2023 Seminar Series on Revenue Management and Pricing
In this seminar series, we invite a group of world-class scholars and rising stars in the area of revenue management and pricing.
Seminar format: Virtual, e.g., Tencent Meeting
Speakers: leading scholars on revenue management and pricing
Audience: Open to public
Language: Chinese (mostly) or English up to speakers
Sponsors: Southeast University School of Economics and Management
Co-Chairs: Ruxian Wang (Johns Hopkins University), Weili Xue (Southeast University)
报告人介绍
Speakers
报告一：Optimizing Offline Product Design and Online Assortment Policy: Measuring the Relative Impact of Each Decision
报告人：Prof. Heng Zhang Arizona State University
报告人简介：Heng Zhang is an assistant professor in the Supply Chain Management Department at Arizona State University, W.P. Carey School of Business. His research interests include revenue management, marketing analytics, supply chain management, data analytics, data-driven optimization, and algorithmic causal inference. He holds a Ph.D. with a concentration in Operations Management from the Data Sciences and Operations Department USC Marshall School of Business. Before joining USC, He obtained his M. Sc. in Systems Engineering from the University of Pennsylvania. In the past, he also worked as an economist at Kwai.com and a research scientist at Amazon.com.
报告二: A Simple Way Towards Fair Assortment Planning: Algorithms and Welfare Implication
报告人：Prof. Ruxian Wang Johns Hopkins University
报告人简介: Dr. Ruxian Wang is a Professor at Johns Hopkins University, Carey Business School. Before returning to academia, he worked in Hewlett-Packard Company for several years as a research scientist. He received Ph.D. from Columbia University. His research and teaching interests include operations management, revenue management, pricing, discrete choice models, data-driven decision making. His articles appeared in several flagship journals in his field, such as Management Science, Manufacturing & Service Operations Management, Operations Research, Production and Operations Management. He received Meritorious Service Awards from Management Science, Manufacturing & Service Operations Management, and Outstanding Service Award from the Production and Operations Management Society (POMS). He is currently serving the Production and Operations Management (POM) journal as a senior editor.
报告三：Effect of Consumer Awareness on Corporate Social Responsibility under Asymmetric Information
报告人：Prof. Guang Xiao Hong Kong Polytechnic University
报告人简介: Dr Guang Xiao is an Associate Professor and the program director of MSc in Global Business and Decision Analysis at Department of Logistics and Maritime Studies, The Hong Kong Polytechnic University. He has received his bachelor degrees in both Mathematics and Philosophy from Peking University, master degree in Operations Research from University of Delaware, and Ph.D. degree in Operations Management from Olin Business School, Washington University in St. Louis. His research interests include supply chain risk management, sustainable and socially responsible operations, interfaces of operations and finance/marketing, etc. He has led several research projects supported by the Research Grants Council of Hong Kong. His research work has been published in many flagship journals in the field, including Management Science, Manufacturing & Service Operations Management, and Production and Operations Management.
报告四：AI Assistance and Service Usage
报告人：Prof. Yao Cui Cornell University
报告人简介：Yao Cui is an assistant professor of operations, technology, and information management at the Samuel Curtis Johnson Graduate School of Management at Cornell University. His research interests center around operational innovation, specifically examining how new technologies can be leveraged to develop novel operations and pricing strategies in supply chains, the gig economy, and the hospitality industry. Using both analytical and empirical methodologies, Professor Cui’s research aims to uncover innovative solutions to practical problems facing these industries.
报告时间安排
Agenda
主持人：王汝现教授（约翰霍普金斯大学）、薛巍立教授（东南大学）
时间：2022/08/31（Thu.）9:00-11:00(上午AM)
报告人：Prof. Heng Zhang
Arizona State University
主题: Optimizing Offline Product Design and Online Assortment Policy: Measuring the Relative Impact of Each Decision
摘要：Revenue management decisions often involve both offline and online decisions. Offline decisions are made first and establish the broad and long-term operational context in which online decisions are frequently and repeatedly made, often in real time. We consider a joint optimization of offline and online decisions. Specifically, we examine a setting in which the offline decision concerns the selection of product-design characteristics (e.g., price, capacity, return eligibility, and other characteristics) and the online decision concerns the dynamic assortment optimization over a selling season. Our formulation has many applications, including optimizing products' return eligibility and determining product discounts. We formulate an optimization problem that combines the impact of both offline and online decisions on the expected revenue. To determine the product design, we reformulate the choice-based deterministic linear program, solve its continuous relaxation, and round the resulting solution. Using value function approximations enables us to obtain a dynamic assortment policy whose expected revenue is at least a constant fraction of the choice-based deterministic linear program. Combining these two results, we show that our approach provides an approximate solution to the joint optimization problem with performance guarantees. Numerical experiments based on real transaction data from a major U.S. retailer show that our method achieves 95% - 97% effectiveness, an advantage of up to 18% over methods that disregard the interplay between offline and online decisions. This framework also yields a systematic quantitative measure of the relative importance of both offline and online decisions. Based on this measure, numerical experiments highlight the crucial role of product design, accounting for 94% and 85% of the observed variation in effectiveness across various methods in applications involving volume discount and return eligibility, respectively.
