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 …
Deep reinforcement learning in production systems: a systematic literature review
Shortening product development cycles and fully customisable products pose major
challenges for production systems. These not only have to cope with an increased product …
challenges for production systems. These not only have to cope with an increased product …
A reinforcement learning based RMOEA/D for bi-objective fuzzy flexible job shop scheduling
The flexible job shop scheduling problem (FJSP) is significant for realistic manufacturing.
However, the job processing time usually is uncertain and changeable during …
However, the job processing time usually is uncertain and changeable during …
A multi-action deep reinforcement learning framework for flexible Job-shop scheduling problem
This paper presents an end-to-end deep reinforcement framework to automatically learn a
policy for solving a flexible Job-shop scheduling problem (FJSP) using a graph neural …
policy for solving a flexible Job-shop scheduling problem (FJSP) using a graph neural …
Flexible job-shop scheduling via graph neural network and deep reinforcement learning
Recently, deep reinforcement learning (DRL) has been applied to learn priority dispatching
rules (PDRs) for solving complex scheduling problems. However, the existing works face …
rules (PDRs) for solving complex scheduling problems. However, the existing works face …
Deep reinforcement learning for dynamic scheduling of a flexible job shop
The ability to handle unpredictable dynamic events is becoming more important in pursuing
agile and flexible production scheduling. At the same time, the cyber-physical convergence …
agile and flexible production scheduling. At the same time, the cyber-physical convergence …
Research on adaptive job shop scheduling problems based on dueling double DQN
BA Han, JJ Yang - Ieee Access, 2020 - ieeexplore.ieee.org
Traditional approaches for job shop scheduling problems are ill-suited to deal with complex
and changeable production environments due to their limited real-time responsiveness …
and changeable production environments due to their limited real-time responsiveness …
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 …
Dynamic multi-objective scheduling for flexible job shop by deep reinforcement learning
S Luo, L Zhang, Y Fan - Computers & Industrial Engineering, 2021 - Elsevier
In modern volatile and complex manufacturing environment, dynamic events such as new
job insertions and machine breakdowns may randomly occur at any time and different …
job insertions and machine breakdowns may randomly occur at any time and different …
Dynamic scheduling for flexible job shop using a deep reinforcement learning approach
Y Gui, D Tang, H Zhu, Y Zhang, Z Zhang - Computers & Industrial …, 2023 - Elsevier
Due to the influence of dynamic changes in the manufacturing environment, a single
dispatching rule (SDR) cannot consistently attain better results than other rules for dynamic …
dispatching rule (SDR) cannot consistently attain better results than other rules for dynamic …