Uncertain multi-objective programming model: a genetic algorithm approach

Kailash Lachhwani

International Journal of Mathematics in Operational Research, 2017, vol. 11, issue 2, 271-283

Abstract: This paper aims at describing an uncertain multi-objective programming model involving uncertain variables with genetic algorithm approach. In this paper, the uncertain multiobjective programming model is converted into an equivalent crisp mathematical programming model. Then, a genetic algorithm is proposed to search the Stackelberg-Nash equilibrium of the uncertain multiobjective programming model with supporting numerical illustrations. Finally, sensitivity analysis study is carried out over parameters of algorithm and solution obtained to show efficiency and robustness of genetic algorithm for uncertain multiobjective programming model.

Keywords: uncertain multi-objective programming; genetic algorithm; Stackelberg-Nash equilibrium; expected value; crisp model; uncertain measure. (search for similar items in EconPapers)
Date: 2017
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=86304 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijmore:v:11:y:2017:i:2:p:271-283

Access Statistics for this article

More articles in International Journal of Mathematics in Operational Research from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().