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Reducing costs of repairable inventory supply systems via dynamic scheduling

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  • Tiemessen, H.G.H.
  • van Houtum, G.J.

Abstract

We study a system consisting of one repair shop and one stockpoint, where spare parts of multiple critical repairables are kept on stock to serve an installed base of technical systems. Part requests are met from stock if possible, and backordered otherwise. The objective is to minimize aggregate downtime via smart repair job scheduling. We evaluate various relevant dynamic scheduling policies, including two that stem from other application fields. One of them is the myopic allocation rule from the make-to-stock environment. It selects the SKU with the highest expected backorder reduction per invested time unit and has excellent performance on repairable inventory systems. It combines the following three strengths: (i) it selects the SKU with the shortest expected repair time in case of backorders, (ii) it recognizes the benefits of short average repair times even if there are no backorders, and (iii) it takes the stochasticity of the part failure processes into account. We investigate the optimality gaps of the heuristic scheduling rules, compare their performance on a large test bed containing problem instances of real-life size, and illustrate the impact of key problem characteristics on the aggregate downtime. We show that the myopic allocation rule performs well and that it outperforms the other heuristic scheduling rules.

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  • Tiemessen, H.G.H. & van Houtum, G.J., 2013. "Reducing costs of repairable inventory supply systems via dynamic scheduling," International Journal of Production Economics, Elsevier, vol. 143(2), pages 478-488.
    • Handle: RePEc:eee:proeco:v:143:y:2013:i:2:p:478-488
    DOI: 10.1016/j.ijpe.2012.08.008
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    Cited by:

    1. Rezaei Somarin, Aghil & Chen, Songlin & Asian, Sobhan & Wang, David Z.W., 2017. "A heuristic stock allocation rule for repairable service parts," International Journal of Production Economics, Elsevier, vol. 184(C), pages 131-140.
    2. Pedram Sahba & Barış Balcıog̃lu & Dragan Banjevic, 2018. "Multilevel rationing policy for spare parts when demand is state dependent," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 40(3), pages 751-780, July.
    3. Andrei Sleptchenko & M. Eric Johnson, 2015. "Maintaining Secure and Reliable Distributed Control Systems," INFORMS Journal on Computing, INFORMS, vol. 27(1), pages 103-117, February.
    4. Turan, Hasan Hüseyin & Atmis, Mahir & Kosanoglu, Fuat & Elsawah, Sondoss & Ryan, Michael J., 2020. "A risk-averse simulation-based approach for a joint optimization of workforce capacity, spare part stocks and scheduling priorities in maintenance planning," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    5. Minjae Park & Ki Mun Jung & Dong Ho Park, 2016. "Optimal warranty policies considering repair service and replacement service under the manufacturer’s perspective," Annals of Operations Research, Springer, vol. 244(1), pages 117-132, September.
    6. Topan, E. & Eruguz, A.S. & Ma, W. & van der Heijden, M.C. & Dekker, R., 2020. "A review of operational spare parts service logistics in service control towers," European Journal of Operational Research, Elsevier, vol. 282(2), pages 401-414.
    7. 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).
    8. Somarin, Aghil Rezaei & Sharma, Pankaj & Tiwari, Sunil & Chen, Songlin, 2023. "Stock reallocation policy for repairable service parts in case of supply disruptions due to extreme weather events," International Journal of Production Economics, Elsevier, vol. 256(C).

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