in a set of fuzzy rules. These rules specify how balance control can be achieved based on the level of difference between; (1) the actual and target buffer level; and (2) the actual and target section performance. Since these parameters are normally expressed in linguistic terms, they are encoded in rules expressed with fuzzy logic. The number of operators to be moved in and out of a sewing section can, therefore, be determined by means of fuzzy inferences. To evaluate the effectiveness of the system, we compared its performance to the decisions made by experienced supervisors at a large shirt factory. The system was found to increase in production efficiency by 30%
Keywords :
assembling; control system synthesis; fuzzy control; inference mechanisms; knowledge based systems; process control; textile industry; apparel manufacturing; fuzzy inferences; fuzzy logic control systems; fuzzy rules; hybrid assembly lines; knowledge capture; production efficiency; production processes; production rates; rule-based system; sewing operations; Assembly; Control systems; Fuzzy control; Fuzzy logic; Fuzzy sets; Fuzzy systems; Knowledge based systems; Manufacturing processes; Production facilities; Production systems;
fLanguage :
English
Journal_Title :
Engineering Management, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9391
Type :
jour
DOI :
10.1109/TEM.2002.1010885
Filename :
1010885
Link To Document :
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