Knowledge-based reinforcement learning and estimation of distribution algorithm for flexible job shop scheduling problem
Y Du, J Li, X Chen, P Duan… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Inthis study, a flexible job shop scheduling problem with time-of-use electricity price
constraint is considered. The problem includes machine processing speed, setup time, idle …
constraint is considered. The problem includes machine processing speed, setup time, idle …
A reinforcement learning approach for flexible job shop scheduling problem with crane transportation and setup times
Y Du, J Li, C Li, P Duan - IEEE Transactions on Neural …, 2022 - ieeexplore.ieee.org
Flexible job shop scheduling problem (FJSP) has attracted research interests as it can
significantly improve the energy, cost, and time efficiency of production. As one type of …
significantly improve the energy, cost, and time efficiency of production. As one type of …
[PDF][PDF] A deep reinforcement learning based solution for flexible job shop scheduling problem
B Han, J Yang - International Journal of Simulation Modelling, 2021 - researchgate.net
Flexible job shop Scheduling problem (FJSP) is a classic problem in combinatorial
optimization and a very common form of organization in a real production environment …
optimization and a very common form of organization in a real production environment …
A self-learning genetic algorithm based on reinforcement learning for flexible job-shop scheduling problem
R Chen, B Yang, S Li, S Wang - Computers & industrial engineering, 2020 - Elsevier
As an important branch of production scheduling, flexible job-shop scheduling problem
(FJSP) is difficult to solve and is proven to be NP-hard. Many intelligent algorithms have …
(FJSP) is difficult to solve and is proven to be NP-hard. Many intelligent algorithms have …
Multi-objective reinforcement learning framework for dynamic flexible job shop scheduling problem with uncertain events
H Wang, J Cheng, C Liu, Y Zhang, S Hu, L Chen - Applied Soft Computing, 2022 - Elsevier
The economic benefits for manufacturing companies will be influenced by how it handles
potential dynamic events and performs multi-objective real-time scheduling for existing …
potential dynamic events and performs multi-objective real-time scheduling for existing …
Deep reinforcement learning for dynamic flexible job shop scheduling with random job arrival
J Chang, D Yu, Y Hu, W He, H Yu - Processes, 2022 - mdpi.com
The production process of a smart factory is complex and dynamic. As the core of
manufacturing management, the research into the flexible job shop scheduling problem …
manufacturing management, the research into the flexible job shop scheduling problem …
Deep reinforcement learning for dynamic flexible job shop scheduling problem considering variable processing times
L Zhang, Y Feng, Q Xiao, Y Xu, D Li, D Yang… - Journal of Manufacturing …, 2023 - Elsevier
In recent years, the uncertainties and complexity in the production process, due to the
boosted customized requirements, has dramatically increased the difficulties of Dynamic …
boosted customized requirements, has dramatically increased the difficulties of Dynamic …
Dynamic opposite learning enhanced dragonfly algorithm for solving large-scale flexible job shop scheduling problem
D Yang, M Wu, D Li, Y Xu, X Zhou, Z Yang - Knowledge-Based Systems, 2022 - Elsevier
Flexible job shop scheduling problem (FJSP) has attracted many research interests, in
particular for meta-heuristic algorithm (MA) developers due to the superior optimization …
particular for meta-heuristic algorithm (MA) developers due to the superior optimization …
Solving flexible job shop scheduling problems via deep reinforcement learning
Flexible job shop scheduling problem (FJSSP), as a variant of the job shop scheduling
problem, has a larger solution space. Researchers are always looking for good methods to …
problem, has a larger solution space. Researchers are always looking for good methods to …
Real-time data-driven dynamic scheduling for flexible job shop with insufficient transportation resources using hybrid deep Q network
Y Li, W Gu, M Yuan, Y Tang - Robotics and Computer-Integrated …, 2022 - Elsevier
With the extensive application of automated guided vehicles in manufacturing system,
production scheduling considering limited transportation resources becomes a difficult …
production scheduling considering limited transportation resources becomes a difficult …
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