时间：2024年7月11日上午9:00-11:00

地点：东校区财科馆EMBA教室

【讲座一】

题目：Consumer Choice Probability Models – Temporal Trees, Representation and Identification

ABSTRACT:A consumer choice probability model (CCPM) characterizes the consumer choice probabilities (CCPs) through a set of equality and inequality constraints. We develop a temporal tree consumer choice probability model where a set of branching probabilities fully characterize the consumer choice probabilities of all rational consumer choice models (CCM) such as Random Utility Model (RUM) and Rank List Model (RLM).

We will outline the Temporal Tree Representation of standard CCMs such as Multinomial Logit Model and its variants, Exponomial Model, Markov Chain Model etc. We will show that a suitably defined subclass of Temporal Trees can be uniquely identified for any given rational consumer choice probabilities. This makes the Temporal Tree amenable for Identification using machine learning algorithms with extension to consumer choice models that are built on consumer and/or product attributes.

报告人：Prof. J. George Shanthikumar(Richard E. Dauch Chair of Manufacturing and Operations Management and Distinguished Professor of Management)

报告人简介：J. George Shanthikumar joined the Krannert School as the Richard E. Dauch Chair in Manufacturing and Operations Management in 2009. In 2014, he is recognized as the University Distinguished professor of Management. Before joining Purdue, he was a Chancellor’s Professor of Industrial Engineering and Operations Research at the University of California, Berkeley, CA. He received the B. Sc. degree in mechanical engineering from the University of Sri Lanka, Peradeniya, and the M. A. Sc. and Ph. D. degrees in industrial engineering from the University of Toronto, Toronto, Canada.

His research interests are in integrated inter-disciplinary decision making, model uncertainty & learning, production systems modeling and analysis, queueing theory, reliability, scheduling, semiconductor yield management, simulation, stochastic processes, and sustainable supply chain management. He has written or written jointly over 300 papers on these topics. He is a coauthor (with John A. Buzacott) of the book Stochastic Models of Manufacturing Systems and a coauthor (with Moshe Shaked) of the book Stochastic Orders and Their Applications and the book Stochastic Orders.

He is a department editor for Management Science, a department editor for Production and Operations Management, an associate editor of Probability in the Engineering and Informational Sciences, an associate editor of Naval Research Logistics, and a member of the editorial advisory boards of Asia-Pacific Journal of Operations Research and IEEE Transactions on Automation Sciences and Engineering. He was a member of the editorial advisory board of Journal of the Production and Operations Management Society, was a co-editor of Flexible Services & Manufacturing Journal, area editor for Operations Research Letters and was an associate editor for IIE Transactions, International Journal of Flexible Manufacturing Systems, Journal of Discrete Event Dynamic Systems, Operations Research, OPSEARCH, and Queueing Systems: Theory and Applications. He is also a fellow of INFORMS and POMS.

【讲座二】

题目：Mostly Beneficial Clustering: Aggregating Data for Operational Decision Making

Abstract:With increasingly volatile market conditions and rapid product innovations, operational decision-making for large-scale systems entails solving thousands of problems with limited data. Data aggregation is proposed to combine the data across problems to improve the decisions obtained by solving those problems individually. We propose a novel cluster-based Shrunken-SAA approach that can exploit the cluster structure among problems when implementing the data aggregation approaches. We prove that, as the number of problems grows, leveraging the given cluster structure among problems yields additional benefits over the data aggregation approaches that neglect such structure. When the cluster structure is unknown, we show that unveiling the cluster structure, even at the cost of a few data points, can be beneficial, especially when the distance between clusters of problems is substantial. Our proposed approach can be extended to general cost functions under mild conditions. When the number of problems gets large, the optimality gap of our proposed approach decreases exponentially in the distance between the clusters. We explore the performance of the proposed approach through the application of managing newsvendor systems via numerical experiments. We investigate the impacts of distance metrics between problem instances on the performance of the cluster-based Shrunken-SAA approach with synthetic data. We further validate our proposed approach with real data and highlight the advantages of cluster-based data aggregation, especially in the small-data large-scale regime, compared to the existing approaches.

报告人：李成璋副教授（上海交通大学安泰经济与管理学院）

报告人简介：Chengzhang Li is an Associate Professor in Management Science at Antai College of Economics and Management, Shanghai Jiao Tong University (SJTU). His research interests lie in supply chain management, socially responsible operations, and data-driven decision making. His works have been published in Management Science and Production and Operations Management. Prior to joining SJTU, he received his M.S. in Statistics and Computer Science and his Ph.D. in Operations Management both from Purdue University, and his B.S. in Mechanical Engineering and Automation from SJTU.

【讲座三】

题目：Output-Oriented Agricultural Subsidy Design

Abstract:Many governments subsidize the agricultural industry, trying to raise the market outputs either for domestic needs or for export. In many countries, particularly developing countries, the producers’ market may be fragmented, involving a large number of farmers with variable productivity levels. The format of subsidies can have significantly different implications for farmers in different market segments. In this study, we examine four types of subsidies. A planting subsidy is paid to a farmer based on the amount of input, and a har- vesting subsidy compensates a farmer for the cost incurred during the process of output col- lection and distribution. The government may also offer a combined subsidy under which a farmer gets paid for both planting and harvesting, or it may offer a selective subsidy under which a farmer can choose to be subsidized on either planting or harvesting but not both. In addition to examining the efficiency of budget spending and social welfare, two common performance measures studied in various contexts, we thoroughly analyze the implications of subsidies on the output and wealth distributions among the farmers. In general, subsidizing on harvesting or overly compensating on planting can increase the disparity among the farmers, whereas an appropriate level of planting subsidy helps to balance the distributions of the farmers’ output and profit. A comprehensive evaluation of the government’s policies reveals that the harvesting subsidy, while inducing the most dispersed output and profit distributions, leads to the most efficient use of input resources and the highest social welfare. The planting subsidy, although being the most effective in balancing the farmer income for a moderate output increase, performs poorly in budget spending, resource usage, and welfare generation when the government sets an aggressive target for output increase. In such a situation, the combined subsidy can offer the most evenly distributed farmer income, with the least amount of budget needed to achieve the output target.

报告人：李元宸博士（同济大学经济与管理学院、管理高等研究院助理教授）

报告人简介：Yuanchen LI is an assistant professor at the Advanced Institute of Business at Tongji University. He received his Ph.D. in Operations Management from Purdue University and a bachelor’s degree from Shanghai Jiao Tong University. His current research interests include supply chain management and healthcare management. His work has been published in Management Science, Manufacturing and Service Operations Management, and Production and Operations Management.