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Inspection policy optimization for hierarchical multistate systems under uncertain mission scenarios: a risk-averse perspective

Zhang, Boyuan, Liu, Yu, Wu, Shaomin (2024) Inspection policy optimization for hierarchical multistate systems under uncertain mission scenarios: a risk-averse perspective. IISE Transactions, . pp. 1-17. ISSN 2472-5854. E-ISSN 2472-5862. (doi:10.1080/24725854.2024.2322695) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided) (KAR id:105085)

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Most engineered systems intend to perform missions with a pre-specified target success probability to reduce undesirable failure risks. Before executing the next mission, inspection activities are conducted across various physical levels for assessing the probability of mission success. However, due to the randomness of a system’s degradation behavior and the presence of measurement errors, inspection results inevitably contain uncertainty. Meanwhile, the mission duration and acceptable system states may also be uncertain due to uncontrollable factors, such as random operating environments and mission demands. In such a circumstance, it is of great significance to identify the optimal multilevel inspection policy to answer, as great confident as possible, the question that the system can complete the next mission with a target mission success probability. A novel metric is developed to gauge the effectiveness of a multilevel inspection policy to assess if the system can complete the next mission with the target success probability from a risk-averse perspective, based on which an optimization method is put forth to seek an inspection policy under uncertain scenarios with the aim of minimizing the maximum regret of the proposed metric. A stochastic fractal search algorithm, along with two tailored local search rules, is designed to resolve the resulting optimization problem efficiently. Two cases, including a three-component system and a rocket fueling mechanism’s control system, are used to illustrate the efficacy of the proposed approach which is capable of effectively identifying the risk of mission failures by inspection policies.

Item Type: Article
DOI/Identification number: 10.1080/24725854.2024.2322695
Uncontrolled keywords: min-max regret; multilevel inspection; multistate systems; risk-averse;uncertain mission scenarios
Subjects: H Social Sciences > HA Statistics > HA33 Management Science
Divisions: Divisions > Kent Business School - Division > Department of Analytics, Operations and Systems
Funders: National Natural Science Foundation of China (
Depositing User: Shaomin Wu
Date Deposited: 22 Feb 2024 22:01 UTC
Last Modified: 17 Apr 2024 11:09 UTC
Resource URI: (The current URI for this page, for reference purposes)

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