主旨报告嘉宾
胡明，Professor of Operations Management at Rotman School of Management, University of Toronto（多伦多大学罗特曼商学院）and one of the 2018 Poets & Quants Best 40 Under 40 MBA Professors. He was awarded Rotman School Teaching Award 5 years in a row from 2009 to 2013 and 2015 for teaching undergraduate/MBA courses. His research has been featured in media such as Financial Times. Most recently, he focuses on operations management in the context of sharing economy, social buying, crowd funding, crowd sourcing, and two-sided markets, with the goal to exploit operational decisions to the benefit of the society. He is the recipient of Wickham Skinner Early-Career Research Accomplishments Award by POM Society (2016) and Best Operations Management Paper in Management Science Award by INFORMS (2017). He currently serves as the editor-in-chief of Naval Research Logistics, co-editor of a special issue of Manufacturing & Service Operations Management on sharing economy and innovative marketplaces, department co-editor of Service Science, and associate editor of Operations Research and Manufacturing & Service Operations Management, and senior editor of Production and Operations Management. He currently also serves as Vice Chair/Chair-Elect for the RM&P Section of INFORMS and Secretary/Treasurer for the MSOM Society of INFORMS. He received a master's degree in Applied Mathematics from Brown University in 2003, and a Ph.D. in Operations Research from Columbia University in 2009.
报告题目：From the Classics to New Tunes: A Neoclassical View on Sharing Economy and Innovative Marketplaces
报告摘要：Operations management has the tradition of coming from and going back to real-life applications. It deals with the management of the process of matching supply with demand. The emerging business process in a sharing economy or an innovative marketplace calls for active management from the operational perspective. We take a neoclassical perspective by drawing inspiration from the classic models in operations management and economics. We aim at building connections and identifying differences between those traditional models and the new applications in sharing economy and innovative market places.
黎擎，Professor of Operations Management at the Business School at Hong Kong University of Science and Technology (香港科技大学商学院). He is also the Academic Director for Msc in Global Operations program at HKUST. His research focuses on dynamic inventory control, behavioral operations management, and business analytics. In recent years, he has worked with companies on worker recruiting, turnovers, warehouse layout, SKU rationalization, and appointment scheduling. Besides work, he enjoys reading history and Chinese art.
报告题目：Reducing Waste of Perishables in Retailing through Transshipment
报告摘要：Transshipment in retailing is a practice where one outlet ships its excess inventory to another outlet with inventory shortages. By balancing inventories, transshipment can reduce waste and increase fill rate at the same time. In this paper, we explore the idea of transshipping perishable goods in offline retailing. In the offline retailing of perishable goods, customers typically choose the newest items first, which can lead to substantial waste. We show that in this context, transshipment plays two roles. One is inventory balancing, which is well known in the literature. The other is inventory separation, which is new to the literature. That is, transshipment allows a retailer to put newer inventory in one outlet and older inventory in the other. This makes it easier to sell older inventory and reduces waste as a result. Our numerical studies show that transshipment and clearance sales are substitutes in terms of both increasing profit and reducing waste. In particular, transshipment can increase profit by up to several percentage points. It is most beneficial in increasing profit when the variable cost of products is high and hence few items are put on clearance sale. Although transshipment does not always reduce waste, when it does, the reduction can be substantial. Similar to the way it impacts profit, transshipment can reduce waste the most when the variable cost of products is high and hence products are too expensive to be put on clearance sale.
汤文捷，Visiting assistant professor in the Institute of Operations Research & Analytics (IORA) and Business School at National University of Singapore (新加坡国立大学IORA中心&商学院). Her main research interests involve developing analytical models to study individual or firm decision-making in the context of retailing, recruitment, or supply chain management, and exploring experimentally how teams can improve their judgment quality and ability to innovate. She received a PhD degree in Decision Sciences from INSEAD, and a BSdegree in Physics from Fudan University.
报告题目：Size Matters, So Does Duration: The Interplay between Offer Size and Offer Deadline
报告摘要：This work investigates the interplay between offer size and offer deadline in a Stackelberg game involving a proposer and responder. The proposer acts first by making an offer to the responder with a deadline, and the responder, concurrently following a continuous time finite-horizon search for alternative offers, has to respond to the proposer's offer by the deadline. Taking into account the responder's reaction, the proposer's optimal strategy can vary from an exploding offer---an offer that has to be accepted or rejected on the spot---to an offer with extended deadline under different market conditions, proxied by characteristics of the alternative offer distribution. In particular, the proposer should offer an exploding offer when the alternative offer market is unfavorable to the responder, and the harsher it is, the smaller the offer size. On the other hand, when the alternative offer market is favorable to the responder, the proposer can benefit from making a smaller (compared to the exploding offer)non-exploding offer, and the more favorable the market, the smaller the offer size and the longer the deadline. Our analysis is further extended to the case where the responder has private knowledge of the alternative offers' arrival rate, and we characterize the optimal strategy for the proposer when she either makes a single offer or a menu of offers that serves as a self-selection mechanism. In the latter case, the optimal menu of offers can be implemented as a sign-up bonus type of contract.
