Learning to Solve Multiple-TSP With Time Window and Rejections via Deep Reinforcement Learning-Reference-Cited by-同舟云学术

Learning to Solve Multiple-TSP With Time Window and Rejections via Deep Reinforcement Learning

Author:

Zhang Rongkai¹^ORCID,Zhang Cong²,Cao Zhiguang³^ORCID,Song Wen⁴^ORCID,Tan Puay Siew³,Zhang Jie²,Wen Bihan¹^ORCID,Dauwels Justin⁵

Affiliation:

1. School of Electrical and Electronic Engineering, Nanyang Technological University, Jurong West, Singapore

2. School of Computer Science and Engineering, Nanyang Technological University, Jurong West, Singapore

3. Singapore Institute of Manufacturing Technology (SIMTech), Agency for Science Technology and Research (A*STAR), Fusionopolis, Singapore

4. Institute of Marine Science and Technology, Shandong University, Qingdao, China

5. Department of Microelectronics, Faculty EEMCS, Technische Universiteit Delft, Delft, The Netherlands

Funder

Agency for Science Technology and Research Career Development Fund

IEO Decentralized GAP Project of Continuous Last-Mile Logistics (CLML) at the Singapore Institute of Manufacturing Technology

Rapid-Rich Object Search (ROSE) Laboratory, Nanyang Technological University, Singapore

Ministry of Education, Singapore, under its Academic Research Fund Tier 1

Start-Up Grant

National Natural Science Foundation of China

Shandong Provincial Natural Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Mechanical Engineering,Automotive Engineering

Link

Reference36 articles.

1. Reinforcement learning for solving the vehicle routing problem;nazari;Proc Adv Neural Inf Process Syst,2018

3. Pointer networks;vinyals;Proc Adv Neural Inf Process Syst,2015

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