Inventory Management With Multisource Heterogeneous Information: Roles of Representation Learning and Information Fusion
研究了如何利用表示学习和信息融合策略处理多源异构信息，以改进库存管理中的订货决策，并通过真实数据案例验证了方法的有效性。
The prevalence of omnichannel marketing and sales enables firms to make ordering decisions based on multisource heterogeneous information from all the channels. This work extends the data-driven inventory management models with single-source or homogeneous information to those with multisource heterogeneous information. Representation learning and information fusion strategies are used to deal with the challenges of high dimensionality and heterogeneity of multisource heterogeneous information. Quantile regression convolutional attention neural networks with different structures embedding different information fusion strategies are developed to address the problems. Two cases with real-world data are studied, and the results show that the proposed methods using representation learning and/or information fusion strategies have (far) better performances than the existing methods without using these strategies. Moreover, some managerial insights into representation learning and information fusion, different from the practices of deep learning in computer vision and natural language processing, are provided for inventory management problems.