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Fuzzy versus stochastic approaches to multicriteria linear programming under uncertainty

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  • R. Slowinski
  • J. Teghem

Abstract

Recently, both authors independently proposed two different approaches to multicriteria linear programming under uncertainty with a view of an application to some long term planning problems. Slowinski [11] has developed a method called FLIP (Fuzzy LInear Programming) based on the application of fuzzy numbers for modeling imprecise data. On the other hand, Teghem et al. [17] have proposed the method STRANGE (STRAtegy for Nuclear Generation of Electricity), a stochastic approach to the same problem. Both methods are interactive and at each step present to the decision maker (DM) a large representation of efficient solutions. The aim of this study is to compare FLIP and STRANGE. A didactic example is first defined and resolved by both methods. Next, every stage of both procedures is analyzed and compared on the basis of this example; taking into account imprecise data, formulation of deterministic multicriteria problems associated with the original problem, getting the first compromise solution, the role of the DM in the interactive decision‐making steps, etc. For each of these stages, possible limitations, advantages, and inconveniences of both methods are emphasized. General conclusions following from this comparison are finally drawn.

Suggested Citation

  • R. Slowinski & J. Teghem, 1988. "Fuzzy versus stochastic approaches to multicriteria linear programming under uncertainty," Naval Research Logistics (NRL), John Wiley & Sons, vol. 35(6), pages 673-695, December.
    • Handle: RePEc:wly:navres:v:35:y:1988:i:6:p:673-695
    DOI: 10.1002/1520-6750(198812)35:63.0.CO;2-L
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    References listed on IDEAS

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    1. Kunsch, P.L. & Teghem, J. Jr., 1987. "Nuclear fuel cycle optimization using multi-objective stochastic linear programming," European Journal of Operational Research, Elsevier, vol. 31(2), pages 240-249, August.
    2. Kall, P., 1982. "Stochastic programming," European Journal of Operational Research, Elsevier, vol. 10(2), pages 125-130, June.
    3. 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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    1. Ryu, Kwangyeol & Yücesan, Enver, 2010. "A fuzzy newsvendor approach to supply chain coordination," European Journal of Operational Research, Elsevier, vol. 200(2), pages 421-438, January.
    2. Luhandjula, M.K., 2006. "Fuzzy stochastic linear programming: Survey and future research directions," European Journal of Operational Research, Elsevier, vol. 174(3), pages 1353-1367, November.
    3. Shih-Pin Chen & Wen-Lung Huang, 2014. "Solving Fuzzy Multiproduct Aggregate Production Planning Problems Based on Extension Principle," International Journal of Mathematics and Mathematical Sciences, Hindawi, vol. 2014, pages 1-18, August.

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