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Quantitative analysis of multi-periodic supply chain contracts with options via stochastic programming

Author

Listed:
  • VAN DELFT, Christian
  • VIAL, Jean-Philippe

    (University of Geneva)

Abstract

We propose a stochastic programming approach for quantitative analysis of supply contracts, involving flexibility, between a buyer and a supplier, in a supply chain framework. Specifically, we consider the case of multi-periodic contracts in the face of correlated demands. To design such contracts, one has to estimate the savings or costs induced for both parties, as well as the optimal orders and commitments. We show how to model the stochastic process of the demand and the decision problem for both parties using the algebraic modeling language AMPL. The resulting linear programs are solved with a commercial linear programming solver; we compute the economic performance of these contracts, giving evidence that this methodology allows to gain insight into realistic problems.

Suggested Citation

  • VAN DELFT, Christian & VIAL, Jean-Philippe, 2001. "Quantitative analysis of multi-periodic supply chain contracts with options via stochastic programming," Les Cahiers de Recherche 733, HEC Paris.
  • Handle: RePEc:ebg:heccah:0733
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    File URL: http://www.hec.fr/var/fre/storage/original/application/fccce44d0af83d2bc0da1379c5804531.pdf
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    References listed on IDEAS

    as
    1. Andrew J. Clark & Herbert Scarf, 2004. "Optimal Policies for a Multi-Echelon Inventory Problem," Management Science, INFORMS, vol. 50(12_supple), pages 1782-1790, December.
    2. Kahneman, Daniel & Tversky, Amos, 1979. "Prospect Theory: An Analysis of Decision under Risk," Econometrica, Econometric Society, vol. 47(2), pages 263-291, March.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    stochastic programming; supply contract; linear programming; modeling software; decision tree;

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • M11 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - Production Management

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