A review of reinforcement learning based intelligent optimization for manufacturing scheduling
As the critical component of manufacturing systems, production scheduling aims to optimize
objectives in terms of profit, efficiency, and energy consumption by reasonably determining …
objectives in terms of profit, efficiency, and energy consumption by reasonably determining …
Reinforcement learning for predictive maintenance: A systematic technical review
The manufacturing world is subject to ever-increasing cost optimization pressures.
Maintenance adds to cost and disrupts production; optimized maintenance is therefore of …
Maintenance adds to cost and disrupts production; optimized maintenance is therefore of …
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
Abstract Systems and machines undergo various failure modes that result in machine health
degradation, so maintenance actions are required to restore them back to a state where they …
degradation, so maintenance actions are required to restore them back to a state where they …
Real-time scheduling for distributed permutation flowshops with dynamic job arrivals using deep reinforcement learning
S Yang, J Wang, Z Xu - Advanced Engineering Informatics, 2022 - Elsevier
Distributed manufacturing plays an important role for large-scale companies to reduce
production and transportation costs for globalized orders. However, how to real-timely and …
production and transportation costs for globalized orders. However, how to real-timely and …
Q-learning driven multi-population memetic algorithm for distributed three-stage assembly hybrid flow shop scheduling with flexible preventive maintenance
Y Jia, Q Yan, H Wang - Expert Systems with Applications, 2023 - Elsevier
The distributed assembly flow shop scheduling (DAFS) problem has received much
attention in the last decade, and a variety of metaheuristic algorithms have been developed …
attention in the last decade, and a variety of metaheuristic algorithms have been developed …
Transfer reinforcement learning method with multi-label learning for compound fault recognition
Z Wang, Q Zhang, L Tang, T Shi, J Xuan - Advanced Engineering …, 2023 - Elsevier
In complex working site, bearings used as the important part of machine, could
simultaneously have faults on several positions. Consequently, multi-label learning …
simultaneously have faults on several positions. Consequently, multi-label learning …
A data-driven methodology with a nonparametric reliability method for optimal condition-based maintenance strategies
M Mikhail, MS Ouali, S Yacout - Reliability Engineering & System Safety, 2024 - Elsevier
Optimizing condition-based maintenance (CBM) strategies based on machine learning (ML)
methods as reinforcement learning (RL) have been receiving increasing attention due to …
methods as reinforcement learning (RL) have been receiving increasing attention due to …
Deep reinforcement learning for distributed flow shop scheduling with flexible maintenance
Q Yan, W Wu, H Wang - Machines, 2022 - mdpi.com
A common situation arising in flow shops is that the job processing order must be the same
on each machine; this is referred to as a permutation flow shop scheduling problem …
on each machine; this is referred to as a permutation flow shop scheduling problem …
Autonomous driving at the handling limit using residual reinforcement learning
While driving a vehicle safely at its handling limit is essential in autonomous vehicles in
Level 5 autonomy, it is a very challenging task for current conventional methods. Therefore …
Level 5 autonomy, it is a very challenging task for current conventional methods. Therefore …
Parallel hyper heuristic algorithm based on reinforcement learning for the corridor allocation problem and parallel row ordering problem
Hyper heuristics is a relatively new optimisation algorithm. Numerous studies have reported
that hyper heuristics are well applied in combinatorial optimisation problems. As a classic …
that hyper heuristics are well applied in combinatorial optimisation problems. As a classic …