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Robust solutions and risk measures for a supply chain planning problem under uncertainty

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  • C A Poojari

    (Brunel University)

  • C Lucas

    (Brunel University)

  • G Mitra

    (Brunel University)

Abstract

We consider a strategic supply chain planning problem formulated as a two-stage stochastic integer programming (SIP) model. The strategic decisions include site locations, choices of production, packing and distribution lines, and the capacity increment or decrement policies. The SIP model provides a practical representation of real-world discrete resource allocation problems in the presence of future uncertainties which arise due to changes in the business and economic environment. Such models that consider the future scenarios (along with their respective probabilities) not only identify optimal plans for each scenario, but also determine a hedged strategy for all the scenarios. We 1) exploit the natural decomposable structure of the SIP problem through Benders’ decomposition, 2) approximate the probability distribution of the random variables using the generalized lambda distribution, and 3) through simulations, calculate the performance statistics and the risk measures for the two models, namely the expected-value and the here-and-now.

Suggested Citation

  • C A Poojari & C Lucas & G Mitra, 2008. "Robust solutions and risk measures for a supply chain planning problem under uncertainty," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(1), pages 2-12, January.
    • Handle: RePEc:pal:jorsoc:v:59:y:2008:i:1:d:10.1057_palgrave.jors.2602381
    DOI: 10.1057/palgrave.jors.2602381
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    5. Tang, Christopher S. & Davarzani, Hoda & Sarkis, Joseph, 2015. "Quantitative models for managing supply chain risks: A reviewAuthor-Name: Fahimnia, Behnam," European Journal of Operational Research, Elsevier, vol. 247(1), pages 1-15.
    6. 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.
    7. Jahani, Hamed & Abbasi, Babak & Sheu, Jiuh-Biing & Klibi, Walid, 2024. "Supply chain network design with financial considerations: A comprehensive review," European Journal of Operational Research, Elsevier, vol. 312(3), pages 799-839.
    8. M S Sodhi & C S Tang, 2011. "Determining supply requirement in the sales-and-operations-planning (S&OP) process under demand uncertainty: a stochastic programming formulation and a spreadsheet implementation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(3), pages 526-536, March.
    9. B D Williams & M A Waller, 2011. "Estimating a retailer's base stock level: an optimal distribution center order forecast policy," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(4), pages 662-666, April.
    10. Hahn, G.J. & Kuhn, H., 2012. "Simultaneous investment, operations, and financial planning in supply chains: A value-based optimization approach," International Journal of Production Economics, Elsevier, vol. 140(2), pages 559-569.
    11. Weskamp, Christoph & Koberstein, Achim & Schwartz, Frank & Suhl, Leena & Voß, Stefan, 2019. "A two-stage stochastic programming approach for identifying optimal postponement strategies in supply chains with uncertain demand," Omega, Elsevier, vol. 83(C), pages 123-138.

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