A data mining perspective of the newsvendor problem

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Abstract

This study examined a modified newsvendor problem. The modified newsvendor problem concerned with how wholesalers can achieve maximum profits. Instead of building a profit function for the wholesalers and finding the optimal solutions as previous studies would do, this study proposed a novel, data mining approach to address the problem. Specifically, the modified newsvendor problem were transformed into a classification problem. According to a set of relevant attributes, we used the regularized multiple criteria linear programming (RMCLP) model to classify dealers into two categories, namely Type A and Type B, according to the associated order quantity of dealers. Experiments showed that the RMCLP model gave high accuracy in predicting to which type a dealer belong to. One important implication of this study is to provide insights into the design and development of supply chain coordination policy. © 2013 The Authors. Published by Elsevier B.V.

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Yu, X., Qi, Z., & Zhao, Y. (2013). A data mining perspective of the newsvendor problem. In Procedia Computer Science (Vol. 17, pp. 166–172). Elsevier B.V. https://doi.org/10.1016/j.procs.2013.05.023

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