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This paper aims to analyze the green efficiency performance of the logistics industry in China's 30 provinces from 2008 to 2017. We first evaluate the green efficiency of the logistics industry through the non-directional distance function method. Then, we use the functional clustering method funHDDC, which is one of the popular machine learning methods, to divide 30 provinces into 4 clusters and analyze the similarities and differences in green efficiency performance patterns among different groups. Further, we explore the driving factors of dynamic changes in green efficiency through the decomposition method. The main conclusions of this paper are as follows: (1) in general, the level of green efficiency is closely related to the geographical location. From the clustering results, we can find that most of the eastern regions belong to the cluster with higher green efficiency, while most of the western regions belong to the cluster with lower green efficiency. However, the green efficiency performance in several regions with high economic levels, such as Beijing and Shanghai, is not satisfactory. (2) Based on the analysis of decomposition results, the innovation effect of China's logistics industry is the most obvious, but the efficiency change still needs to be improved, and technical leadership should be strengthened. Based on these conclusions, we further propose some policy recommendations for the green development of the logistics industry in China. [ABSTRACT FROM AUTHOR]

This paper discusses an allocation issue of promotion budget and traffic volume, faced by VIP.com that is the largest online flash-sales platform in China. Through flash sales mode, each day VIP.com provides consumers one hundred authorized brands of products on consignment, which last for a short period of time (frequently 3–5 days), called by VIP.com 'Dangqi'. As a consignee, VIP.com has no pricing rights of products but can allocate the promotion budget and traffic volume to each brand. Due to different categories of products and different brands of products having different responses to the same promotion budget and different profit margins, it is very necessary for VIP.com to allocate promotion budget and traffic volume among all brands of products offered in each 'Dangqi' reasonably in order to improve the usage efficiency of limited resources. Based on the real historical data of VIP.com, we first find main elements that influence the sales revenues of different brands of products through machine learning. We then predict the sales of all brands of products in each 'Dangqi' by multiplicative regression model, which has better accuracy of forecasting than other forecast models and obtain the function relationship between the total sales of each brand and its main impact elements. Finally, considering VIP.com's actual concerns, we develop allocation optimization models with objectives of maximizing VIP.com's total sales and total sales profit, respectively. The results from VIP.com's real data tests show that under the same resource investment the presented allocation optimization approach can yield a significant increase in VIP.com's total sales and sales profit in each 'Dangqi'. [ABSTRACT FROM AUTHOR]