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Using genetic algorithm for solving quadratic bilevel programming problems via fuzzy goal programming

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  • Bijay Baran Pal
  • Debjani Chakraborti

Abstract

This article presents how genetic algorithm (GA) can be efficiently used to fuzzy goal programming (FGP) formulation of quadratic bilevel programming problems (QBLPPs) in a hierarchical decision system. In the proposed approach, the concept of tolerance membership functions in fuzzy sets for measuring the achievement of highest membership value (unity) of the defined fuzzy goals of a problem to the extent possible by minimising the under-deviational variables of the defined membership goals on the basis of priorities of achieving the fuzzy goals is considered. In the decision making process, the sensitivity analysis with variations of priority structure of the goals is performed and then the notion of Euclidean distance function is used to identify the appropriate priority structure under which the most satisfactory decision can be reached in the fuzzy decision environment. The potential use of the approach is illustrated by a numerical example.

Suggested Citation

  • Bijay Baran Pal & Debjani Chakraborti, 2013. "Using genetic algorithm for solving quadratic bilevel programming problems via fuzzy goal programming," International Journal of Applied Management Science, Inderscience Enterprises Ltd, vol. 5(2), pages 172-195.
    • Handle: RePEc:ids:injams:v:5:y:2013:i:2:p:172-195
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    Cited by:

    1. Debjani Chakraborti, 2016. "Evolutionary technique based goal programming approach to chance constrained interval valued bilevel programming problems," OPSEARCH, Springer;Operational Research Society of India, vol. 53(2), pages 390-408, June.

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