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Optimization Over Stochastic Integer Efficient Set

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Optimization Theory, Decision Making, and Operations Research Applications

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 31))

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Abstract

In this paper we study the problem of optimizing a linear function over an integer efficient solution set of a Multiple objective Stochastic Integer Linear Programming problem (MOSILP). Once the problem is converted into a deterministic one by adapting the 2-levels recourse approach, a new pivoting technique is applied to generate an optimal efficient solution without having to enumerate all of them. This method combines two techniques, the L-Shaped method and the combined method developed in [Kall, Stochastic Linear Programming (1976)]. A detailed didactic example is given to illustrate different steps of our algorithm.

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Author information

Authors and Affiliations

  1. USTHB University, 32, Bab-Ezzouar, El-Alia 16111, Algiers, Algeria

    Djamal Chaabane

  2. ENSTP-KOUBA, Algiers, Algeria

    Fatma Mebrek

Authors
  1. Djamal Chaabane
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  2. Fatma Mebrek
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Corresponding author

Correspondence to Djamal Chaabane .

Editor information

Editors and Affiliations

  1. , Faculty of Engineering, Aristotle University of Thessaloniki, Thessaloniki, 54124, Greece

    Athanasios Migdalas

  2. , Economical and Social Sciences, University of Macedonia, Loggou-Tourpali, Naoussa, 59200, Greece

    Angelo Sifaleras

  3. , Department of Applied Informatics, University of Macedonia, Egnatia St. 156, Thessaloniki, 54006, Greece

    Christos K. Georgiadis

  4. , Department of Marketing and Operations M, University of Macedonia, Agiou Dimitriou 49, Edessa, 58200, Greece

    Jason Papathanasiou

  5. , Department of Applied Informatics, University of Macedonia, Egnatia Street 156, Thessaloniki, 54006, Greece

    Emmanuil Stiakakis

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Chaabane, D., Mebrek, F. (2013). Optimization Over Stochastic Integer Efficient Set. In: Migdalas, A., Sifaleras, A., Georgiadis, C., Papathanasiou, J., Stiakakis, E. (eds) Optimization Theory, Decision Making, and Operations Research Applications. Springer Proceedings in Mathematics & Statistics, vol 31. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-5134-1_7

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