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

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

Published:2024-08-19 Issue: Volume: Page:1-51
ISSN:0020-7543
Container-title:International Journal of Production Research
language:en
Short-container-title:International Journal of Production Research

Author:

Rolf Benjamin¹,Beier Alexander²,Jackson Ilya³,Müller Marcel¹,Reggelin Tobias¹,Stuckenschmidt Heiner²,Lang Sebastian¹⁴^ORCID

Affiliation:

1. Institut für Logistik und Materialflusstechnik, Otto-von-Guericke-Universität Magdeburg, Magdeburg, Germany

2. Fakultät für Wirtschaftsinformatik und Mathematik, Universität Mannheim, Mannheim, Germany

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

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

Funder

Federal Ministry of Education and Research of Germany

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/00207543.2024.2390968

Reference272 articles.

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2. Three-stage algorithms for the large-scale dynamic vehicle routing problem with industry 4.0 approach

3. How Machine Learning Will Transform Supply Chain Management;Agrawal Narendra;Harvard Business Review,2024

4. Agrawal Rakesh Tomasz Imieliński and Arun Swami. 1993. “Mining Association Rules Between Sets of Items in Large Databases.” In Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data SIGMOD '93 207–216. New York NY USA: Association for Computing Machinery. https://doi.org/10.1145/170035.170072.

5. Agrawal Rakesh and Ramakrishnan Srikant. 1994. “Fast Algorithms for Mining Association Rules in Large Databases.” In Proceedings of the 20th International Conference on Very Large Data Bases VLDB '94 487–499. San Francisco CA USA: Morgan Kaufmann Publishers Inc.