A review on reinforcement learning algorithms and applications in supply chain management-Reference-Cited by-同舟云学术

A review on reinforcement learning algorithms and applications in supply chain management

Published:2022-11-03 Issue: Volume: Page:1-29
ISSN:0020-7543
Container-title:International Journal of Production Research
language:en
Short-container-title:International Journal of Production Research

Author:

Rolf Benjamin¹^ORCID,Jackson Ilya²^ORCID,Müller Marcel¹^ORCID,Lang Sebastian³^ORCID,Reggelin Tobias¹^ORCID,Ivanov Dmitry⁴

Affiliation:

1. Otto-von-Guericke-University Magdeburg, Magdeburg, Germany

2. Massachusetts Institute of Technology, Center for Transportation & Logistics, Cambridge, MA, USA

3. Fraunhofer Institute for Factory Operation and Automation IFF, Magdeburg, Germany

4. Berlin School of Economics and Law, Berlin, Germany

Publisher

Informa UK Limited

Subject

Industrial and Manufacturing Engineering,Management Science and Operations Research,Strategy and Management

Link

https://www.tandfonline.com/doi/pdf/10.1080/00207543.2022.2140221

Reference189 articles.

1. A reinforcement learning-based framework for disruption risk identification in supply chains

2. Achiam, J. 2018. “OpenAI Spinning up. GitHub Repository.” Accessed 16 February 2022. https://spinningup.openai.com/.

3. Interterminal Truck Routing Optimization Using Cooperative Multiagent Deep Reinforcement Learning

4. Adi, T. N., Y. A. Iskandar, H. Bae, and Y. Choi. 2020. “Reduction of Number of Empty-Truck Trips in Inter-Terminal Transportation Using Multi-agent Q-learning.” In Interconnected Supply Chains in An Era of Innovation – Proceedings of the 8th International Conference on Information Systems, Logistics and Supply Chain, ILS 2020, Austin, TX, USA, 167–172.

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