胡震禹，Assistant professor in the Institute of Operations Research & Analytics (IORA) and Business School at National University of Singapore (新加坡国立大学IORA中心&商学院). Prior to that, he received his Ph.D. in Industrial Engineering from the University of Illinois at Urbana-Champaign (UIUC), and he obtained his B.Sc. in Applied Mathematics from Sun Yat-sen University. He worked as a Research Summer Internat IBM T.J. Watson Research Center in 2014. His research focuses on dynamic pricing, revenue management, inventory and supply chain management.
报告题目：Puzzle of Gain-Seeking and the Dynamic Pricing Problem
报告摘要：Behavioural pricing and revenue management aims to incorporate realistic consumer behaviour into firms' pricing and inventory models. The key input to these models is market demand, which is often assumed to inherit the characteristics of consumer behaviour---as when, for example, one assumes that a market consisting of loss-averse consumers is more responsive to losses than to gains. The empirical evidences on loss-sensitive demand (as opposed to gain-sensitive demand), however, are frustratingly mixed. In this talk, I will offer one explanation on why gain-sensitive demand and loss-averse consumers can coexist and examine the implication of gain-sensitive demand on the dynamic pricing strategies.
龙卓瑜，Assistant professor in the Department of Systems Engineering and Engineering Management at the Chinese University of Hong Kong (香港中文大学系统工程与工程管理系). He joined the department in 2013, after receiving his PhD degree from the National University of Singapore. His research interests include logistics management, supply chain management, project management, robust optimization and risk management.
报告题目：Preservation of Supermodularity in Parametric Optimization: Necessary and Sufficient Conditionson Constraint Structures
报告摘要：We present a systematic study of the preservation of supermodularity under parametric optimization, that allows us to derive complementarity among parameters and monotone structural properties of optimal policies in many operations models. We introduce new concepts of mostly-sublattice and additive mostly-sublattice, which significantly generalize the commonly imposed sublattice condition, anduse them to establish the necessary and sufficient conditions on the feasibleset so that supermodularity can be preserved under various assumptions on the objective functions. We further identify some classes of polyhedral sets which satisfy these concepts. Finally, we illustrate how our results can be used on assemble-to-order systems.
杨超林，Associate professor in the School of Information Management and Engineering at Shanghai University of Finance and Economics (上海财经大学信息管理与工程学院，SUFE). Before joining SUFE in 2015, he earned a Ph. Din Systems Engineering and Engineering Management from the Chinese University of Hong Kong. His research interests include supply chain, inventory and revenue management. His research has been published in journals such as Operations Research and Management Science. He has consulted for several companies on their inventory strategy optimization such as JD.com and SF Technology.
报告题目：Aggregation Bias in Estimating Linear and Log-Log Demand Functions
报告摘要：We consider the problem of estimating demand functions using historical sales data. In particular, we study how using aggregate data may result in bias in such estimations. We consider two demand models, a linear model and a log-log model, and two ways of how the price in the aggregate data is calculated, simple average price or weighted average price. We study whether there exists estimation bias in each of the four cases as well as the direction of the bias. We show that ingeneral, the correlation between average price and price dispersion in each time period has an effect on the direction of the bias. We then propose ways to reduce the aggregation bias. We also perform numerical experiments using empirical data from TMall.com and the numerical experiments show that ourresults can help predict the direction of the bias as well as reduce the bias.
陈艺天，Senior staff algorithm engineer, Bigo Inc.前京东资深算法工程师。在京东期间，主要从事电商统计建模与运筹优化相关的数据建模工作，带领团队搭建着京东第一个动态定价系统，该系统目前管理着京东自营约50%~70%中长尾商品的自动价格管理；参与京东的销量预测系统的优化，率先搭建起基于神经网络的预测模型框架，该框架提升原京东物流单量约50%的准确度。多次受邀去KDD作技术报告。
报告题目：工业界数据驱动的业务建模: Business, Data and Model
报告摘要：在数据爆炸的今天，通过对历史数据的梳理与分析，进而搭建数据驱动的业务解决方案是目前很多公司目前都在尝试进行的工作:如电商或零售商会根据历史的用户购买数据进行动态定价或促销优化，短视频公司会分析历史用户行为进而调整自己的推荐和运营策略。一个好的数据驱动的业务解决方案往往涉及到对业务问题的深入理解，业务数据的收集整理分析，和最终业务模型的搭建(包含对未来的预测和基于预测结果的优化决策)。我们将从business, data, model这三个角度去阐述业务建模常用的框架策略并从这些角度逐一呈现我们在国内一大型电商平台对百万级别商品动态定价的解决方案框架。在时长一个月的区域对照实验结果显示，我们的动态定价方案可以大幅度提升电商的营收和毛利，该方案目前正应用海量中长尾商品的价格管理。
