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  1. 研究報告
  2. 数理モデル化と問題解決(MPS)
  3. 2020
  4. 2020-MPS-130

Periodic-Review Joint Replenishment Policy using Multi-Agent Reinforcement Learning

https://ipsj.ixsq.nii.ac.jp/records/206875
https://ipsj.ixsq.nii.ac.jp/records/206875
e9b00961-1ae8-427f-a58b-29db7bedada5
名前 / ファイル ライセンス アクション
IPSJ-MPS20130001.pdf IPSJ-MPS20130001.pdf (839.6 kB)
Copyright (c) 2020 by the Information Processing Society of Japan
オープンアクセス
Item type SIG Technical Reports(1)
公開日 2020-09-22
タイトル
タイトル Periodic-Review Joint Replenishment Policy using Multi-Agent Reinforcement Learning
タイトル
言語 en
タイトル Periodic-Review Joint Replenishment Policy using Multi-Agent Reinforcement Learning
言語
言語 eng
資源タイプ
資源タイプ識別子 http://purl.org/coar/resource_type/c_18gh
資源タイプ technical report
著者所属
Graduate School of Engineering, The University of Tokyo
著者所属
Graduate School of Engineering, The University of Tokyo
著者所属
Graduate School of Engineering, The University of Tokyo
著者所属(英)
en
Graduate School of Engineering, The University of Tokyo
著者所属(英)
en
Graduate School of Engineering, The University of Tokyo
著者所属(英)
en
Graduate School of Engineering, The University of Tokyo
著者名 Hiroshi, Suetsugu

× Hiroshi, Suetsugu

Hiroshi, Suetsugu

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Yoshiaki, Narusue

× Yoshiaki, Narusue

Yoshiaki, Narusue

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Hiroyuki, Morikawa

× Hiroyuki, Morikawa

Hiroyuki, Morikawa

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著者名(英) Hiroshi, Suetsugu

× Hiroshi, Suetsugu

en Hiroshi, Suetsugu

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Yoshiaki, Narusue

× Yoshiaki, Narusue

en Yoshiaki, Narusue

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Hiroyuki, Morikawa

× Hiroyuki, Morikawa

en Hiroyuki, Morikawa

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論文抄録
内容記述タイプ Other
内容記述 A periodic-review joint replenishment problem is considered. In literature, can-order and modified periodic-review policies have been proposed, and either of them cannot always outperform the other depending on the demand characteristics. In addition, whereas there are many types of joint-replenishment cost structures in practical settings, most studies have assumed the fixed joint-replenishment costs, and, for the periodic-review system, no study has been conducted to incorporate the practical cost structures into the existing heuristic approach. The purpose of this paper is to propose a multi-agent reinforcement learning-based solution for a joint replenishment problem, which can be used for problems with several demand settings, and be applied for various cost structures with minor modification. Our numerical experiments demonstrate that the performance of our proposed agent is equal or greater than that of the existing heuristic policies, that are can-order, and modified periodic policies.
論文抄録(英)
内容記述タイプ Other
内容記述 A periodic-review joint replenishment problem is considered. In literature, can-order and modified periodic-review policies have been proposed, and either of them cannot always outperform the other depending on the demand characteristics. In addition, whereas there are many types of joint-replenishment cost structures in practical settings, most studies have assumed the fixed joint-replenishment costs, and, for the periodic-review system, no study has been conducted to incorporate the practical cost structures into the existing heuristic approach. The purpose of this paper is to propose a multi-agent reinforcement learning-based solution for a joint replenishment problem, which can be used for problems with several demand settings, and be applied for various cost structures with minor modification. Our numerical experiments demonstrate that the performance of our proposed agent is equal or greater than that of the existing heuristic policies, that are can-order, and modified periodic policies.
書誌レコードID
収録物識別子タイプ NCID
収録物識別子 AN10505667
書誌情報 研究報告数理モデル化と問題解決(MPS)

巻 2020-MPS-130, 号 1, p. 1-6, 発行日 2020-09-22
ISSN
収録物識別子タイプ ISSN
収録物識別子 2188-8833
Notice
SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc.
出版者
言語 ja
出版者 情報処理学会
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Hiroshi, Suetsugu, Yoshiaki, Narusue, Hiroyuki, Morikawa, 2020: 情報処理学会, 1–6 p.

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