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Multiobjective Fuzzy Random Programming

In: Fuzzy Stochastic Multiobjective Programming

Author

Listed:
  • Masatoshi Sakawa

    (Hiroshima University)

  • Ichiro Nishizaki

    (Hiroshima University)

  • Hideki Katagiri

    (Hiroshima University)

Abstract

In this chapter, by considering not only the randomness of parameters involved in objective functions and/or constraints but also the experts’ ambiguous understanding of realized values of the random parameters, multiobjective programming problems with fuzzy random variables are formulated. Four types of optimization models for fuzzy random programming are developed by incorporating a concept of possibility measure into stochastic programming models discussed in the previous chapter. After introducing an extension concept of Pareto optimal solutions on the basis of possibility theory and probability theory, we show the development of interactive methods for fuzzy random multiobjective programming to derive a satisficing solution for a decision maker (DM).

Suggested Citation

  • Masatoshi Sakawa & Ichiro Nishizaki & Hideki Katagiri, 2011. "Multiobjective Fuzzy Random Programming," International Series in Operations Research & Management Science, in: Fuzzy Stochastic Multiobjective Programming, chapter 0, pages 101-168, Springer.
    • Handle: RePEc:spr:isochp:978-1-4419-8402-9_4
    DOI: 10.1007/978-1-4419-8402-9_4
    as

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