An interactive fuzzy satisficing method based on variance minimization under expectation constraints for multiobjective stochastic linear programming problems

  • Authors:

  • Kosuke Kato;Masatoshi Sakawa

  • Affiliations:

  • Hiroshima Institute of Technology, Faculty of Applied Information Science, 731-5193, Hiroshima, Japan;Hiroshima University, Graduate School of Engineering, 739-8527, Higashi-Hiroshima, Japan

  • Venue:
  • Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue on Bio-inspired Learning and Intelligent Systems
  • Year:
  • 2011

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Abstract

In this paper, we focus on multiobjective linear programming problems involving random variable coefficients in objective functions and constraints. Using the concept of chance constrained conditions, such multiobjective stochastic linear programming problems are transformed into deterministic ones based on the variance minimization model under expectation constraints. After introducing fuzzy goals to reflect the ambiguity of the decision maker’s judgements for objective functions, we propose an interactive fuzzy satisficing method to derive a satisficing solution for them as a fusion of the stochastic programming and the fuzzy one. The application of the proposed method to an illustrative numerical example shows its usefulness.