A deep reinforcement learning approach for rail renewal and maintenance planning
R Mohammadi, Q He - Reliability Engineering & System Safety, 2022 - Elsevier
… potential capabilities of Deep Reinforcement Learning (DRL), … DRL methods to solve renewal
and maintenance planning. … -based approach is developed to optimize maintenance and …
and maintenance planning. … -based approach is developed to optimize maintenance and …
Reinforcement and deep reinforcement learning-based solutions for machine maintenance planning, scheduling policies, and optimization
O Ogunfowora, H Najjaran - Journal of Manufacturing Systems, 2023 - Elsevier
… of systems and machines with reinforcement learning, smart maintenance planners can be …
applications of reinforcement and deep reinforcement learning for maintenance planning and …
applications of reinforcement and deep reinforcement learning for maintenance planning and …
A deep reinforcement learning assisted simulated annealing algorithm for a maintenance planning problem
… solution approaches to corrective maintenance planning. This study contributes to maintenance
planning … Deep Reinforcement Learning (DRL) and Simulated Annealing (SA) algorithm. …
planning … Deep Reinforcement Learning (DRL) and Simulated Annealing (SA) algorithm. …
Deep reinforcement learning driven inspection and maintenance planning under incomplete information and constraints
CP Andriotis, KG Papakonstantinou - Reliability Engineering & System …, 2021 - Elsevier
… maintenance planning, such as post-disaster recovery, eg in [19,20,21]. Respectively,
admissible solution strategies to the above approaches … and maintenance planning methods is …
admissible solution strategies to the above approaches … and maintenance planning methods is …
A deep reinforcement learning approach for maintenance planning
SS Rabbanian, M Nemati… - IIE Annual Conference …, 2021 - search.proquest.com
… would be the best maintenance decision whether to plan maintenance or to postpone it to a
… goal of Deep Reinforcement Learning in this study is to find the optimal maintenance policy …
… goal of Deep Reinforcement Learning in this study is to find the optimal maintenance policy …
Deep reinforcement learning for long‐term pavement maintenance planning
… maintenance planning problem, a machine learning method, DRL, was introduced in this
research to better learn the maintenance … cost-effectiveness in maintenance decision-making …
research to better learn the maintenance … cost-effectiveness in maintenance decision-making …
A maintenance planning framework using online and offline deep reinforcement learning
… management is an area of interest across several industries. Specifically, this paper develops
a deep reinforcement learning (… We approach the problem of rehabilitation planning in an …
a deep reinforcement learning (… We approach the problem of rehabilitation planning in an …
Deep-reinforcement-learning-based predictive maintenance model for effective resource management in industrial IoT
… We focus on the issues of maintenance management, which are essential to optimizing
production time of the machinery. One of the decision-making priorities is to assign the correct …
production time of the machinery. One of the decision-making priorities is to assign the correct …
Deep Reinforcement Learning Approach for Maintenance Planning in a Flow-Shop Scheduling Problem
MG Marchesano, L Staiano, G Guizzi… - New Trends in …, 2022 - ebooks.iospress.nl
… The aim of this paper is to use an RL approach for scheduling maintenance interventions
by considering the health status of the machine in terms of probability of failure. The approach …
by considering the health status of the machine in terms of probability of failure. The approach …
Deep reinforcement learning for condition-based maintenance planning of multi-component systems under dependent competing risks
… a customized Deep Reinforcement Learning (DRL) approach for the CBM planning of
multi-… Different from the threshold-based maintenance decision making in traditional CBM, the …
multi-… Different from the threshold-based maintenance decision making in traditional CBM, the …
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