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Interactive Multiobjective Optimization Under Uncertainty

Author

Listed:
  • G. Klein

    (Department of Business Analysis and Communication, College of Administration and Business, Louisiana Tech University, PO Box 10318, Ruston, Louisiana 71272-0046)

  • H. Moskowitz

    (Krannert Graduate School of Management, Purdue University, West Lafayette, Indiana 47907)

  • A. Ravindran

    (University of Oklahoma, 660 Parrington Oval, Room 104, Norman, Oklahoma 73019)

Abstract

Uncertainty presents unique difficulties in multiobjective optimization problems, because decision makers are faced with risky situations requiring analysis of multiple outcomes in differing states of nature. Very few direct choice (interactive) multiobjective methods are capable of addressing problems with probabilistic outcomes. We thus propose a general multiobjective algorithm which accommodates uncertainty. The method is appropriate for use in a multiple criteria framework with a discrete number of states of nature. Without loss of generality, and in the interest of simplicity of exposition, our method is explored and developed in the context of a bicriterion optimization problem using a two stage mathematical programming model. Simulation and behavioral experiments are conducted which verify that the method is viable for problems with greater dimensionality.

Suggested Citation

  • G. Klein & H. Moskowitz & A. Ravindran, 1990. "Interactive Multiobjective Optimization Under Uncertainty," Management Science, INFORMS, vol. 36(1), pages 58-75, January.
    • Handle: RePEc:inm:ormnsc:v:36:y:1990:i:1:p:58-75
    DOI: 10.1287/mnsc.36.1.58
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    Citations

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    Cited by:

    1. Ringuest, Jeffrey L. & Graves, Samuel B., 2000. "A sampling-based method for generating nondominated solutions in stochastic MOMP problems," European Journal of Operational Research, Elsevier, vol. 126(3), pages 651-661, November.
    2. Urli, Bruno & Nadeau, Raymond, 2004. "PROMISE/scenarios: An interactive method for multiobjective stochastic linear programming under partial uncertainty," European Journal of Operational Research, Elsevier, vol. 155(2), pages 361-372, June.
    3. Fatima Bellahcene, 2019. "Decision maker's preferences modeling for multiple objective stochastic linear programming problems," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 29(3), pages 5-16.
    4. Charitopoulos, Vassilis M. & Dua, Vivek, 2017. "A unified framework for model-based multi-objective linear process and energy optimisation under uncertainty," Applied Energy, Elsevier, vol. 186(P3), pages 539-548.

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