Constructing a fuzzy flow-shop sequencing model based on statistical data

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Title:	Constructing a fuzzy flow-shop sequencing model based on statistical data
Authors:	Yao JS Lin FT
Contributors:	應數系
Keywords:	point estimate confidence interval interval-valued fuzzy number fuzzy flow-shop model flow-shop sequencing problem signed distance ranking method
Date:	2002
Issue Date:	2009-12-11 09:55:56 (UTC+8)
Abstract:	This study investigated an approach for incorporating statistics with fuzzy sets in the flow-shop sequencing problem. This work is based on the assumption that the precise value for the processing time of each job is unknown, but that some sample data are available. A combination of statistics and fuzzy sets provides a powerful tool for modeling and solving this problem. Our work intends to extend the crisp flow-shop sequencing problem into a generalized fuzzy model that would be useful in practical situations. In this study, we constructed a fuzzy flow-shop sequencing model based on statistical data, which uses level (1 - alpha, 1 - beta) interval-valued fuzzy numbers to represent the unknown job processing time, Our study shows that this fuzzy flow-shop model is an extension of the crisp flow-shop problem and the results obtained from the fuzzy flow-shop model provides the same job sequence as that of the crisp problem. (C) 2001 Elsevier Science Inc. All rights reserved.
Relation:	INTERNATIONAL JOURNAL OF APPROXIMATE REASONING Volume: 29 Issue: 3 Pages: 215-234
Appears in Collections:	[Department of Applied Mathematics] journal articles

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