Artificial intelligence to solve production scheduling problems in real industrial settings: Systematic Literature Review
This literature review examines the increasing use of artificial intelligence (AI) in
manufacturing systems, in line with the principles of Industry 4.0 and the growth of smart …
manufacturing systems, in line with the principles of Industry 4.0 and the growth of smart …
Design patterns of deep reinforcement learning models for job shop scheduling problems
S Wang, J Li, Q Jiao, F Ma - Journal of Intelligent Manufacturing, 2024 - Springer
Production scheduling has a significant role when optimizing production objectives such as
production efficiency, resource utilization, cost control, energy-saving, and emission …
production efficiency, resource utilization, cost control, energy-saving, and emission …
Research on flexible job shop scheduling problem with AGV using double DQN
M Yuan, L Zheng, H Huang, K Zhou, F Pei… - Journal of Intelligent …, 2025 - Springer
In the context of Industry 4.0 and intelligent manufacturing, AGVs are widely used in flexible
job shop resource transportation, which sharply increases the uncertainty and complexity of …
job shop resource transportation, which sharply increases the uncertainty and complexity of …
A spatial pyramid pooling-based deep reinforcement learning model for dynamic job-shop scheduling problem
X Wu, X Yan - Computers & Operations Research, 2023 - Elsevier
The dynamic job-shop scheduling problem (DJSP) is a typical of scheduling tasks where
rescheduling is performed when encountering unexpected events such as random job …
rescheduling is performed when encountering unexpected events such as random job …
A deep reinforcement learning model for dynamic job-shop scheduling problem with uncertain processing time
The dynamic job-shop scheduling problem (DJSP) is a type of scheduling tasks where
rescheduling is performed when encountering the uncertainties such as the uncertain …
rescheduling is performed when encountering the uncertainties such as the uncertain …
[HTML][HTML] Deep reinforcement learning-based dynamic scheduling for resilient and sustainable manufacturing: A systematic review
Dynamic scheduling plays a pivotal role in smart manufacturing by enabling real-time
adjustments to production schedules, thereby enhancing system resilience and promoting …
adjustments to production schedules, thereby enhancing system resilience and promoting …
Probabilistic Automata-Based Method for Enhancing Performance of Deep Reinforcement Learning Systems
Deep reinforcement learning (DRL) has demonstrated significant potential in industrial
manufacturing domains such as workshop scheduling and energy system management …
manufacturing domains such as workshop scheduling and energy system management …
A literature review of reinforcement learning methods applied to job-shop scheduling problems
X Zhang, GY Zhu - Computers & Operations Research, 2024 - Elsevier
The job-shop scheduling problem (JSP) is one of the most famous production scheduling
problems, and it is an NP-hard problem. Reinforcement learning (RL), a machine learning …
problems, and it is an NP-hard problem. Reinforcement learning (RL), a machine learning …
[HTML][HTML] Solving flexible job-shop scheduling problem with heterogeneous graph neural network based on relation and deep reinforcement learning
H Tang, J Dong - Machines, 2024 - mdpi.com
Driven by the rise of intelligent manufacturing and Industry 4.0, the manufacturing industry
faces significant challenges in adapting to flexible and efficient production methods. This …
faces significant challenges in adapting to flexible and efficient production methods. This …
Enhancing manufacturing excellence with Lean Six Sigma and zero defects based on Industry 4.0.
Improving quality, enhancing productivity, redesigning machining tools, eliminating waste in
production, and shortening lead time are all objectives aimed at improving customer …
production, and shortening lead time are all objectives aimed at improving customer …