Application of Markov renewal theory and semi‐Markov decision processes in maintenance modeling and optimization of multi‐unit systems-Reference-Cited by-同舟云学术

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Application of Markov renewal theory and semi‐Markov decision processes in maintenance modeling and optimization of multi‐unit systems

Published:2020-08-03 Issue:7 Volume:67 Page:548-558
ISSN:0894-069X
Container-title:Naval Research Logistics (NRL)
language:en
Short-container-title:Naval Research Logistics

Author:

Salari Nooshin¹,Makis Viliam¹^ORCID

Affiliation:

1. Mechanical and Industrial EngineeringUniversity of Toronto Toronto Ontario Canada

Funder

Natural Sciences and Engineering Research Council of Canada

Publisher

Wiley

Subject

Management Science and Operations Research,Ocean Engineering,Modeling and Simulation

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/nav.21932

Reference32 articles.

1. Monotone optimal preventive maintenance policies for stochastically failing equipment

2. Season-Dependent Condition-Based Maintenance for a Wind Turbine Using a Partially Observed Markov Decision Process

3. A sequential condition-based repair/replacement policy with non-periodic inspections for a system subject to continuous wear

4. Markov regenerative stochastic Petri nets

5. Markov renewal theory

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1. Structured replacement policies for a system subject to random mission types;Naval Research Logistics (NRL);2024-05-30

2. Reliability modelling and self-healing policy design for systems with limited resources;Reliability Engineering & System Safety;2023-12

3. Structured Replacement Policies for Offshore Wind Turbines;Probability in the Engineering and Informational Sciences;2023-10-02

4. A deep reinforcement learning approach for repair-based maintenance of multi-unit systems using proportional hazards model;Reliability Engineering & System Safety;2023-06

5. A novel condition-based maintenance framework for parallel manufacturing systems based on bivariate birth/birth–death processes;Reliability Engineering & System Safety;2023-01

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