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Allocation planning under service-level contracts

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  • Kloos, Konstantin
  • Pibernik, Richard

Abstract

Motivated by the practical limitations of current demand fulfillment systems, this paper addresses the problem of allocation planning under service-level contracts in a multi-period setting. We provide a formal definition of the allocation planning problem under a type of service-level contract that is particularly relevant to manufacturing industries and formulate a corresponding stochastic dynamic program. Based on a rigorous formal analysis of the dynamic program, we derive the requirements a “good” allocation policy should meet and use them to evaluate the heuristic policies proposed in the literature and to derive new allocation policies that may enhance the performance of allocation planning under service-level contracts. After detailed characterization and discussion of these new policies, we present the results of an extensive numerical study that allow us to quantify and compare allocation policies’ performance and to derive recommendations for decision makers in practice.

Suggested Citation

  • Kloos, Konstantin & Pibernik, Richard, 2020. "Allocation planning under service-level contracts," European Journal of Operational Research, Elsevier, vol. 280(1), pages 203-218.
    • Handle: RePEc:eee:ejores:v:280:y:2020:i:1:p:203-218
    DOI: 10.1016/j.ejor.2019.07.018
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    Citations

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    Cited by:

    1. Fadaki, Masih & Asadikia, Atie, 2024. "Augmenting Monte Carlo Tree Search for managing service level agreements," International Journal of Production Economics, Elsevier, vol. 271(C).
    2. Kim, Nayeon & Montreuil, Benoit & Klibi, Walid, 2022. "Inventory availability commitment under uncertainty in a dropshipping supply chain," European Journal of Operational Research, Elsevier, vol. 302(3), pages 1155-1174.
    3. Martin Albrecht, 2021. "Component Allocation in Make-to-stock Assembly Systems," SN Operations Research Forum, Springer, vol. 2(2), pages 1-19, June.
    4. Seitz, Alexander & Grunow, Martin & Akkerman, Renzo, 2020. "Data driven supply allocation to individual customers considering forecast bias," International Journal of Production Economics, Elsevier, vol. 227(C).

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