An α-fuzzy goal approximate algorithm for solving fuzzy multiple objective linear programming problems

Lu, J; Ruan, D; Wu, F; Zhang, G

An α-fuzzy goal approximate algorithm for solving fuzzy multiple objective linear programming problems

Lu, J

Ruan, D Wu, F Zhang, G

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Publication Type:: Journal Article
Citation:: Soft Computing, 2007, 11 (3), pp. 259 - 267
Issue Date:: 2007-02-01

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Field	Value	Language
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732	en_US
dc.contributor.author	Ruan, D	en_US
dc.contributor.author	Wu, F	en_US
dc.contributor.author	Zhang, G https://orcid.org/0000-0003-3960-0583	en_US
dc.date.issued	2007-02-01	en_US
dc.identifier.citation	Soft Computing, 2007, 11 (3), pp. 259 - 267	en_US
dc.identifier.issn	1432-7643	en_US
dc.identifier.uri	http://hdl.handle.net/10453/4019
dc.description.abstract	Multiple conflicting objectives in many decision making problems can be well described by multiple objective linear programming (MOLP) models. This paper deals with the vague and imprecise information in a multiple objective problem by fuzzy numbers to represent parameters of an MOLP model. This so-called fuzzy MOLP (or FMOLP) model will reflect some uncertainty in the problem solution process since most decision makers often have imprecise goals for their decision objectives. This study proposes an approximate algorithm based on a fuzzy goal optimization under the satisfactory degree α to handle both fuzzy and imprecise issues. The concept of a general fuzzy number is used in the proposed algorithm for an FMOLP problem with fuzzy parameters. As a result, this algorithm will allow decision makers to provide fuzzy goals in any form of membership functions. © Springer-Verlag 2006.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP0211701
dc.relation.ispartof	Soft Computing	en_US
dc.relation.isbasedon	10.1007/s00500-006-0067-5	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	An α-fuzzy goal approximate algorithm for solving fuzzy multiple objective linear programming problems	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	11	en_US
utslib.for	080105 Expert Systems	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	1702 Cognitive Sciences	en_US
dc.location.activity	ISI:000241456100007	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	11	en_US

Abstract:

Multiple conflicting objectives in many decision making problems can be well described by multiple objective linear programming (MOLP) models. This paper deals with the vague and imprecise information in a multiple objective problem by fuzzy numbers to represent parameters of an MOLP model. This so-called fuzzy MOLP (or FMOLP) model will reflect some uncertainty in the problem solution process since most decision makers often have imprecise goals for their decision objectives. This study proposes an approximate algorithm based on a fuzzy goal optimization under the satisfactory degree α to handle both fuzzy and imprecise issues. The concept of a general fuzzy number is used in the proposed algorithm for an FMOLP problem with fuzzy parameters. As a result, this algorithm will allow decision makers to provide fuzzy goals in any form of membership functions. © Springer-Verlag 2006.

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http://hdl.handle.net/10453/4019