时间：2023/09/04（Mon.）9:00-11:00(上午AM)
报告人：Prof. Ruxian Wang
Johns Hopkins University
主题：A Simple Way Towards Fair Assortment Planning: Algorithms and Welfare Implication
腾讯会议：774-935-411
摘要: Large e-commerce retailers and department stores function as marketplaces for millions of sellers, and consumers rely on the platform's assortment and display decisions to examine different sellers and make purchase decisions. Traditionally, the primary objective of these marketplaces for assortment planning is to maximize the total expected revenue under some business, which creates unfairness among sellers, because only a single assortment with the highest expected revenue is chosen. This results in some sellers being excluded from recommendations or assortments, with minimal market exposure and revenue, leading to a disproportional distribution of revenue among sellers. To address this issue, we propose fairness constraints that ensure fairer market exposures for all sellers. These constraints ensure each seller to have a minimum market exposure, which may depend on the seller's reputation, product quality, and price, among other features. We show that the optimal solution with fairness constraints is to randomize over at most $n$ nested assortments, where $n$ is the number of sellers (or products), and the optimal solution can be found in polynomial time. For other cases in which there are additional constraints including imposing cardinality constraint on the assortments and limiting the number of different assortments, we characterize the structure of the optimal solutions and propose efficient heuristics. We further explore the impact of fairness constraints on consumer welfare, and show that it always increases when such constraints are imposed. Our analysis reveals that all sellers and the platform could also benefit when fairness constraints induce new sellers with high quality products enter the platform resulting in a win-win-win situation for all parties involved. Even when there is no new seller entry, we identify cases in which the total welfare improves and therefore propose a revenue redistributing mechanism to achieve a win-win-win solution.
时间：2023/09/06（Wes.）9:00-11:00(上午AM)
报告人：Prof. Guang Xiao
Hong Kong Polytechnic University
主题：Effect of Consumer Awareness on Corporate Social Responsibility under Asymmetric Information
腾讯会议：777-345-227
摘要：This paper studies the interaction between a firm and its consumers under the consideration of corporate social responsibility (CSR) and asymmetric information. We develop a game-theoretic model in which a firm can be either socially responsible or irresponsible. However, consumers cannot observe the firm's exact CSR type, and the firm can signal its CSR type through price and a third-party certification. We highlight several interesting findings. First, due to information asymmetry, as more consumers become socially concerned or as they have stronger willingness to reward (punish) the responsible (irresponsible) firm, the responsible firm might even be worse off, whereas the irresponsible firm might be better off. Second, improving the accuracy of the deployed certifications will always benefit the responsible firm but may benefit or hurt the irresponsible firm. Finally, we extend our base model to consider the case where the firm can endogenously decide its CSR type. We find that if the CSR type can only be signaled through price alone, then being responsible is always less profitable and thus a profit-maximizing firm has no incentive to adopt CSR. By contrast, combining price signaling with a reasonably accurate certification could incentivize the profit-maximizing firm to engage in CSR when there is a sufficient number of consumers who are concerned about CSR. Our results suggest that addressing the information asymmetry issue is the key to aligning consumers' goodwill with firms' responsible behaviors. In particular, concerned parties should first exert efforts to create transparency in firms' sustainability practices before making investments to educate consumers and influence their purchasing behaviors.
时间：2023/09/08（Fri.）9:00-11:00(上午AM)
报告人：Prof. Yao Cui
Cornell University
主题：AI Assistance and Service Usage
腾讯会议：408-245-186
摘要：As companies increasingly integrate artificial intelligence (AI) into their services, it is becoming crucial to understand AI's effect on service users. In this paper, we investigate how AI assistance impacts consumer service usage and how this impact varies according to users' experience levels. We address these questions through a collaboration with an international on-demand car-sharing platform that recently underwent a technology upgrade involving an AI-powered driver monitoring and feedback system. Combining theoretical insights with empirical analyses, we uncover a heterogeneous effect characterized by a U-shaped relationship: users with low or high experience levels increase their service usage due to AI assistance, while users with medium experience levels do not. Furthermore, we find that the motivations behind increased service usage differ between users with low versus high experience levels: low-experience users tend to benefit more from AI as they find greater marginal value in its feedback, while high-experience users tend to benefit more from AI likely due to their proficiency in internalizing AI's feedback and improving their behavior. On the other hand, medium-experience users are less likely to benefit from AI because they derive a lower marginal value from AI's feedback compared to their less experienced peers, and unlike their more experienced peers, they are less capable of enhancing the value of the AI by adaptively reacting to it. These results highlight the need for customized solutions to maximize the value that AI can create for different segments of users.