Spare parts recommendation for corrective maintenance of capital goods considering demand dependency

İpek Dursun, Anastasiia Grishina, Alp Akcay (Corresponding author), Geert-Jan van Houtum (Corresponding author)

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

We consider a maintenance service provider that services geographically dispersed customers with its local service engineers. Traditionally, when a system failure is reported, a service engineer makes a diagnostic visit to the customer’s location to perform corrective maintenance. If spare parts are required, they are ordered and a second visit is scheduled at a later date to complete the corrective maintenance. In this paper, the service provider can proactively send spare parts to the customer to avoid the costly second visit. Motivated by a real-world problem in the high-tech industry, our model considers the cost of a second visit, fixed shipment costs, retrieval costs for the parts that are sent to the customer, and send-back costs for the parts that are sent but not used for corrective maintenance. The uncertainty in the set of parts required for corrective maintenance is modeled with a general distribution that can capture the dependencies between demands for different spare parts. We formulate an integer linear program to find the optimal set of spare parts that minimizes the expected total cost. We derive analytical results for the structure of the optimal policy and compare the optimal policy with two benchmark policies from practice. We observe that the policies from practice often find the optimal policy, and a new heuristic policy that exploits the structure of the optimal policy, on average, performs better than the benchmark policies.
Original languageEnglish
Pages (from-to)71-86
Number of pages16
JournalEuropean Journal of Operational Research
Volume318
Issue number1
DOIs
Publication statusAccepted/In press - 2024

Funding

This research was partially supported by the EU project \u2018DayTiMe- Digital Lifecycle Twins for Predictive Maintenance\u2019 (grant agreement \u2018ITEA-2018-Daytime-17030\u2019). This research was partially supported by the \u2018DayTiMe- Digital Lifecycle Twins for Predictive Maintenance\u2019 project (grant agreement \u2018 ITEA-2018-Daytime-17030 \u2019).

FundersFunder number
European CommissionITEA-2018-Daytime-17030

    Keywords

    • Maintenance
    • Repair kit problem
    • Service control tower
    • Spare part recommendation

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