Multi-Agent Reinforcement Learning for Extended Flexible Job Shop Scheduling

doi:10.3390/machines12010008

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	Multi-Agent Reinforcement Learning for Extended Flexible Job Shop Scheduling
	Peng, Shaoming 1,2; Xiong, Gang 2,3,4; Yang, Jing 1,2; Shen, Zhen 2,5; Tamir, Tariku Sinshaw 6,7; Tao, Zhikun 1,2; Han, Yunjun 2,3; Wang, Fei-Yue 8
刊名	MACHINES
	2024
卷号	12 期号:1 页码:25
关键词	production planning and scheduling multi-agent reinforcement learning flexible job shop path flexibility technological flexibility
DOI	10.3390/machines12010008
通讯作者	Han, Yunjun(yunjun.han@ia.ac.cn)
英文摘要	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 manufacturing, and multiple aircraft maintenance. However, optimizing the scheduling puts forward higher requirements on accuracy, real time, and generalization, while subject to the curse of dimension and usually incomplete information. Various coupling relations among operations, stations, and resources aggravate the problem. To deal with the above challenges, we propose a multi-agent reinforcement learning algorithm where the scheduling environment is modeled as a decentralized partially observable Markov decision process. Each job is regarded as an agent that decides the next triplet, i.e., operation, station, and employed resource. This paper is novel in addressing the flexible job shop scheduling problem with dual flexibility and varied transportation time in consideration and proposing a double Q-value mixing (DQMIX) optimization algorithm under a multi-agent reinforcement learning framework. The experiments of our case study show that the DQMIX algorithm outperforms existing multi-agent reinforcement learning algorithms in terms of solution accuracy, stability, and generalization. In addition, it achieves better solution quality for larger-scale cases than traditional intelligent optimization algorithms.
资助项目	National Key Research and Development Program of China
WOS关键词	GENETIC ALGORITHM
WOS研究方向	Engineering
语种	英语
出版者	MDPI
WOS记录号	WOS:001151486500001
资助机构	National Key Research and Development Program of China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/55346]
专题	多模态人工智能系统全国重点实验室
通讯作者	Han, Yunjun
作者单位	1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China 3.Chinese Acad Sci, Inst Automat, Beijing Engn Res Ctr Intelligent Syst & Technol, Beijing 100190, Peoples R China 4.Chinese Acad Sci, Cloud Comp Ctr, Guangdong Engn Res Ctr Printing & Intelligent Mfg, Dongguan 523808, Peoples R China 5.Qingdao Acad Intelligent Ind, Intelligent Mfg Ctr, Qingdao 266109, Peoples R China 6.Guangdong Univ Technol, State Key Lab Precis Elect Mfg Technol & Equipment, Guangzhou 510006, Peoples R China 7.Debremarkos Univ, Inst Technol, Sch Elect & Comp Engn, Debremarkos 269, Ethiopia 8.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Peng, Shaoming,Xiong, Gang,Yang, Jing,et al. Multi-Agent Reinforcement Learning for Extended Flexible Job Shop Scheduling[J]. MACHINES,2024,12(1):25.
APA	Peng, Shaoming.,Xiong, Gang.,Yang, Jing.,Shen, Zhen.,Tamir, Tariku Sinshaw.,...&Wang, Fei-Yue.(2024).Multi-Agent Reinforcement Learning for Extended Flexible Job Shop Scheduling.MACHINES,12(1),25.
MLA	Peng, Shaoming,et al."Multi-Agent Reinforcement Learning for Extended Flexible Job Shop Scheduling".MACHINES 12.1(2024):25.