Modelling, analysis and improvement of an integrated chance-constrained model for level of repair analysis and spare parts supply control

In traditional practices, maintenance system and spare parts inventory control are usually considered in isolation, resulting in suboptimality. In a military system, the level of repair analysis (LORA) is often employed to help operate its repair networks. In this paper, we consider an integrated LO...

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Vydáno v:International journal of production research Ročník 58; číslo 10; s. 3090 - 3109
Hlavní autoři: Liu, Weimiao, Liu, Kanglin, Deng, Tianhu
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Taylor & Francis 18.05.2020
Taylor & Francis LLC
Témata:
Algorithms
Constraints
Control systems
Inventory control
level of repair analysis
Maintenance
Maintenance scheduling
non-linear programming
Nonlinear programming
Repair
Spare parts
ISSN:0020-7543, 1366-588X
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Shrnutí:In traditional practices, maintenance system and spare parts inventory control are usually considered in isolation, resulting in suboptimality. In a military system, the level of repair analysis (LORA) is often employed to help operate its repair networks. In this paper, we consider an integrated LORA and inventory control problem and formulate this problem as a mixed-integer nonlinear programming problem with chance constraints. Two second-order cone constraints are proposed to approximate the chance constraints. Furthermore, we propose an outer approximation (OA) algorithm based on the OA cuts. Extensive numerical results show that the OA algorithm significantly improves the computational efficiency under various types of components and network complexity. Next, we investigate the influence of service level and resource capacity, and propose the findings. Our results indicate that a higher service level leads to steeper costs, more resources, larger storage and heavier repair burdens at operating sites. Moreover, enhancements in resource capacity from the status quo lead to improvements in repairs and shrinkage in discards, bringing direct economic benefits. The insights extend to uncertain settings. It may be initially counterintuitive for many practitioners that demand uncertainty poses relatively subtle impacts.
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ISSN:0020-7543
1366-588X
DOI:10.1080/00207543.2019.1629669