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 …
Learning to dispatch for job shop scheduling via deep reinforcement learning
Priority dispatching rule (PDR) is widely used for solving real-world Job-shop scheduling
problem (JSSP). However, the design of effective PDRs is a tedious task, requiring a myriad …
problem (JSSP). However, the design of effective PDRs is a tedious task, requiring a myriad …
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 dual attention network-based reinforcement learning
Flexible manufacturing has given rise to complex scheduling problems such as the flexible
job shop scheduling problem (FJSP). In FJSP, operations can be processed on multiple …
job shop scheduling problem (FJSP). In FJSP, operations can be processed on multiple …
A deep reinforcement learning framework based on an attention mechanism and disjunctive graph embedding for the job-shop scheduling problem
R Chen, W Li, H Yang - IEEE Transactions on Industrial …, 2022 - ieeexplore.ieee.org
The job-shop scheduling problem (JSSP) is a classical NP-hard combinatorial optimization
problem, and the operating efficiency of manufacturing system is affected directly by the …
problem, and the operating efficiency of manufacturing system is affected directly by the …
Dynamic job-shop scheduling problems using graph neural network and deep reinforcement learning
CL Liu, TH Huang - IEEE Transactions on Systems, Man, and …, 2023 - ieeexplore.ieee.org
The job-shop scheduling problem (JSSP) is one of the best-known combinatorial
optimization problems and is also an essential task in various sectors. In most real-world …
optimization problems and is also an essential task in various sectors. In most real-world …
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 …
[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 …
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 …
A deep multi-agent reinforcement learning approach to solve dynamic job shop scheduling problem
Manufacturing industry is experiencing a revolution in the creation and utilization of data, the
abundance of industrial data creates a need for data-driven techniques to implement real …
abundance of industrial data creates a need for data-driven techniques to implement real …