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Fuzzy Multiobjective Stochastic 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 the imprecision of a decision maker’s (DM’s) judgments for stochastic objective functions and/or constraints in multiobjective problems, fuzzy multiobjective stochastic programming is developed. Assuming that the DM has a fuzzy goal for each of expectations and variances of the original stochastic objective functions, multiobjective stochastic programming problems are formulated. For reflecting the diversity of criteria for optimizing the stochastic objective functions, optimization criteria different from expectation and variance are also provided to maximize the probability of the objective functions being greater than or equal to target values as well as to optimize the target values under a given probability.

Suggested Citation

  • Masatoshi Sakawa & Ichiro Nishizaki & Hideki Katagiri, 2011. "Fuzzy Multiobjective Stochastic Programming," International Series in Operations Research & Management Science, in: Fuzzy Stochastic Multiobjective Programming, chapter 0, pages 49-99, Springer.
    • Handle: RePEc:spr:isochp:978-1-4419-8402-9_3
    DOI: 10.1007/978-1-4419-8402-9_3
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    Citations

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    Cited by:

    1. M. Sakawa & T. Matsui & H. Katagiri, 2013. "An interactive fuzzy satisficing method for random fuzzy multiobjective integer programming problems through probability maximization with possibility," Fuzzy Information and Engineering, Springer, vol. 5(3), pages 335-350, September.
    2. Ngandu Balekelayi & Haile Woldesellasse & Solomon Tesfamariam, 2022. "Comparison of the Performance of a Surrogate Based Gaussian Process, NSGA2 and PSO Multi-objective Optimization of the Operation and Fuzzy Structural Reliability of Water Distribution System: Case Stu," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(15), pages 6169-6185, December.
    3. Moussa Larbani & Po Lung Yu, 2012. "Decision Making and Optimization in Changeable Spaces, a New Paradigm," Journal of Optimization Theory and Applications, Springer, vol. 155(3), pages 727-761, December.
    4. S. Dutta & B.C. Sahoo & Rajashree Mishra & S. Acharya, 2016. "Fuzzy Stochastic Genetic Algorithm for Obtaining Optimum Crops Pattern and Water Balance in a Farm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(12), pages 4097-4123, September.

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