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Stochastic Programming for Supply Chain Planning Under Demand Uncertainty

In: Managing Supply Chain Risk

Author

Listed:
  • ManMohan S. Sodhi

    (City University)

  • Christopher S. Tang

    (University of California, Los Angeles)

Abstract

In this chapter we focus on stochastic programming for making optimal supply chain planning decisions under uncertainty. In particular, we extend deterministic linear programming for supply-chain planning (SCP) by using stochastic programming to incorporate the issues of demand risk and liquidity risk. Because the resulting stochastic linear programming model is similar to that of Asset-Liability Management (ALM) and because the literature using stochastic programming for ALM is extensive, we survey various modeling and solution choices developed in this literature and discuss their applicability to supply chain planning. This survey forms a basis for making modeling/solution choices in research and in practice to manage the risks of unmet demand, excess inventory and inadequate cash liquidity when demand is uncertain.

Suggested Citation

  • ManMohan S. Sodhi & Christopher S. Tang, 2012. "Stochastic Programming for Supply Chain Planning Under Demand Uncertainty," International Series in Operations Research & Management Science, in: Managing Supply Chain Risk, edition 127, chapter 0, pages 259-277, Springer.
    • Handle: RePEc:spr:isochp:978-1-4614-3238-8_15
    DOI: 10.1007/978-1-4614-3238-8_15
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    Cited by:

    1. Mustafa UNGAN, 2017. "Relationship Between Supply Chain Risks: An Empirical Work," Risk in Contemporary Economy, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, pages 293-303.
    2. Mustafa UNGAN, 2017. "Relationship Between Supply Chain Risks: An Empirical Work," Proceedings RCE 2017, Editura Lumen, vol. 0, pages 293-303, November.

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