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A state space augmentation algorithm for the replenishment cycle inventory policy

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

Abstract

In this work we propose an efficient dynamic programming approach for computing replenishment cycle policy parameters under non-stationary stochastic demand and service level constraints. The replenishment cycle policy is a popular inventory control policy typically employed for dampening planning instability. The approach proposed in this work achieves a significant computational efficiency and it can solve any relevant size instance in trivial time. Our method exploits the well known concept of state space relaxation. A filtering procedure and an augmenting procedure for the state space graph are proposed. Starting from a relaxed state space graph our method tries to remove provably suboptimal arcs and states (filtering) and then it tries to efficiently build up (augmenting) a reduced state space graph representing the original problem. Our experimental results show that the filtering procedure and the augmenting procedure often generate a small filtered state space graph, which can be easily processed using dynamic programming in order to produce a solution for the original problem.

Suggested Citation

  • Rossi, Roberto & Tarim, S. Armagan & Hnich, Brahim & Prestwich, Steven, 2011. "A state space augmentation algorithm for the replenishment cycle inventory policy," International Journal of Production Economics, Elsevier, vol. 133(1), pages 377-384, September.
    • Handle: RePEc:eee:proeco:v:133:y:2011:i:1:p:377-384
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    References listed on IDEAS

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    1. Tarim, S. Armagan & Smith, Barbara M., 2008. "Constraint programming for computing non-stationary (R, S) inventory policies," European Journal of Operational Research, Elsevier, vol. 189(3), pages 1004-1021, September.
    2. Tarim, S. Armagan & Kingsman, Brian G., 2004. "The stochastic dynamic production/inventory lot-sizing problem with service-level constraints," International Journal of Production Economics, Elsevier, vol. 88(1), pages 105-119, March.
    3. de Kok, Ton & Inderfurth, Karl, 1997. "Nervousness in inventory management: Comparison of basic control rules," European Journal of Operational Research, Elsevier, vol. 103(1), pages 55-82, November.
    4. James H. Bookbinder & Jin-Yan Tan, 1988. "Strategies for the Probabilistic Lot-Sizing Problem with Service-Level Constraints," Management Science, INFORMS, vol. 34(9), pages 1096-1108, September.
    5. Harvey M. Wagner & Thomson M. Whitin, 1958. "Dynamic Version of the Economic Lot Size Model," Management Science, INFORMS, vol. 5(1), pages 89-96, October.
    6. Pujawan, I Nyoman & Silver, Edward A., 2008. "Augmenting the lot sizing order quantity when demand is probabilistic," European Journal of Operational Research, Elsevier, vol. 188(3), pages 705-722, August.
    7. Tempelmeier, Horst, 2007. "On the stochastic uncapacitated dynamic single-item lotsizing problem with service level constraints," European Journal of Operational Research, Elsevier, vol. 181(1), pages 184-194, August.
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    Cited by:

    1. 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.
    2. Liu, Kanglin & Zhang, Zhi-Hai, 2018. "Capacitated disassembly scheduling under stochastic yield and demand," European Journal of Operational Research, Elsevier, vol. 269(1), pages 244-257.
    3. 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.
    4. Huseyin Tunc & Onur A. Kilic & S. Armagan Tarim & Roberto Rossi, 2018. "An Extended Mixed-Integer Programming Formulation and Dynamic Cut Generation Approach for the Stochastic Lot-Sizing Problem," INFORMS Journal on Computing, INFORMS, vol. 30(3), pages 492-506, August.
    5. 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.
    6. Roberto Rossi & S. Tarim & Brahim Hnich & Steven Prestwich, 2012. "Constraint programming for stochastic inventory systems under shortage cost," Annals of Operations Research, Springer, vol. 195(1), pages 49-71, May.
    7. 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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