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Stochastic Linear Programming

In: Linear and Multiobjective Programming with Fuzzy Stochastic Extensions

Author

Listed:
  • Masatoshi Sakawa

    (Hiroshima University)

  • Hitoshi Yano

    (Nagoya City University)

  • Ichiro Nishizaki

    (Hiroshima University)

Abstract

In this chapter, after overviewing elementary probability, two-stage programming and chance constrained programming are explained in detail. In two-stage programming, a shortage or an excess arising from the violation of the constraints is penalized, and then the expectation of the amount of the penalties for the constraint violation is minimized. In contrast, chance constrained programming admits random data variations and permits constraint violations up to specified probability limits, and its formulation is somewhat variable, including the expectation model, the variance model, the probability model, and the fractile model.

Suggested Citation

  • Masatoshi Sakawa & Hitoshi Yano & Ichiro Nishizaki, 2013. "Stochastic Linear Programming," International Series in Operations Research & Management Science, in: Linear and Multiobjective Programming with Fuzzy Stochastic Extensions, edition 127, chapter 0, pages 149-196, Springer.
    • Handle: RePEc:spr:isochp:978-1-4614-9399-0_5
    DOI: 10.1007/978-1-4614-9399-0_5
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    Citations

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    Cited by:

    1. Avik Pradhan & M. P. Biswal, 2017. "Multi-choice probabilistic linear programming problem," OPSEARCH, Springer;Operational Research Society of India, vol. 54(1), pages 122-142, March.
    2. María Luisa Nolé & David Soler & Juan Luis Higuera-Trujillo & Carmen Llinares, 2022. "Optimization of the Cognitive Processes in a Virtual Classroom: A Multi-objective Integer Linear Programming Approach," Mathematics, MDPI, vol. 10(7), pages 1-20, April.
    3. Soumendra Nath Sanyal & Izabela Nielsen & Subrata Saha, 2020. "Multi-Objective Human Resource Allocation Approach for Sustainable Traffic Management," IJERPH, MDPI, vol. 17(7), pages 1-16, April.

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