舒嘉博士，東南大學經濟管理學院教授、博士生導師、副院長，2012年獲國家優秀青年科學基金，2015年獲國家杰出青年科學基金，2016年4月入選青年長江學者。研究方向為：物流與供應鏈管理，交通管理，醫療管理。主持國家自然科學基金、教育部歸國留學人員科研啟動基金和教育部新世紀優秀人才支持計劃等多項課題，學術成果發表在Operations Research, Transportation Science, Naval Research Logistics等國際權威期刊上。
報告內容簡介（Abstract）：Containers are widely used in the shipping industry mainly because of their capability to facilitate multimodal transportation. How to effectively reposition the nonrevenue empty containers is the key to reduce the cost and improve the service in the liner shipping industry. In this paper, we propose a two-stage robust optimization model that takes into account the laden containers routing as well as the empty container repositioning, and define the robustness for this model with uncertainties in the supply and demand of the empty containers. Based on this definition, we present the robust formulations for the uncertainty sets corresponding to the?p-norm, wherep= 1, 2, and ∞, and analyze the computational complexities for all of these formulations. The only polynomial-time solvable case corresponds to the?1-norm, which we use to conduct the numerical study. We compare our approach with both the deterministic model and the stochastic model for the same problem in the rolling horizon simulation environment. The computational results establish the potential practical usefulness of the proposed approach.
榮鷹博士，現任上海交通大學安泰經濟與管理學院教授、博士生導師，2015年度國家優秀青年科學基金資助。他于2010年8月回國執教于上海交通大學，此前在美國加州大學伯克利分校和里海大學從事科研工作，并在上海交通大學和美國里海大學分別獲學士、碩士和博士學位。主要研究領域為服務運營優化、新興商業模型的運作以及數據驅動的優化模型。研究成果發表在Management Science, Operations Research，Manufacturing & Service Operations Management, Production and Operations Management, Naval Research Logistics, IIE Transactions等國際學術刊物上。榮鷹教授多次獲得過國際獎項，其中包括兩度MSOM最佳論文獎和INFORMS Energy, Natural Resources & Environment Young Researcher Prize。
報告內容簡介（Abstract）：The focus of this talk is to identify the underlying factors and develop an order assignment policy that can help an on-demand meal delivery service platform to grow. By analyzing transactional data obtained from an online meal delivery platform in Hangzhou (China) over a two-month period in 2015, we find empirical evidence that an ``early delivery'' is positively correlated with customer retention: a 10-minute earlier delivery is associated with an increase of one order per month from each customer. However, we find that the negative effect on future orders associated with ``late deliveries'' is much stronger than the positive effect associated with early deliveries. Moreover, we show empirically that a driver's individual local area knowledge and prior delivery experience can reduce late deliveries significantly. Finally, through a simulation study, we illustrate how one can incorporate our empirical results in the development of an order assignment policy that can help a platform to grow its business through customer retention. Our empirical results and our simulation study suggest that to increase future customer orders, an on-demand service platform should address the issues arising from both the supply side (i.e., driver's local area knowledge and delivery experience) and the demand side (i.e., asymmetric impacts of early and late deliveries on customer future orders) into their operations.