Pareto-optimal solutions in fuzzy multi-objective linear programming

The problem of solving multi-objective linear-programming problems, by assuming that the decision maker has fuzzy goals for each of the objective functions, is addressed. Several methods have been proposed in the literature in order to obtain fuzzy-efficient solutions to fuzzy multi-objective progra...

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Veröffentlicht in:Fuzzy sets and systems Jg. 160; H. 18; S. 2714 - 2721
Hauptverfasser: Jiménez, Mariano, Bilbao, Amelia
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Kidlington Elsevier B.V 16.09.2009
Elsevier
Schlagworte:
Applied sciences
Circuit properties
Computer science; control theory; systems
Control theory. Systems
Digital circuits
Electric, optical and optoelectronic circuits
Electronic circuits
Electronics
Exact sciences and technology
Fuzzy mathematical programming
Fuzzy-efficient solution
Goal programming
Information, signal and communications theory
Mathematical methods
Miscellaneous
Multi-objective programming
Pareto-optimal solution
System theory
Telecommunications and information theory
Theoretical computing
Two-phase method
Fuzzy-efficient solution
Two-phase method
Multi-objective programming
Fuzzy mathematical programming
Pareto-optimal solution
Goal programming
Fuzzy system
Linear programming
Fuzzy set
Engineering
Optimal solution
Information processing
Problem solving
Fuzzy-eflicient solution
Mathematical programming
ISSN:0165-0114, 1872-6801
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:The problem of solving multi-objective linear-programming problems, by assuming that the decision maker has fuzzy goals for each of the objective functions, is addressed. Several methods have been proposed in the literature in order to obtain fuzzy-efficient solutions to fuzzy multi-objective programming problems. In this paper we show that, in the case that one of our goals is fully achieved, a fuzzy-efficient solution may not be Pareto-optimal and therefore we propose a general procedure to obtain a non-dominated solution, which is also fuzzy-efficient. Two numerical examples illustrate our procedure.
ISSN:0165-0114
1872-6801
DOI:10.1016/j.fss.2008.12.005