Name
Title | ||
|---|---|---|
Dynamic Selective Maintenance Optimization for Multi-State Systems Over A Finite Horizon: A Deep Reinforcement Learning Approach |
Abstract | ||
|---|---|---|
•The problem is formulated as a Markov decision process with a mixed state space.•The deep reinforcement learning algorithm is customized to resolve the problem.•A postprocess is developed to facilitate the actor to search the optimal solution.•The experience replay and the target network are utilized for training the agent. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.ejor.2019.10.049 | European Journal of Operational Research |
Keywords | Field | DocType |
Maintenance,Dynamic selective maintenance,Deep reinforcement learning,Imperfect maintenance,Multi-state system | Mathematical optimization,Imperfect,Industrial engineering,Markov decision process,Curse of dimensionality,Optimal maintenance,State space,Optimization problem,Mathematics,Maintenance actions,Reinforcement learning | Journal |
Volume | Issue | ISSN |
283 | 1 | 0377-2217 |
Citations | PageRank | References |
2 | 0.35 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu Liu | 1 | 190 | 19.09 |
| Yiming Chen | 2 | 187 | 22.75 |
| Tao Jiang | 3 | 211 | 44.26 |