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A multi-item approach to repairable stocking and expediting in a fluctuating demand environment

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  • Arts, Joachim

Abstract

We consider a single inventory location where multiple types of repairable spare parts are kept for service and maintenance of several different fleets of assets. Demand for each part is a Markov modulated Poisson process (MMPP). Each fleet has a target for the maximum expected number of assets down for lack of a spare part. The inventory manager can meet this target by stocking repairables and by expediting the repair of parts. Expedited repairs have a shorter lead time. There are multiple repair shops (or departments) that handle the repair of parts and the load imposed on repair shops by expedited repairs is constrained. A dual-index policy makes stocking and expediting decisions that depend on demand fluctuations for each spare part type. We formulate the above problem as a non-linear non-convex integer programing problem and provide an algorithm based on column generation to compute feasible near optimal solutions and tight lower bounds. We show how to use the MMPP to model demand fluctuations in maintenance and other settings, including a moment fitting algorithm. We quantify the value of lead time flexibility and show that effective use of this flexibility can yield cost reductions of around 25 percent.

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  • Arts, Joachim, 2017. "A multi-item approach to repairable stocking and expediting in a fluctuating demand environment," European Journal of Operational Research, Elsevier, vol. 256(1), pages 102-115.
    • Handle: RePEc:eee:ejores:v:256:y:2017:i:1:p:102-115
    DOI: 10.1016/j.ejor.2016.06.003
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    Cited by:

    1. Driessen, M.A. & van Houtum, G.J. & Zijm, W.H.M. & Rustenburg, W.D., 2020. "Capacity assignment in repair shops with high material uncertainty," International Journal of Production Economics, Elsevier, vol. 221(C).
    2. Shuai Zhang & Kai Huang & Yufei Yuan, 2021. "Spare Parts Inventory Management: A Literature Review," Sustainability, MDPI, vol. 13(5), pages 1-23, February.
    3. Svoboda, Josef & Minner, Stefan & Yao, Man, 2021. "Typology and literature review on multiple supplier inventory control models," European Journal of Operational Research, Elsevier, vol. 293(1), pages 1-23.
    4. Benjamin Avanzi & Greg Taylor & Bernard Wong & Alan Xian, 2020. "Modelling and understanding count processes through a Markov-modulated non-homogeneous Poisson process framework," Papers 2003.13888, arXiv.org, revised May 2020.
    5. Avci, Mualla Gonca & Selim, Hasan, 2018. "A multi-objective simulation-based optimization approach for inventory replenishment problem with premium freights in convergent supply chains," Omega, Elsevier, vol. 80(C), pages 153-165.
    6. Walid W. Nasr, 2022. "Inventory systems with stochastic and batch demand: computational approaches," Annals of Operations Research, Springer, vol. 309(1), pages 163-187, February.
    7. Christiane B. Haubitz & Ulrich W. Thonemann, 2021. "How to Change a Running System—Controlling the Transition to Optimized Spare Parts Inventory Policies," Production and Operations Management, Production and Operations Management Society, vol. 30(5), pages 1386-1405, May.
    8. Avanzi, Benjamin & Taylor, Greg & Wong, Bernard & Xian, Alan, 2021. "Modelling and understanding count processes through a Markov-modulated non-homogeneous Poisson process framework," European Journal of Operational Research, Elsevier, vol. 290(1), pages 177-195.
    9. Yera, Yoel G. & Lillo, Rosa E. & Ramírez-Cobo, Pepa, 2019. "Fitting procedure for the two-state Batch Markov modulated Poisson process," European Journal of Operational Research, Elsevier, vol. 279(1), pages 79-92.

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