An intelligent open trading system for on-demand delivery facilitated by deep Q network based reinforcement learning-Reference-Cited by-同舟云学术

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An intelligent open trading system for on-demand delivery facilitated by deep Q network based reinforcement learning

Published:2024-06-14 Issue: Volume: Page:1-23
ISSN:0020-7543
Container-title:International Journal of Production Research
language:en
Short-container-title:International Journal of Production Research

Author:

Guo Chaojie¹,Zhang Lele²³^ORCID,Thompson Russell G.¹,Foliente Greg¹,Peng Xiaoshuai⁴

Affiliation:

1. Department of Infrastructure Engineering, The University of Melbourne, Australia

2. School of Mathematics and Statistics, The University of Melbourne, Australia

3. ARC Training Centre in Optimisation Technologies, Integrated Methodologies, and Applications (OPTIMA), The University of Melbourne, Australia

4. School of Management, Lanzhou University, People’s Republic of China

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/00207543.2024.2364349

Reference57 articles.

1. Accenture Interactive. 2015. “The Next Generation of Commerce. Accenture.” In. https://tinyurl.com/y7wnk87e.

2. Validation of an agent based model using a participatory simulation gaming approach: The case of city logistics

3. Anand Nilesh Ron Van Duin and Lori Tavasszy. 2010. “Multi Agent Modeling for City Logistics Policy Analysis: Potentials and Challenges.” Paper presented at the Proceedings of The 45th Annual Conference Transportation and Logistics Trends and Policies: Successes and Failures.

4. Ontology-based multi-agent system for urban freight transportation

5. A comparative study of urban freight transport planning: addressing stakeholder needs

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