A redundancy detection algorithm for fuzzy stochastic multi-objective linear fractional programming problems. Issue 1 (2nd January 2017)

• Record Type: Journal Article
• Authors: Khanjani Shiraz, Rashed; Charles, Vincent; Tavana, Madjid; Di Caprio, Debora
• Is Part Of: Stochastic analysis and applications. Volume 35:Issue 1(2017)
• Page Start: 40
• Page End: 62
• Publication Date: 2017-01-02
• Subjects: Redundancy detection -- fuzzy -- stochastic -- multi-objective -- fractional programming; 90C15 -- 90C29 -- 90C32 -- 90C70; Stochastic analysis -- Periodicals; 519.2205
• DOI: 10.1080/07362994.2016.1248780
• Languages: English
• ISSNs: 0736-2994

Abstract

ABSTRACT: The computational complexity of linear and nonlinear programming problems depends on the number of objective functions and constraints involved and solving a large problem often becomes a difficult task. Redundancy detection and elimination provides a suitable tool for reducing this complexity and simplifying a linear or nonlinear programming problem while maintaining the essential properties of the original system. Although a large number of redundancy detection methods have been proposed to simplify linear and nonlinear stochastic programming problems, very little research has been developed for fuzzy stochastic (FS) fractional programming problems. We propose an algorithm that allows to simultaneously detect both redundant objective function(s) and redundant constraint(s) in FS multi-objective linear fractional programming problems. More precisely, our algorithm reduces the number of linear fuzzy fractional objective functions by transforming them in probabilistic–possibilistic constraints characterized by predetermined confidence levels. We present two numerical examples to demonstrate the applicability of the proposed algorithm and exhibit its efficacy.