[Lecture 1]
Title:新商科：新时代的整合者
Spreker: BaofengHuo, Professor,Director of the Management and Economics Department of Tianjin University
Time: 13:30-14:15pm, Jan. 10th, 2021, Sunday
Venue: Tencent meeting ID：825-699-292/RoomA302, Science Building, North Zhongshan Road Campus
Sponsor:Emerging Markets Service Management research team,Statistics and Interdisciplinary Sciences research team
Abstract:将报告新商科的时代背景、基本理念、部分商学院的探索。
Speaker’s Bio:霍宝锋博士是天津大学管理与经济学部主任、运营管理领域的讲席教授、博士生导师、国家杰出青年基金获得者，Elsevier中国高被引学者。全国工商管理专业学位研究生教育指导委员会委员、天津市社科联副主席（兼职）、天津市管理学科评议召集人。拥有香港中文大学运营管理专业的哲学博士学位和天津大学管理科学与工程专业的工学硕士和管理信息系统专业的工学学士学位。研究与教学领域包括运营管理、物流与供应链管理。论文发表在Journal of Operations Management, Production and Operations Management, Journal of Supply Chain Management, IEEE Transactions on Engineering Management, International Journal of Operations and Production Management, Journal of Business Logistics, International Journal of Production Research, International Journal of Production Economics, Information & Management, Business Horizons，《管理科学学报》、《系统工程理论与实践》、《科研管理》等期刊。担任《天津大学学报(社会科学版)》主编、Journal of Operations Management副主编、International Journal of Physical Distribution & Logistics Management高级副主编、Production and Operations Management编委、IEEE Transactions on Engineering Management编委, International Journal of Operations & Production Management编委，International Journal of Production Economics编委, Industrial Management & Data Systems编委。
[Lecture 2]
Title:Adulteration or not? traceability, government regulation and competition.
Spreker: Weihua Zhou, Professor,Zhejiang University
Time: 14:15-15:00pm, Jan. 10th, 2021, Sunday
Venue: Tencent meeting ID：825-699-292/RoomA302, Science Building, North Zhongshan Road Campus
Sponsor:Emerging Markets Service Management research team,Statistics and Interdisciplinary Sciences research team
Speaker’s Bio:周伟华，教授、博士生导师（供应链管理方向）。浙江大学“求是青年学者”。曾受国家留学基金委资助在斯坦福开展合作研究。现任浙江大学数据分析和管理国际研究中心主任，管理学院副院长，浙江大学“大数据＋分析和管理”创新团队首席专家，浙江省高校水平创新团队“数据分析和管理”负责人，浙江大学-麻省理工大学“食品供应链系统化风险管理”项目中方负责人。兼任中国优选法统筹法与经济数学研究会服务科学与运作管理分会副理事长；管理科学工程协会长三角协同管理研究会副理事长；中国运筹学会随机服务与运作管理分会常务理事；浙江省技术经济和管理现代化研究会副理事长；中国管理现代化研究会运作管理专业委员会专业委员；管理科学与工程学会理事；《管理工程学报》、《Modern Supply Chain Research and Applications》编委。
近年来，围绕供应链金融，数据驱动管理，农产品供应链从事供应链管理方面的教学与科研工作，重点研究供应链中的优化控制策略和协作竞争策略，解决/分析了制造业供应链、农产品供应链中的相关问题。目前在国内外重要学术刊物上发表论文30余篇，包括发表在Management Science、Operations Research、Production and Operations Management、管理世界、经济要参、管理工程学报等国内外顶级刊物。已出版专著3本、教材1本。
Abstract:We analyze farmers' adulteration behavior in two competing supply chains: traceable supply chain (TSC) and untraceable supply chain (USC). Each supply chain includes an upstream farmer and a downstream vendor. The upstream farmer might adulterate his food and sell it to the downstream vendor. The government is responsible for food safety and inspects the vendor in the market. For the TSC, food products are marked with the identity of the upstream farmer, and if the government find traceable products adulterated, he imposes a penalty on the farmer, while in the USC, the producer of the food (farmer) cannot be identified, so the government directly punishes the vendor if the food is found adulterated. We fully characterize the farmer's equilibrium adulteration behavior considering the effect of traceability and competition. Besides, we analyze how the cost for traceability, the initial quality difference of two SCs, the level of quality enhancement after adulteration, and the government's penalty jointly impact the farmer's adulteration behaviors. Contrary to the common wisdom that a higher penalty can inhibit adulteration behavior, our results demonstrate that there exist conditions under which the supply chain's traceability may backfire and inadvertently leads the farm to adulterate when the government imposes a higher penalty. Furthermore, empirical analysis with actual data on government sampling results also validates our theoretical results.
