RESEARCH PAPER
Global-dynamic maintenance management of multi-component degrading plants with non-immediate replacement: a self-adaptive grouping approach
1
School of Reliability and Systems Engineering, Beihang University, China
2
School of Management Engineering, Qingdao University of Technology, China
These authors had equal contribution to this work
Submission date: 2024-05-09
Final revision date: 2024-07-08
Acceptance date: 2024-09-09
Online publication date: 2024-09-26
Publication date: 2024-09-26
Corresponding author
Eksploatacja i Niezawodność – Maintenance and Reliability 2025;27(1):193132
HIGHLIGHTS
- A global adaptive group maintenance strategy oriented to degrading systems is proposed.
- Maintenance is delayed to balance failure risk mitigation and resource preparation.
- A global dynamic union of group PM and OM is realized within an infinite time horizon.
- A heuristic reverse group search algorithm is devised to improve optimization efficiency.
KEYWORDS
cost-effectivenessgroup maintenanceintelligent asset managementreplacement schedulingdynamic decision-making
TOPICS
ABSTRACT
Group maintenance management is pivotal to ensure operational safety and performance of multi-component plants attributed to its capacity to share maintenance resources/time. Most group maintenance models, however, are globally/partially static following pre-specified maintenance sequences, with limited focus on the adaptability of group partition procedure. To fill this gap, we devise an innovative global-dynamic condition-based group maintenance policy. In contrast to existing methods, it allows for (a) postponement of component maintenance upon inspection to facilitate flexible resource allocation, and (b) automatic refinement of group maintenance structures to promote adaptivity. The proposed policy is shown to establish a global renewal mechanism for maintenance group partition over an infinite time horizon, which constitutes a dynamic union of both scheduled maintenance and opportunistic maintenance to mitigate downtime. A heuristic grouping algorithm is developed to realize efficient maintenance group planning, which verifies model effectiveness via numerical experiments.
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