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An investigation of setup instability in non-stationary stochastic inventory systems

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  • Kilic, Onur A.
  • Tarim, S. Armagan

Abstract

In stochastic inventory systems unfolding uncertainties in demand lead to the revision of earlier replenishment plans which in turn results in an instability or so-called system nervousness. In this paper, we provide the grounds for measuring system nervousness in non-stationary demand environments, and gauge the stability and the cost performances of (R,S) and (s,S) inventory policies. Our results reveal that, both the stability and the cost performance of inventory policies are affected by the demand pattern as well as the cost parameters, and the (R,S) policy has the potential to replace the cost-optimal (s,S) policy for systems with limited flexibility.

Suggested Citation

  • Kilic, Onur A. & Tarim, S. Armagan, 2011. "An investigation of setup instability in non-stationary stochastic inventory systems," International Journal of Production Economics, Elsevier, vol. 133(1), pages 286-292, September.
    • Handle: RePEc:eee:proeco:v:133:y:2011:i:1:p:286-292
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    References listed on IDEAS

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    1. Heisig, Gerald, 2001. "Comparison of (s,S) and (s,nQ) inventory control rules with respect to planning stability," International Journal of Production Economics, Elsevier, vol. 73(1), pages 59-82, August.
    2. 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.
    3. Dean H. Kropp & Robert C. Carlson, 1984. "A Lot-Sizing Algorithm for Reducing Nervousness in MRP Systems," Management Science, INFORMS, vol. 30(2), pages 240-244, February.
    4. Joseph D. Blackburn & Dean H. Kropp & Robert A. Millen, 1986. "A Comparison of Strategies to Dampen Nervousness in MRP Systems," Management Science, INFORMS, vol. 32(4), pages 413-429, April.
    5. Tarim, S. Armagan & Kingsman, Brian G., 2006. "Modelling and computing (Rn, Sn) policies for inventory systems with non-stationary stochastic demand," European Journal of Operational Research, Elsevier, vol. 174(1), pages 581-599, October.
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    Cited by:

    1. Tunc, Huseyin & Kilic, Onur A. & Tarim, S. Armagan & Eksioglu, Burak, 2013. "A simple approach for assessing the cost of system nervousness," International Journal of Production Economics, Elsevier, vol. 141(2), pages 619-625.
    2. 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.
    3. 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.
    4. Xiang, Mengyuan & Rossi, Roberto & Martin-Barragan, Belen & Tarim, S. Armagan, 2023. "A mathematical programming-based solution method for the nonstationary inventory problem under correlated demand," European Journal of Operational Research, Elsevier, vol. 304(2), pages 515-524.
    5. Gurkan, M. Edib & Tunc, Huseyin & Tarim, S. Armagan, 2022. "The joint stochastic lot sizing and pricing problem," Omega, Elsevier, vol. 108(C).
    6. Dural-Selcuk, Gozdem & Rossi, Roberto & Kilic, Onur A. & Tarim, S. Armagan, 2020. "The benefit of receding horizon control: Near-optimal policies for stochastic inventory control," Omega, Elsevier, vol. 97(C).
    7. Choudhary, Devendra & Shankar, Ravi, 2015. "The value of VMI beyond information sharing in a single supplier multiple retailers supply chain under a non-stationary (Rn, Sn) policy," Omega, Elsevier, vol. 51(C), pages 59-70.
    8. 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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