Guided probabilistic reinforcement learning for sampling-efficient maintenance scheduling of multi-component system-Reference-Cited by-同舟云学术

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Guided probabilistic reinforcement learning for sampling-efficient maintenance scheduling of multi-component system

Published:2023-07 Issue: Volume:119 Page:677-697
ISSN:0307-904X
Container-title:Applied Mathematical Modelling
language:en
Short-container-title:Applied Mathematical Modelling

Author:

Zhang Yiming,Zhang Dingyang,Zhang Xiaoge^ORCID,Qiu Lemiao,Chan Felix T.S.,Wang Zili,Zhang Shuyou

Funder

National Natural Science Foundation of China

Research Grants Council, University Grants Committee

The Hong Kong Polytechnic University

National Key Research and Development Program of China

Publisher

Elsevier BV

Subject

Applied Mathematics,Modeling and Simulation

Reference73 articles.

1. Group maintenance scheduling for two-component systems with failure interaction;Yang;Appl. Math. Modell.,2019

2. A joint production scheduling approach considering multiple resources and preventive maintenance tasks;Wong;Int. J. Prod. Res.,2013

3. Modeling the effects of dependence between competing failure processes on the condition-based preventive maintenance policy;Cao;Appl. Math. Modell.,2021

4. Analyzing human error in aircraft ground damage incidents;Wenner;Int. J. Ind. Ergon.,2000

5. Bayesian network modeling of accident investigation reports for aviation safety assessment;Zhang;Reliab. Eng. Syst. Saf.,2021

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1. Efficient opportunistic maintenance strategies via pruning in parallel–series systems with economic dependence;Computers & Industrial Engineering;2024-10

2. Multi-agent Deep Q-Learning for Maintenance Scheduling of Engineering System with Large-Scale State Space;Mechanisms and Machine Science;2024

3. Probabilistic Diagnosis of Aviation Engine with Invertible Neural Network;Mechanisms and Machine Science;2024

4. Efficient Opportunistic Maintenance Strategies in Parallel-Series Systems with Economic Dependence;2024

5. Reinforcement and deep reinforcement learning-based solutions for machine maintenance planning, scheduling policies, and optimization;Journal of Manufacturing Systems;2023-10

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