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ISTMO: An interval reference point-based method for stochastic multiobjective programming problems


  • Muñoz, Maria M.
  • Ruiz, Francisco


In this paper, we present an interactive algorithm (ISTMO) for stochastic multiobjective problems with continuous random variables. This method combines the concept of probability efficiency for stochastic problems with the reference point philosophy for deterministic multiobjective problems. The decision maker expresses her/his references by dividing the variation range of each objective into intervals, and by setting the desired probability for each objective to achieve values belonging to each interval. These intervals may also be redefined during the process. This interactive procedure helps the decision maker to understand the stochastic nature of the problem, to discover the risk level (s)he is willing to assume for each objective, and to learn about the trade-offs among the objectives.

Suggested Citation

  • Muñoz, Maria M. & Ruiz, Francisco, 2009. "ISTMO: An interval reference point-based method for stochastic multiobjective programming problems," European Journal of Operational Research, Elsevier, vol. 197(1), pages 25-35, August.
  • Handle: RePEc:eee:ejores:v:197:y:2009:i:1:p:25-35

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    References listed on IDEAS

    1. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    2. R. Caballero & E. Cerda & M. Muñoz & L. Rey, 2002. "Analysis and comparisons of some solution concepts for stochastic programming problems," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 10(1), pages 101-123, June.
    3. Ballestero, Enrique, 2001. "Stochastic goal programming: A mean-variance approach," European Journal of Operational Research, Elsevier, vol. 131(3), pages 476-481, June.
    4. 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.
    5. Teghem, J. & Dufrane, D. & Thauvoye, M. & Kunsch, P., 1986. "Strange: An interactive method for multi-objective linear programming under uncertainty," European Journal of Operational Research, Elsevier, vol. 26(1), pages 65-82, July.
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    Cited by:

    1. Abdelaziz, Fouad Ben, 2012. "Solution approaches for the multiobjective stochastic programming," European Journal of Operational Research, Elsevier, vol. 216(1), pages 1-16.
    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. Ballestero, Enrique & Bravo, Mila & Pérez-Gladish, Blanca & Arenas-Parra, Mar & Plà-Santamaria, David, 2012. "Socially Responsible Investment: A multicriteria approach to portfolio selection combining ethical and financial objectives," European Journal of Operational Research, Elsevier, vol. 216(2), pages 487-494.


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