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The benefit of receding horizon control: Near-optimal policies for stochastic inventory control

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  • Dural-Selcuk, Gozdem
  • Rossi, Roberto
  • Kilic, Onur A.
  • Tarim, S. Armagan

Abstract

In this paper we address the single-item, single-stocking point, non-stationary stochastic lot-sizing problem under backorder costs. It is well known that the (s, S) policy provides the optimal control for such inventory systems. However the computational difficulties and the nervousness inherent in (s, S) paved the way for the development of various near-optimal inventory control policies. We provide a systematic comparison of these policies and present their expected cost performances. We further show that when these policies are used in a receding horizon framework the cost performances improve considerably and differences among policies become insignificant.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:jomega:v:97:y:2020:i:c:s0305048319302440
    DOI: 10.1016/j.omega.2019.07.007
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    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. Gutierrez-Alcoba, Alejandro & Rossi, Roberto & Martin-Barragan, Belen & Embley, Tim, 2023. "The stochastic inventory routing problem on electric roads," European Journal of Operational Research, Elsevier, vol. 310(1), pages 156-167.
    6. Ren, Ke & Bidkhori, Hoda & Shen, Zuo-Jun Max, 2024. "Data-driven inventory policy: Learning from sequentially observed non-stationary data," Omega, Elsevier, vol. 123(C).

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