主讲人：Youhua (Frank) Chen教授 香港城市大学管理科学系讲座教授及系主任
Many retailers regularly introduce new, short life-cycle products. Unlike existing products whose historical sales data may be an indicator of future sales, a new product does not have such data. Instead, a firm may have been selling similar products in the past and keeps a good record of them. In addition to demand/sales figures, the data record may contain rich information about the attributes (features) of the products, such as retail price, design style, and season, the so-called covariate information to demand. In this project we attempt to link a new product, by using covariate information, to “similar” products that were sold historically. Weights are used to measure similarities between the new product and historical products, and the values of those weights are estimated by employing machine learning methods such as k-nearest neighbours, classification and regression tree, and random forests, to the data. Then, the pair of the realized demand of a similar historical product and its associated weight, together with those from other similar products, are utilised to approximate the expected profit and other quantities which take on the (conditional) demand distribution. This approach is applied to determine the optimal order quantities before a risk-averse firm launches a new product. Risk aversion requires the firm to attain a profit target with high confidence, which can be formulated as a value-at-risk (VaR) constraint. Besides devising efficient solutions, we also prove the proposed approximation to be asymptotically optimal even with the sample-dependent approximation for the VaR constraint. We will also use real-world data to verify our models and methods and present key managerial insights.
Youhua (Frank) Chen，多伦多大学博士，现任香港城市大学管理科学系讲座教授及系主任。在2012年加入香港城市大学之前，Youhua (Frank) Chen教授曾在新加坡国立大学商学院（1997-2001）和香港中文大学系统工程与工程管理系（2001-2012）任职。Youhua (Frank) Chen教授的研究兴趣包括共享经济、医疗健康管理、供应链建模和库存系统分析，在OR、MS、POM、M&SOM、NRL等运作管理领域国际顶级期刊发表多篇学术论文，例如代表作“Quantifying the bullwhip effect in a simple supply chain: The impact of forecasting, lead times, and information”发表后已经被引用2200余篇次，在供应链管理领域名列前茅。