Intelligent scheduling and reconfiguration via deep reinforcement learning in smart manufacturing

2021 ◽  
pp. 1-18
Author(s):  
Shengluo Yang ◽  
Zhigang Xu
Keyword(s):  
Reinforcement Learning ◽  
Smart Manufacturing ◽  
Intelligent Scheduling

Intelligent scheduling of discrete automated production line via deep reinforcement learning

2020 ◽  
Vol 58 (11) ◽  
pp. 3362-3380 ◽  
Author(s):  
Daming Shi ◽  
Wenhui Fan ◽  
Yingying Xiao ◽  
Tingyu Lin ◽  
Chi Xing
Keyword(s):  
Reinforcement Learning ◽  
Production Line ◽  
Automated Production ◽  
Intelligent Scheduling

scholarly journals Self-repair of smart manufacturing systems by deep reinforcement learning

CIRP Annals ◽  
2020 ◽  
Vol 69 (1) ◽  
pp. 421-424 ◽  
Author(s):  
Bogdan I. Epureanu ◽  
Xingyu Li ◽  
Aydin Nassehi ◽  
Yoram Koren
Keyword(s):  
Reinforcement Learning ◽  
Manufacturing Systems ◽  
Smart Manufacturing ◽  
Self Repair ◽  
Smart Manufacturing Systems

Intelligent scheduling using a neural network model in conjunction with reinforcement learning

2005 ◽  
Vol 219 (2) ◽  
pp. 229-235
Author(s):  
C J Fourie
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Reinforcement Learning ◽  
Learning From Experience ◽  
Scheduling System ◽  
Simulated Environment ◽  
The Neural Network ◽  
Learning Techniques ◽  
Proposed Model ◽  
Intelligent Scheduling

This paper describes the use of an artificial neural network in conjunction with reinforcement learning techniques to develop an intelligent scheduling system that is capable of learning from experience. In a simulated environment the model controls a mobile robot that transports material to machines. States of ‘happiness’ are defined for each machine, which are the inputs to the neural network. The output of the neural network is the decision on which machine to service next. After every decision, a critic evaluates the decision and a teacher ‘rewards’ the network to encourage good decisions and discourage bad decisions. From the results obtained, it is concluded that the proposed model is capable of learning from past experience and thereby improving the intelligence of the system.

Dynamic job-shop scheduling in smart manufacturing using deep reinforcement learning

2021 ◽  
Vol 190 ◽  
pp. 107969
Author(s):  
Libing Wang ◽  
Xin Hu ◽  
Yin Wang ◽  
Sujie Xu ◽  
Shijun Ma ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Smart Manufacturing ◽  
Shop Scheduling ◽  
Dynamic Job Shop
Sign in / Sign up

Export Citation Format

Share Document