A Multi-Agent Reinforcement Learning Method With Route Recorders for Vehicle Routing in Supply Chain Management

doi:10.1109/TITS.2022.3150151

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 平行智能技术与系统团队

	A Multi-Agent Reinforcement Learning Method With Route Recorders for Vehicle Routing in Supply Chain Management
	Ren, Lei 1,2; Fan, Xiaoyang 1,2; Cui, Jin 3,4; Shen, Zhen5 ; Lv, Yisheng5 ; Xiong, Gang6,7
发表期刊	IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
ISSN	1524-9050
	2022-02-15
页码	11
通讯作者	Cui, Jin(jincui@buaa.edu.cn)
摘要	In the modern supply chain system, large-scale transportation tasks require the collaborative work of multiple vehicles to be completed on time. Over the past few decades, multi-vehicle route planning was mainly implemented by heuristic algorithms. However, these algorithms face the dilemma of long computation time. In recent years, some machine learning-based methods are also proposed for vehicle route planning, but the existing algorithms can hardly solve multi-vehicle time-sensitive problems. To overcome this problem, we propose a novel multi-agent reinforcement learning model, which optimizes the route length and the vehicle's arrival time simultaneously. The model is based on the encoder-decoder framework. The encoder mines the relationship between the customer nodes in the problem, and the decoder generates the route of each vehicle iteratively. Specially, we design multiple route recorders to extract the route history information of vehicles and realize the communication between them. In the inferring phase, the model could immediately generate routes for all vehicles in a new instance. To further improve the performance of the model, we devise a multi-sampling strategy and obtain the balance boundary between computation time and performance improvement. In addition, we propose a simulation-based vehicle configuration method to select the optimal number of vehicles in real applications. For validation, we conduct a series of experiments on problems with different customer amounts and various vehicle numbers. The results show that the proposed model outperforms other typical algorithms in both performance and calculation time.
关键词	Reinforcement learning Costs Task analysis Transportation Optimization Computational modeling Vehicle routing Vehicle routing supply chain management multi-agent reinforcement learning (MARL) route recorder
DOI	10.1109/TITS.2022.3150151
关键词[WOS]	OPTIMIZATION ; ALGORITHM ; NUMBER
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2019YFB1705502]
项目资助者	National Key Research and Development Program of China
WOS研究方向	Engineering ; Transportation
WOS类目	Engineering, Civil ; Engineering, Electrical & Electronic ; Transportation Science & Technology
WOS记录号	WOS:000758740900001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：15[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/47907
专题	多模态人工智能系统全国重点实验室_平行智能技术与系统团队
通讯作者	Cui, Jin
作者单位	1.Beihang Univ, Sch Automat Sci & Elect Engn, Beijing 100191, Peoples R China 2.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Beijing 100191, Peoples R China 3.Beihang Univ, Res Inst Frontier Sci, Beijing 100191, Peoples R China 4.Beihang Univ, Ningbo Inst Technol, Ningbo 315800, Peoples R China 5.Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Inst Automat, Beijing 100190, Peoples R China 6.Chinese Acad Sci, Inst Automat, Beijing Engn Res Ctr Intelligent Syst & Technol, Beijing 100190, Peoples R China 7.Chinese Acad Sci, Cloud Comp Ctr, Dongguan 523808, Peoples R China
推荐引用方式 GB/T 7714	Ren, Lei,Fan, Xiaoyang,Cui, Jin,et al. A Multi-Agent Reinforcement Learning Method With Route Recorders for Vehicle Routing in Supply Chain Management[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,2022:11.
APA	Ren, Lei,Fan, Xiaoyang,Cui, Jin,Shen, Zhen,Lv, Yisheng,&Xiong, Gang.(2022).A Multi-Agent Reinforcement Learning Method With Route Recorders for Vehicle Routing in Supply Chain Management.IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,11.
MLA	Ren, Lei,et al."A Multi-Agent Reinforcement Learning Method With Route Recorders for Vehicle Routing in Supply Chain Management".IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2022):11.