Q-learning in a competitive supply chain

T. Tongeren, van, U. Kaymak, D. Naso, E. Asperen, van

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

8 Citations (Scopus)
3 Downloads (Pure)

Abstract

The participants in a competitive supply chain take their decisions individually in a distributed environment and independent of one another. At the same time, they must coordinate their actions so that the total profitability of the supply chain is safeguarded. This decision problem is known to be a difficult one and the decisions at different stages of the supply chain may lead to large oscillations if not coordinated properly. In this paper, we consider reinforcement learning agents in a multi-echelon supply chain and study under which conditions they are able to manage the supply chain. Q-learning in the well-known beer game is used as a case. It is found that the reinforcement learning agents can learn better policies than humans, although they do not always converge to the optimal policy.
Original languageEnglish
Title of host publicationIEEE International Conference on Systems, Man and Cybernetics (ISIC), 7-10 October 2007, Montreal
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Pages1211-1216
ISBN (Print)978-1-4244-0991-4
DOIs
Publication statusPublished - 2007

Fingerprint Dive into the research topics of 'Q-learning in a competitive supply chain'. Together they form a unique fingerprint.

Cite this