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An optimization approach for brass casting blending problem under aletory and epistemic uncertainties

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  • SakallI, Ümit Sami
  • Baykoç, Ömer Faruk
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    Abstract

    A critical process in brass casting is the determination of the materials and their quantities to be added into the blend. The reason of being critical is the uncertainty about metal percentages in scrap raw materials. In this paper, the aleatory and epistemic uncertainties, which are modeled by using probability and possibility theory, respectively, have been handled simultaneously in a blending optimization problem for brass casting and a solution approach that transforms the possibilistic uncertainties into probabilistic ones is proposed. A numerical example is performed by the data supplied from MKE brass factory in Turkey. The results of the example have showed that the proposed approach can be effectively used for solving blending problem including aleatory and epistemic uncertainties in brass casting and other scrap based production process.

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    File URL: http://www.sciencedirect.com/science/article/pii/S0925527311002398
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    Bibliographic Info

    Article provided by Elsevier in its journal International Journal of Production Economics.

    Volume (Year): 133 (2011)
    Issue (Month): 2 (October)
    Pages: 708-718

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    Handle: RePEc:eee:proeco:v:133:y:2011:i:2:p:708-718

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    Web page: http://www.elsevier.com/locate/ijpe

    Related research

    Keywords: Blending problem Uncertainty Probability/possibility transformation Brass casting Optimization;

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    1. Mula, J. & Poler, R. & Garcia-Sabater, J.P. & Lario, F.C., 2006. "Models for production planning under uncertainty: A review," International Journal of Production Economics, Elsevier, vol. 103(1), pages 271-285, September.
    2. A. Charnes & W. W. Cooper, 1959. "Chance-Constrained Programming," Management Science, INFORMS, vol. 6(1), pages 73-79, October.
    3. Rong, Aiying & Lahdelma, Risto, 2008. "Fuzzy chance constrained linear programming model for optimizing the scrap charge in steel production," European Journal of Operational Research, Elsevier, vol. 186(3), pages 953-964, May.
    4. Wang, Reay-Chen & Liang, Tien-Fu, 2005. "Applying possibilistic linear programming to aggregate production planning," International Journal of Production Economics, Elsevier, vol. 98(3), pages 328-341, December.
    5. Ashayeri, J. & van Eijs, A. G. M. & Nederstigt, P., 1994. "Blending modelling in a process manufacturing: A case study," European Journal of Operational Research, Elsevier, vol. 72(3), pages 460-468, February.
    6. Inuiguchi, Masahiro & Sakawa, Masatoshi & Kume, Yasufumi, 1994. "The usefulness of possibilistic programming in production planning problems," International Journal of Production Economics, Elsevier, vol. 33(1-3), pages 45-52, January.
    7. Dubois, Didier, 2006. "Possibility theory and statistical reasoning," Computational Statistics & Data Analysis, Elsevier, vol. 51(1), pages 47-69, November.
    8. Dubois, Didier & Prade, Henri, 1986. "Fuzzy sets and statistical data," European Journal of Operational Research, Elsevier, vol. 25(3), pages 345-356.
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