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Maintenance management for geographically distributed assets: a criticality-based approach

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  • Manco, Pasquale
  • Rinaldi, Marta
  • Caterino, Mario
  • Fera, Marcello
  • Macchiaroli, Roberto

Abstract

This paper provides a model to help decision-makers to choose the daily maintenance strategy for geographically distributed assets (GDA) where sites are located in a wide geographical area and a single maintenance centre is involved in managing the maintenance. A hierarchical structure has been used to represent the Multi-System Multi-Component network (MSMCN). A quantitative framework with sequential steps has been developed to plan a daily mix of maintenance actions. First, a dynamic criticality analysis identifies the critical items. A second screening adopts reliability thresholds to determine components that could be preventively replaced. Finally, an iterative economic comparison procedure selects the activities to schedule day by day. The proposed approach also considers time and resources constraints. The model was applied to a real case study to verify its feasibility. Results were compared to the results obtained implementing the current strategy in terms of total downtime, total number of sites visited and total maintenance cost. It was demonstrated that it is possible to reduce the total maintenance cost and the total number of sites visited in a year by balancing opportunistic and preventive maintenance activities with an appropriate selection of the model's thresholds.

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  • Manco, Pasquale & Rinaldi, Marta & Caterino, Mario & Fera, Marcello & Macchiaroli, Roberto, 2022. "Maintenance management for geographically distributed assets: a criticality-based approach," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
    • Handle: RePEc:eee:reensy:v:218:y:2022:i:pb:s0951832021006360
    DOI: 10.1016/j.ress.2021.108148
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    References listed on IDEAS

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    Cited by:

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    2. Si, Guojin & Xia, Tangbin & Gebraeel, Nagi & Wang, Dong & Pan, Ershun & Xi, Lifeng, 2022. "A reliability-and-cost-based framework to optimize maintenance planning and diverse-skilled technician routing for geographically distributed systems," Reliability Engineering and System Safety, Elsevier, vol. 226(C).

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