A Dynamic Scheduling Method Combining Iterative Optimization and Deep Reinforcement Learning to Solve Sudden Disturbance Events in a Flexible Manufacturing Process

A Dynamic Scheduling Method Combining Iterative Optimization and Deep Reinforcement Learning to Solve Sudden Disturbance Events in a Flexible Manufacturing Process

Unpredictable sudden disturbances such as machine failure, processing time lag, and order changes increase the deviation between actual production and the planned schedule, seriously affecting production efficiency. This phenomenon is particularly severe in flexible manufacturing. In this paper, a d...

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Bibliographic Details
Main Authors: Jun Yan, Tianzuo Zhao, Tao Zhang, Hongyan Chu, Congbin Yang, Yueze Zhang
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Mathematics
Subjects:
dynamic scheduling
deep reinforcement learning
flexible job shop
double deep Q-network
rescheduling
Online Access:https://www.mdpi.com/2227-7390/13/1/4
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