[Lecture 3]
Title:Emergency supplies distribution for early disaster response operations under demand information asymmetry
Spreker: Jia Shu, Professor,Southeast China University
Time: 15:00-15:45pm, Jan. 10th, 2021, Sunday
Venue: Tencent meeting ID：825-699-292/RoomA302, Science Building, North Zhongshan Road Campus
Sponsor:Emerging Markets Service Management research team,Statistics and Interdisciplinary Sciences research team
Speaker’s Bio:舒嘉，东南大学/电子科技大学经管学院教授、博士生导师、副院长。在新加坡国立大学和美国麻省理工学院联合培养获得管理科学博士学位，回东南大学任教之前曾在美国和新加坡的大学有任教经历。主要从事物流、交通运输与供应链管理的研究工作。在Operations Research，Transportation Science，INFORMS Journal on Computing发表论文7篇。部分研究成果入选美国麻省理工学院斯隆管理学院研究生课程讲义、获得了包括美国工程院院士，美国管理科学学会前主席，美国INFORMS Fellow等管理科学领域知名科学家的引用、肯定和好评。
Abstract:Quick response to the urgent demands in the affected areas after a disaster through a timely and effective distributing emergency supplies is of great importance in reducing disaster impact. In this study, we consider emergency supplies distribution for early disaster response operations under uncertainty, and propose a single-commodity, two-stage robust model that determines the number of commodities to be distributed from relief facilities to affected areas in a multi-sourcing disaster relief logistics network. In the early response stage after a disaster, the providers of relief-demand information take communities as the statistical standard, but the actual demanders include not only the community population but also the floating population. The resulting demand information asymmetry is the main reason for an uncertain demand, which is the uncertain parameter in our model. Meanwhile, the two stages are defined with respect to demand information asymmetry, and we use the upper bounds, the lower bounds, and the most likely values of uncertain demands to define an uncertainty set. The objective is to minimize the sum of the first-stage cost and the worst-case second-stage cost among all possible realizations of uncertain demands in the uncertainty set. We illustrate the advantage of our model on a case study concerning the 2010 Yushu earthquake in P.R. China. The computational results demonstrate that the two-stage robust model outperforms the deterministic and scenario-based two-stage stochastic models for the same problem.
[Lecture 4]
Title:Making the Most of Your Regret: Workers’ Relocation Decisions in On-Demand Platforms
Spreker: Zhongzhong Jiang, Professor,Dean of the School of Business Administration, Northeastern University
Time: 15:45-16:30pm, Jan. 10th, 2021, Sunday
Venue: Tencent meeting ID：825-699-292/RoomA302, Science Building, North Zhongshan Road Campus
Sponsor:Emerging Markets Service Management research team,Statistics and Interdisciplinary Sciences research team
Speaker’s Bio:蒋忠中，现任东北大学工商管理学院院长、教授(破格)、博导，入选国家级人才计划青年人才，行为与服务运作管理研究所所长；曾任美国明尼苏达大学访问教授，国家自然科学基金委创新研究群体及国际重大合作项目骨干成员；兼任国际期刊International Journal of Engineering Business Management副主编、中国运筹学会随机服务与运作管理分会常务理事、中国运筹学会行为运筹与管理分会常务理事、中国优选法统筹法与经济数学研究会服务科学与运作管理分会常务理事、中国管理科学与工程学会理事、中国系统工程学会智能制造系统工程专业委员会委员、辽宁省工业和信息化厅服务型制造专家等。
近年来，围绕电子商务与共享经济、行为运作与收益管理、物流与供应链优化、服务运作与服务型制造等领域承担国家自然科学基金青年、面上及重点项目等多项；在MSOM、NRL、TRB、EJOR、OMEGA、DSS、AOR、COR、TRE、IEEE Trans.、管理科学学报等国内外顶级/重要学术期刊发表论著60余篇，获省部级优秀成果奖12项及省部级领导批示2项；荣获辽宁青年科技奖、辽宁省“兴辽英才计划”首届青年拔尖人才、辽宁省“百千万人才工程”百层次人才、辽宁省高校杰出青年学者、沈阳市高层次领军人才、沈阳市青年岗位能手等荣誉。
Abstract:We have witnessed a rapid rise of on-demand platforms, such as Uber, in the past few years. While these platforms allow workers to choose their own working hours, they have limited leverage in maintaining availability of workers within a region. As such, platforms often implement various policies, including offering financial incentives and/or communicating customer demand to workers in order to direct more workers to regions with shortage in supply. This research examines how behavioral biases such as regret aversion may influence workers’ relocation decisions and ultimately the system performance. A combination of behavioral modeling and controlled lab experiments is used in this study. We develop analytical models that incorporate regret aversion to produce theoretical predictions, which are then tested and verified via a series of controlled lab experiments. Results show that regret aversion plays an important role in workers’ relocation decisions. Regret averse workers are more willing to relocate to the supply-shortage zone than rational workers. This increased relocation behavior, however, is not sufficient to translate to a better system performance. Platform interventions, such as demand information sharing and dynamic wage bonus, can help further improve the system. We find that workers’ regret-aversion behavior may lead to an increased profit for the platform, a higher surplus for the workers, and an improved demand-supply matching efficiency, thus benefiting the entire on-demand system.
