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Stochastic Programming with Aspiration or Fractile Criteria

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  • Arthur M. Geoffrion

    (Western Management Science Institute University of California, Los Angeles)

Abstract

The general linear programming problem is considered in which the coefficients of the objective function to be maximized are assumed to be random variables with a known multinormal distribution. Three deterministic reformulations involve, respectively, maximizing the expected value, the \alpha -fractile (\alpha fixed, 0

Suggested Citation

  • Arthur M. Geoffrion, 1967. "Stochastic Programming with Aspiration or Fractile Criteria," Management Science, INFORMS, vol. 13(9), pages 672-679, May.
    • Handle: RePEc:inm:ormnsc:v:13:y:1967:i:9:p:672-679
    DOI: 10.1287/mnsc.13.9.672
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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. Wang, S. & Huang, G.H., 2016. "Risk-based factorial probabilistic inference for optimization of flood control systems with correlated uncertainties," European Journal of Operational Research, Elsevier, vol. 249(1), pages 258-269.
    3. Itami, Hiroyuki, 1974. "Parametric Evaluation and Mean-Standard Deviation Analysis in Stochastic Programming Models," Hitotsubashi Journal of commerce and management, Hitotsubashi University, vol. 9(1), pages 62-82, July.
    4. S. N. Gupta & A. K. Jain & Kanti Swarup, 1987. "Stochastic linear fractional programming with the ratio of independent Cauchy variates," Naval Research Logistics (NRL), John Wiley & Sons, vol. 34(2), pages 293-305, April.
    5. Katagiri, Hideki & Sakawa, Masatoshi & Kato, Kosuke & Nishizaki, Ichiro, 2008. "Interactive multiobjective fuzzy random linear programming: Maximization of possibility and probability," European Journal of Operational Research, Elsevier, vol. 188(2), pages 530-539, July.
    6. Ioana Popescu, 2007. "Robust Mean-Covariance Solutions for Stochastic Optimization," Operations Research, INFORMS, vol. 55(1), pages 98-112, February.
    7. Dayi He & Ran Li & Qi Huang & Ping Lei, 2014. "Transportation Optimization with Fuzzy Trapezoidal Numbers Based on Possibility Theory," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-12, August.

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