@article{oai:ipsj.ixsq.nii.ac.jp:00210341,
 author = {Hiroshi, Suetsugu and Yoshiaki, Narusue and Hiroyuki, Morikawa and Hiroshi, Suetsugu and Yoshiaki, Narusue and Hiroyuki, Morikawa},
 issue = {2},
 journal = {情報処理学会論文誌数理モデル化と応用（TOM）},
 month = {Mar},
 note = {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 numerous types of joint-replenishment cost structures exist 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 policies. In this study, a multi-agent reinforcement learning-based solution for a joint replenishment problem is proposed, 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 equals or surpasses that of the existing policies, which are can-order, and modified periodic policies., 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 numerous types of joint-replenishment cost structures exist 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 policies. In this study, a multi-agent reinforcement learning-based solution for a joint replenishment problem is proposed, 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 equals or surpasses that of the existing policies, which are can-order, and modified periodic policies.},
 pages = {1--12},
 title = {Multi-agent Reinforcement Learning Based Approach for Periodic-review Joint Replenishment Problem under Practical Cost Structures},
 volume = {14},
 year = {2021}
}