商业决策与运营分析研究中心、管理科学系学术讲座（1）-（2）

商业决策与运营分析研究中心、管理科学系学术讲座（1）

时间：2023年6月20日（周二） 13:30-15:00

地点：管理学院史带楼503室

主题：Jumping the Queue: Managing Early and Same-Day Appointments

主讲人：Yunxia Zhu（朱云夏）内布拉斯加大学林肯分校副教授

主持人：吴肖乐复旦大学管理学院教授

摘要：

Healthcare providers commonly use overbooking strategies for scheduling to overcome patient no-shows, which can result in multiple patients waiting for service in clinics. The operations literature offers scheduling models that commonly assume patients have a uniform and linear response to wait time. These assumptions often result in scheduling policies that recommend seeing patients in the order of their appointment schedule. Our study seeks to challenge these assumptions and investigates whether patients are heterogeneous in their response to wait times. To this end, we conduct a series of lab experiments and find that patients’ satisfaction with wait time decreases non-linearly. We also find that patients who book their appointments earlier are less satisfied with waiting than those who book later. We incorporate these empirical findings on the heterogeneous effects of wait time satisfaction into an analytical model that allows for adjustments to the original schedule. Using the model’s structural properties, we developed several insights and a real-time decision heuristic procedure that adjusts schedules after patient check-in to improve satisfaction. Our numerical experiments demonstrate the importance of considering the heterogeneous effects of patients in scheduling frameworks. Specifically, we show that an adjustable scheduling policy can improve patient satisfaction by up to 17% when a provider employs an overbooking strategy with patient no-shows. This highlights the inefficiencies of models that do not explicitly consider patient heterogeneity. Our findings suggest that healthcare providers should adopt a more personalized approach to scheduling that takes into account patients’ varying sensitivity to wait time to improve satisfaction.

主讲人简介：

Dr. Yunxia (Peter) Zhu is an Associate Professor of Supply Chain Management and Analytics at College of Business, University of Nebraska-Lincoln. He received his Bachelor of Science in Operations Research from the School of Management at Fudan University and his Ph.D. in Operations Management from the Naveen Jindal School of Management at the University of Texas at Dallas.

Dr. Zhu’s research interests include supply chain management, health care management, and social networks. He currently serves as a Senior Editor at Production and Operations Management. His research has been published at Management Science, Manufacturing & Service Operations Management, Production and Operations Management, INFORMS Journal on Computing, and IISE Transactions.

商业决策与运营分析研究中心、管理科学系学术讲座（2）

时间：2023年6月20日（周二）15:00-16:30

地点：管理学院史带楼503室

主题：Help and Haggle: Social Commerce Through Randomized, All-or-Nothing Discounts

主讲人：Luyi Yang 加利福尼亚大学伯克利分校助理教授

摘要：

This paper studies a novel social commerce practice known as “help-and-haggle,” whereby an online consumer can ask friends to help her “haggle” over the price of a product. Each time a friend agrees to help, the price is cut by a random amount, and if the consumer cuts the product price down to zero within a time limit, she will get the product for free; otherwise, the product reverts to the original price. Help-and-haggle enables the firm to promote its product and boost its social reach as consumers effectively refer their friends to the firm. We model the consumer’s dynamic referral behavior in help-and-haggle and provide prescriptive guidance on how the firm should randomize price cuts. Our results are as follows. First, contrary to conventional wisdom, the firm should not always reduce the (realized) price-cut amount if referrals are less costly for the consumer. In fact, the minimum number of successful referrals the consumer must make to have a chance to win the product can be non-monotone in referral cost. Second, relative to the deterministic-price-cut benchmark, a random-price-cut scheme improves firm payoff, extracts more consumer surplus, and widens social reach. Besides, in most instances, it also reduces the promotion expense while increasing profit from product sales at the same time. Third, help-and-haggle can be more cost-effective in social reach than a reward-per-referral program that offers a cash reward for each successful referral. However, using the prospect of a free product to attract referrals cannibalizes product sales, potentially causing help-and-haggle to fall short. Yet, if consumers are heterogeneous in product valuations and referral costs or face increasing marginal referral costs, help-and-haggle can outperform the reward-per-referral program.

主讲人简介：

Luyi Yang is an assistant professor in Operations and Information Technology Management at the University of California, Berkeley, Haas School of Business. His research interests include service operations, business model innovation, digital marketplaces, sustainability, and operations-marketing interface. His award-winning research has appeared in leading journals and outlets such as Management Science, Operations Research, Manufacturing & Service Operations Management, Information Systems Research, Production and Operations Management, and Harvard Business Review. His work was presented at major companies and organizations such as Uber Technologies, IBM Research, and the Federal Trade Commission and featured in major media outlets such as Forbes and the South China Morning Post. He has taught courses in business analytics, data mining, and operations management. His new book, "Innovative Priority Mechanisms in Service Operations - Theory and Applications", will be published by Springer in July 2023. Prior to joining Berkeley Haas, he was an assistant professor of operations management and business analytics at Johns Hopkins University’s Carey Business School. He received his PhD and MBA from the University of Chicago, Booth School of Business, and his BS in Industrial Engineering and BA in English, both from Tsinghua University.