A Multi-Agent Reinforcement Learning Method With Route Recorders for Vehicle Routing in Supply Chain Management
文献类型：期刊论文
| 作者 | Ren, Lei6,7; Fan, Xiaoyang6,7; Cui, Jin4,5; Shen, Zhen3 ; Lv, Yisheng3; Xiong, Gang1,2
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| 刊名 | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
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| 出版日期 | 2022-02-15 |
| 页码 | 11 |
| 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 |
| ISSN号 | 1524-9050 |
| DOI | 10.1109/TITS.2022.3150151 |
| 通讯作者 | Cui, Jin(jincui@buaa.edu.cn) |
| 英文摘要 | 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. |
| WOS关键词 | OPTIMIZATION ; ALGORITHM ; NUMBER |
| 资助项目 | National Key Research and Development Program of China[2019YFB1705502] |
| WOS研究方向 | Engineering ; Transportation |
| 语种 | 英语 |
| WOS记录号 | WOS:000758740900001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Key Research and Development Program of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/47906]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队 |
| 通讯作者 | Cui, Jin |
| 作者单位 | 1.Chinese Acad Sci, Cloud Comp Ctr, Dongguan 523808, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing Engn Res Ctr Intelligent Syst & Technol, Beijing 100190, Peoples R China 3.Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Inst Automat, Beijing 100190, Peoples R China 4.Beihang Univ, Ningbo Inst Technol, Ningbo 315800, Peoples R China 5.Beihang Univ, Res Inst Frontier Sci, Beijing 100191, Peoples R China 6.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Beijing 100191, Peoples R China 7.Beihang Univ, Sch Automat Sci & Elect Engn, Beijing 100191, 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. |
入库方式： OAI收割
来源：自动化研究所
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