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  • 统计与管理学院2018年学术报告第5期

    【主 题】 Toward an Understanding of Deep Learning for Data Analysis: the Expand-and-Reduce Method

    【报告人】 Yen-Ting (Daniel) Lin 副教授

    University of San Diego

    【时 间】 2018年01月11日(星期四)15:00-16:00

    【地 点】 上海财经大学统计与管理学院大楼1208会议室

    摘 要】We consider two competing electronic waste (e-waste) recovery channels, each of which consists of a collector and a recycler. Collectors obtain e-waste from donations and generate revenue from selling it to recyclers or the secondary market. Recyclers purchase e-waste from collectors, process it, and then sell the recycled material in the commodity market. Each recycler chooses for certification of one of the two standards: e-Stewards or Responsible Recycling (R2). Compared to R2, e-Stewards requires more responsible handling, thus a higher processing cost. However, it can also attract more e-waste from environmentally conscious donors. Seeking to identify factors that influence recyclers to select a standard, we investigate the impact of the secondary market and competition between e-waste recovery channels. We find that a recycler may choose e-Stewards only when facing a competing recovery channel. Interestingly, recyclers with strong scale economies in processing e-waste choose e-Stewards when it incurs significantly higher processing cost than R2. We also investigate the effects of exogenous factors and identify conditions whereby having stronger scale economies in e-waste processing or a higher demand in the secondary market hurt each party's profit.

    嘉宾简介】Yen-Ting (Daniel) Lin is an associate professor of operations management at the University of San Diego School of Business. His current research focuses on sustainable operations. He also works on competitive strategies in the retail industry, including vertical integration, quick response and the impact of strategic customer behavior. Lin graduated from the Kenan-Flagler Business School at University of North Carolina at Chapel Hill with a PhD in operations management. He also has a MS degree in Management Science and Engineering at Stanford University and a BS degree in industrial engineering at National Chiao Tung University in Taiwan. His research has appeared in Manufacturing & Service Operations Management, Production and Operations Management, International Journal of Production Research and International Journal of Production Economics.