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Computing the non-stationary replenishment cycle inventory policy under stochastic supplier lead-times

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  • Rossi, Roberto
  • Tarim, S. Armagan
  • Hnich, Brahim
  • Prestwich, Steven

Abstract

In this paper we address the general multi-period production/inventory problem with non-stationary stochastic demand and supplier lead-time under service level constraints. A replenishment cycle policy (Rn,Sn) is modeled, where Rn is the nth replenishment cycle length and Sn is the respective order-up-to-level. We propose a stochastic constraint programming approach for computing the optimal policy parameters. In order to do so, a dedicated global chance-constraint and the respective filtering algorithm that enforce the required service level are presented. Our numerical examples show that a stochastic supplier lead-time significantly affects policy parameters with respect to the case in which the lead-time is assumed to be deterministic or absent.

Suggested Citation

  • Rossi, Roberto & Tarim, S. Armagan & Hnich, Brahim & Prestwich, Steven, 2010. "Computing the non-stationary replenishment cycle inventory policy under stochastic supplier lead-times," International Journal of Production Economics, Elsevier, vol. 127(1), pages 180-189, September.
    • Handle: RePEc:eee:proeco:v:127:y:2010:i:1:p:180-189
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    References listed on IDEAS

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    Cited by:

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    2. Roberto Rossi & S. Armagan Tarim & Ramesh Bollapragada, 2012. "Constraint-Based Local Search for Inventory Control Under Stochastic Demand and Lead Time," INFORMS Journal on Computing, INFORMS, vol. 24(1), pages 66-80, February.
    3. Shih-Hsien Tseng & Jia-Chen Yu, 2019. "Data-Driven Iron and Steel Inventory Control Policies," Mathematics, MDPI, vol. 7(8), pages 1-15, August.
    4. Pauls-Worm, Karin G.J. & Hendrix, Eligius M.T. & Haijema, René & van der Vorst, Jack G.A.J., 2014. "An MILP approximation for ordering perishable products with non-stationary demand and service level constraints," International Journal of Production Economics, Elsevier, vol. 157(C), pages 133-146.
    5. Visentin, Andrea & Prestwich, Steven & Rossi, Roberto & Tarim, S. Armagan, 2021. "Computing optimal (R,s,S) policy parameters by a hybrid of branch-and-bound and stochastic dynamic programming," European Journal of Operational Research, Elsevier, vol. 294(1), pages 91-99.
    6. Taleizadeh, Ata Allah & Zarei, Hamid Reza & Sarker, Bhaba R., 2017. "An optimal control of inventory under probablistic replenishment intervals and known price increase," European Journal of Operational Research, Elsevier, vol. 257(3), pages 777-791.
    7. Chia-Nan Wang & Thanh-Tuan Dang & Ngoc-Ai-Thy Nguyen, 2020. "A Computational Model for Determining Levels of Factors in Inventory Management Using Response Surface Methodology," Mathematics, MDPI, vol. 8(8), pages 1-23, July.
    8. Hoque, M.A., 2013. "A vendor–buyer integrated production–inventory model with normal distribution of lead time," International Journal of Production Economics, Elsevier, vol. 144(2), pages 409-417.
    9. Heydari, Jafar & Mahmoodi, Mansour & Taleizadeh, Ata Allah, 2016. "Lead time aggregation: A three-echelon supply chain model," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 89(C), pages 215-233.
    10. Louly, Mohamed-Aly & Dolgui, Alexandre, 2013. "Optimal MRP parameters for a single item inventory with random replenishment lead time, POQ policy and service level constraint," International Journal of Production Economics, Elsevier, vol. 143(1), pages 35-40.
    11. Wang, Xun & Disney, Stephen M. & Wang, Jing, 2012. "Stability analysis of constrained inventory systems with transportation delay," European Journal of Operational Research, Elsevier, vol. 223(1), pages 86-95.
    12. Rossi, Roberto & Kilic, Onur A. & Tarim, S. Armagan, 2015. "Piecewise linear approximations for the static–dynamic uncertainty strategy in stochastic lot-sizing," Omega, Elsevier, vol. 50(C), pages 126-140.

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