A redundancy detection algorithm for fuzzy stochastic multi-objective linear fractional programming problems

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 s...

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Vydáno v:Stochastic analysis and applications Ročník 35; číslo 1; s. 40 - 62
Hlavní autoři: Khanjani Shiraz, Rashed, Charles, Vincent, Tavana, Madjid, Di Caprio, Debora
Médium: Journal Article
Jazyk:angličtina
Vydáno: Philadelphia Taylor & Francis 02.01.2017
Taylor & Francis Ltd
Témata:
Algorithms
Complexity
Confidence intervals
fractional programming
Fuzzy
Mathematical analysis
Mathematical programming
multi-objective
Multiple objective analysis
Nonlinear programming
Pareto optimum
Probabilistic methods
Probability theory
Programming
Randomness
Redundancy
Redundancy detection
Simplification
stochastic
Stochasticity
ISSN:0736-2994, 1532-9356
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Shrnutí: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.
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ISSN:0736-2994
1532-9356
DOI:10.1080/07362994.2016.1248780