An adaptive inventory control model for a supply chain with nonstationary customer demands

Jun Geol Back, Chang Ouk Kim, Ick Hyun Kwon

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In this paper, we propose an adaptive inventory control model for a supply chain consisting of one supplier and multiple retailers with nonstationary customer demands. The objective of the adaptive inventory control model is to minimize inventory related cost. The inventory control parameter is safety lead time. Unlike most extant inventory control approaches, modeling the uncertainty of customer demand as a statistical distribution is not a prerequisite in this model. Instead, using a reinforcement learning technique called action-reward based learning, the control parameter is designed to adaptively change as customer demand pattern changes. A simulation based experiment was performed to compare the performance of the adaptive inventory control model.

Original languageEnglish
Title of host publicationPRICAI 2006
Subtitle of host publicationTrends in Artificial Intelligence - 9th Pacific Rim International Conference on Artificial Intelligence, Proceedings
PublisherSpringer Verlag
Pages895-900
Number of pages6
ISBN (Print)3540366679, 9783540366676
DOIs
Publication statusPublished - 2006
Event9th Pacific Rim International Conference on Artificial Intelligence - Guilin, China
Duration: 2006 Aug 72006 Aug 11

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4099 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other9th Pacific Rim International Conference on Artificial Intelligence
CountryChina
CityGuilin
Period06/8/706/8/11

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Back, J. G., Kim, C. O., & Kwon, I. H. (2006). An adaptive inventory control model for a supply chain with nonstationary customer demands. In PRICAI 2006: Trends in Artificial Intelligence - 9th Pacific Rim International Conference on Artificial Intelligence, Proceedings (pp. 895-900). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4099 LNAI). Springer Verlag. https://doi.org/10.1007/11801603_102