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A practical implementation of stochastic programming: an application to the evaluation of option contracts in supply chains

Author

Listed:
  • Christian van Delft

    (GREGH - Groupement de Recherche et d'Etudes en Gestion à HEC - HEC Paris - Ecole des Hautes Etudes Commerciales - CNRS - Centre National de la Recherche Scientifique)

  • Jean-Philippe Vial

Abstract

Stochastic programming is a powerful analytical method in order to solve sequential decision-making problems under uncertainty. We describe an approach to build such stochastic linear programming models. We show that algebraic modeling languages make it possible for non-specialist users to formulate complex problems and have solved them by powerful commercial solvers. We illustrate our point in the case of option contracts in supply chain management and propose a numerical analysis of performance. We propose easy-to-implement discretization procedures of the stochastic process in order to limit the size of the event tree in a multi-period environment.

Suggested Citation

  • Christian van Delft & Jean-Philippe Vial, 2004. "A practical implementation of stochastic programming: an application to the evaluation of option contracts in supply chains," Post-Print hal-00471409, HAL.
    • Handle: RePEc:hal:journl:hal-00471409
    DOI: 10.1016/j.automatica.2003.12.008
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    Citations

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    Cited by:

    1. Araneda-Fuentes, Cristina & Lustosa, Leonardo Junqueira & Minner, Stefan, 2015. "A contract for coordinating capacity decisions in a business-to-business (B2B) supply chain," International Journal of Production Economics, Elsevier, vol. 165(C), pages 158-171.
    2. Fattahi, Mohammad, 2021. "Resilient procurement planning for supply chains: A case study for sourcing a critical mineral material," Resources Policy, Elsevier, vol. 74(C).
    3. Dmitry Ivanov & Alexandre Dolgui & Boris Sokolov & Marina Ivanova, 2017. "Literature review on disruption recovery in the supply chain," International Journal of Production Research, Taylor & Francis Journals, vol. 55(20), pages 6158-6174, October.
    4. Karl Inderfurth & Peter Kelle & Rainer Kleber, 2018. "Inventory control in dual sourcing commodity procurement with price correlation," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 26(1), pages 93-119, March.
    5. Azaron, A. & Brown, K.N. & Tarim, S.A. & Modarres, M., 2008. "A multi-objective stochastic programming approach for supply chain design considering risk," International Journal of Production Economics, Elsevier, vol. 116(1), pages 129-138, November.
    6. Y Shi & F Wu & L K Chu & D Sculli & Y H Xu, 2011. "A portfolio approach to managing procurement risk using multi-stage stochastic programming," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(11), pages 1958-1970, November.
    7. Aharon Ben-Tal & Boaz Golany & Arkadi Nemirovski & Jean-Philippe Vial, 2005. "Retailer-Supplier Flexible Commitments Contracts: A Robust Optimization Approach," Manufacturing & Service Operations Management, INFORMS, vol. 7(3), pages 248-271, February.

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