適応正則化回帰による多段階サプライチェーンモデルの安定化

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2017/10/01

タイトル(英語): Improving The Robustness of A Multi-stage Supply Chain Model Through The Adaptive Regularization of A Demand Predictor

著者名: 齊藤　史哲（青山学院大学理工学部）

著者名(英語): Fumiaki Saitoh (Department of Industrial and Systems Engineering, College of Science and Engineering, Aoyama Gakuin University)

キーワード: サプライチェーン，オンライン学習，適応正則化回帰，在庫シミュレーション，需要予測　　supply chain，online learning，adaptive regularization of the weight vector，inventory simulation，demand prediction

要約(英語): The bullwhip effect is a phenomenon wherein demand fluctuations increase upstream in a supply chain. It can be to reduced through information sharing and demand forecasting. In research on the bullwhip effect for multi-stage supply chain simulations, an approach recommending the use of demand forecast models has been proposed. Since forecast models used in previous studies have been batch learning, they are useful only in situations where sufficient data has already been accumulated. Therefore, it is difficult to apply the batch learning model to a supply chain for which adequate past transaction data is unavailable. In this study, we apply an online learning model to the demand forecaster for a multi-stage supply chain simulation model. We have adopted adaptive regularization of the weight vector as the estimation algorithm for the demand forecaster. Since the proposed model is more powerful than a general online learning algorithm, from the point of view of generalization performance and convergence speed, the proposed method is promising in supply chain simulations. The effectiveness of our approach is confirmed, through computer experiments using the multi-stage supply chain model.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.137 No.10 （2017）

本誌掲載ページ: 1393-1401 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/137/10/137_1393/_article/-char/ja/