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Joint optimization of spare parts inventory and service engineers staffing with full backlogging
We consider the integrated planning of spare parts and service engineers that are needed for serving a group of systems. These systems are subject to different failure types, and for each failure, a service engineer with the necessary spare part has to be assigned to repair the system. The service p...
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Published in: | International journal of production economics 2019-06, Vol.212, p.39-50 |
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Main Authors: | , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | We consider the integrated planning of spare parts and service engineers that are needed for serving a group of systems. These systems are subject to different failure types, and for each failure, a service engineer with the necessary spare part has to be assigned to repair the system. The service provider follows a backlogging policy with part reservations. That is, a repair request is backlogged if one of the required resources is not immediately available upon demand. Moreover, a spare part is reserved if the requested spare part is in stock but no service engineer is immediately available. The spare parts are typically slow-movers and are managed according to a base-stock policy. The objective is to jointly determine the stock levels and the number of service engineers to minimize the total service costs subject to a constraint on the expected total waiting times of the repair calls. For the evaluation of a given setting, we present an exact method (computationally feasible for small problems) and an accurate approximation. For the joint optimization, we present a greedy heuristic that efficiently produces close-to-optimal results. We test how the heuristic performs compared to the optimal solution and the separate optimization of spare parts and service engineers in an extensive numerical study. In a case study with 93 types of spare parts, we show that the solution of the greedy algorithm is always within 2% of the optimal solution and is up to 20% better than a separated optimization approach encountered in practice. |
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ISSN: | 0925-5273 1873-7579 |
DOI: | 10.1016/j.ijpe.2019.02.007 |