Solutions of multiple objective linear programming problems by applying T-sets in imprecise environment

Arindam Garai, Palash Mandal and Tapan Kumar Roy

International Journal of Operational Research, 2020, vol. 37, issue 2, 198-219

Abstract: In this paper, technique to find Pareto optimal solutions to multiple objective linear programming problems under imprecise environment is discussed. In imprecise environment, we observe that more preferable optimal values may be obtained by allowing membership functions to take arbitrary values, i.e., by removing constraints that impose membership values to fall in range between zero and one, than existing fuzzy optimisation techniques. Further, membership functions are not utilised as per definitions in existing fuzzy optimisation techniques. Also, such constraints may make the model infeasible. Consequently, one set viz. T-set is defined to supersede fuzzy set to represent impreciseness. Next, one general algorithm comprising T-sets, is given to find Pareto optimal solutions to multiple objective linear programming problems in imprecise environment. Numerical examples further illustrate proposed algorithm. Finally conclusions are drawn.

Keywords: multiple objective decision making; fuzzy set; fuzzy mathematical programming; linear programming problem; T-Pareto optimal solution; T-characteristic function; T-set; fuzzy decision making; optimisation. (search for similar items in EconPapers)
Date: 2020
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=105367 (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:ijores:v:37:y:2020:i:2:p:198-219

Access Statistics for this article

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