Multi-Agent Reinforcement Learning for Extended Flexible Job Shop Scheduling

Multi-Agent Reinforcement Learning for Extended Flexible Job Shop Scheduling

An extended flexible job scheduling problem is presented with characteristics of technology and path flexibility (dual flexibility), varied transportation time, and an uncertain environment. The scheduling can greatly increase efficiency and security in complex scenarios, e.g., distributed vehicle m...

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Bibliographic Details
Main Authors: Shaoming Peng, Gang Xiong, Jing Yang, Zhen Shen, Tariku Sinshaw Tamir, Zhikun Tao, Yunjun Han, Fei-Yue Wang
Format: Article
Language:English
Published: MDPI AG 2023-12-01
Series:Machines
Subjects:
production planning and scheduling
multi-agent reinforcement learning
flexible job shop
path flexibility
technological flexibility
Online Access:https://www.mdpi.com/2075-1702/12/1/8

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https://www.mdpi.com/2075-1702/12/1/8

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