Residual Scheduling: A New Reinforcement Learning Approach to Solving Job Shop Scheduling Problem

Residual Scheduling: A New Reinforcement Learning Approach to Solving Job Shop Scheduling Problem

Job-shop scheduling problem (JSP) is a mathematical optimization problem widely used in industries like manufacturing, and flexible JSP (FJSP) is also a common variant. Since they are NP-hard, it is intractable to find the optimal solution for all cases within reasonable times. Thus, it becomes impo...

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Manylion Llyfryddiaeth
Prif Awduron: Kuo-Hao Ho, Jui-Yu Cheng, Ji-Han Wu, Fan Chiang, Yen-Chi Chen, Yuan-Yu Wu, I-Chen Wu
Fformat: Erthygl
Iaith:English
Cyhoeddwyd: IEEE 2024-01-01
Cyfres:IEEE Access
Pynciau:
Deep reinforcement learning (DRL)
flexible job-shop scheduling problem (FJSP)
graph neural network (GNN)
job-shop scheduling problem (JSP)
Mynediad Ar-lein:https://ieeexplore.ieee.org/document/10413475/

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https://ieeexplore.ieee.org/document/10413475/

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