Maximizing and minimizing sets in solving fuzzy linear programming

Seyed Hossein Razavi Hajiagha, Hannan Amoozad Mahdiraji, Edmundas Kazemieras Zavadskas, Shide Sadat Hashemi

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


Linear programming with fuzzy information is a continuous field of researches in uncertain programming. Since the lack of a certain and deterministic solution is a natural characteristic of problems under uncertainty, different methods proposed various schemes to solve such problems. In this paper, a new framework is developed to solve fuzzy linear programming where the problem’s parameters, include objective function coefficients, technological matrix elements and right hand side values, are stated as fuzzy numbers. The proposed method is based on the notion of maximizing and minimizing sets, as a well known and widely accepted method of fuzzy numbers ranking, and tries to find a solution which optimizes the utility function of fuzzy objective functions by considering fuzzy constraints which are analyzed based on the concept of α-cuts and interval numbers relations. To show the applicability of the proposed method, its application is illustrated in a numerical example and its results are compared with a current method.

Original languageEnglish
Pages (from-to)1-20
Number of pages20
JournalEconomic Computation and Economic Cybernetics Studies and Research
Issue number2
Publication statusPublished - 1 Jan 2014
Externally publishedYes


  • Fuzzy linear programming
  • Fuzzy numbers
  • Maximization set
  • Minimization set

ASJC Scopus subject areas

  • Economics and Econometrics
  • Computer Science Applications
  • Applied Mathematics


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