Intelligent Decision-Making of Scheduling for Dynamic Permutation Flowshop via Deep Reinforcement Learning

Intelligent Decision-Making of Scheduling for Dynamic Permutation Flowshop via Deep Reinforcement Learning

Dynamic scheduling problems have been receiving increasing attention in recent years due to their practical implications. To realize real-time and the intelligent decision-making of dynamic scheduling, we studied dynamic permutation flowshop scheduling problem (PFSP) with new job arrival using deep...

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Bibliographic Details
Main Authors: Shengluo Yang, Zhigang Xu, Junyi Wang
Format: Article
Language:English
Published: MDPI AG 2021-02-01
Series:Sensors
Subjects:
permutation flowshop scheduling problem
deep reinforcement learning
actor-critic
dynamic scheduling
real-time scheduling
new job arrival
Online Access:https://www.mdpi.com/1424-8220/21/3/1019

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