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Robust strategies for facility location under uncertainty

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Author Info

  • Gülpınar, Nalan
  • Pachamanova, Dessislava
  • Çanakoğlu, Ethem
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    Abstract

    This paper considers a stochastic facility location problem in which multiple capacitated facilities serve customers with a single product, and a stockout probabilistic requirement is stated as a chance constraint. Customer demand is assumed to be uncertain and to follow either a normal or an ambiguous distribution. We study robust approximations to the problem in order to incorporate information about the random demand distribution in the best possible, computationally tractable way. We also discuss how a decision maker’s risk preferences can be incorporated in the problem through robust optimization. Finally, we present numerical experiments that illustrate the performance of the different robust formulations. Robust optimization strategies for facility location appear to have better worst-case performance than nonrobust strategies. They also outperform nonrobust strategies in terms of realized average total cost when the actual demand distributions have higher expected values than the expected values used as input to the optimization models.

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    File URL: http://www.sciencedirect.com/science/article/pii/S0377221712006042
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    Bibliographic Info

    Article provided by Elsevier in its journal European Journal of Operational Research.

    Volume (Year): 225 (2013)
    Issue (Month): 1 ()
    Pages: 21-35

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    Handle: RePEc:eee:ejores:v:225:y:2013:i:1:p:21-35

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

    Related research

    Keywords: Robust facility location decisions; Robust inventory management; Ambiguous demand distributions; Robust approximations to chance constraints;

    References

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    1. Owen, Susan Hesse & Daskin, Mark S., 1998. "Strategic facility location: A review," European Journal of Operational Research, Elsevier, vol. 111(3), pages 423-447, December.
    2. E S Sheppard, 1974. "A conceptual framework for dynamic location - allocation analysis," Environment and Planning A, Pion Ltd, London, vol. 6(5), pages 547-564, May.
    3. Santoso, Tjendera & Ahmed, Shabbir & Goetschalckx, Marc & Shapiro, Alexander, 2005. "A stochastic programming approach for supply chain network design under uncertainty," European Journal of Operational Research, Elsevier, vol. 167(1), pages 96-115, November.
    4. Aharon, Ben-Tal & Boaz, Golany & Shimrit, Shtern, 2009. "Robust multi-echelon multi-period inventory control," European Journal of Operational Research, Elsevier, vol. 199(3), pages 922-935, December.
    5. Gulpinar, Nalan & Rustem, Berc, 2007. "Robust optimal decisions with imprecise forecasts," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3595-3611, April.
    6. Yao, Zhishuang & Lee, Loo Hay & Jaruphongsa, Wikrom & Tan, Vicky & Hui, Chen Fei, 2010. "Multi-source facility location-allocation and inventory problem," European Journal of Operational Research, Elsevier, vol. 207(2), pages 750-762, December.
    7. Berman, Oded & Krass, Dmitry & Tajbakhsh, M. Mahdi, 2012. "A coordinated location-inventory model," European Journal of Operational Research, Elsevier, vol. 217(3), pages 500-508.
    8. Zuo-Jun Max Shen & Mark S. Daskin, 2005. "Trade-offs Between Customer Service and Cost in Integrated Supply Chain Design," Manufacturing & Service Operations Management, INFORMS, vol. 7(3), pages 188-207, September.
    9. Karthik Natarajan & Dessislava Pachamanova & Melvyn Sim, 2008. "Incorporating Asymmetric Distributional Information in Robust Value-at-Risk Optimization," Management Science, INFORMS, vol. 54(3), pages 573-585, March.
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    Cited by:
    1. Gabrel, Virginie & Murat, Cécile & Thiele, Aurélie, 2014. "Recent advances in robust optimization: An overview," European Journal of Operational Research, Elsevier, vol. 235(3), pages 471-483.

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