讲座：Managing Appointment Booking under Customer Choices
题 目：Managing Appointment Booking under Customer Choices
嘉 宾：Nan Liu, Assistant Professor, Operations Management, Boston College Carroll School of Management
时 间：2018年6月14日（周四） 10:00-11:30
地 点：上海交通大学徐汇校区 包图A303室
Motivated by the increasing use of online appointment booking platforms, we study how to offer appointment slots to customers in order to maximize the total number of slots booked. We develop two models, non-sequential offering and sequential offering, to capture different types of interactions between customers and the scheduling system. In these two models, the scheduler offers either a single set of appointment slots for the arriving customer to choose from, or multiple sets in sequence, respectively. For the non-sequential model, we identify a static randomized policy which is asymptotically optimal when the system demand and capacity increase simultaneously, and we further show that offering all available slots at all times has a constant factor of 2 performance guarantee. For the sequential model, we derive a closed-form optimal policy for a large class of instances and develop a simple, effective heuristic for those instances without an explicit optimal policy. By comparing these two models, our study generates useful operational insights for improving the current appointment booking processes. In particular, our analysis reveals an interesting equivalence between the sequential offering model and the non-sequential offering model with perfect customer preference information. This equivalence allows us to apply sequential offering in a wide range of interactive scheduling contexts. Our extensive numerical study shows that sequential offering can significantly improve the slot fill rate (6-8% on average and up to 18% in our testing cases) compared to non-sequential offering. Given the recent and ongoing growth of online and mobile appointment booking platforms, our research findings can be particularly useful to inform user interface design of these booking platforms.
This is joint work with Peter van de Ven (CWI, Netherland) and Bo Zhang (IBM Research AI).
Nan Liu is Assistant Professor of Operations Management at Boston College Carroll School of Management. Dr. Liu studies operations management topics in service industries (e.g., health care, retail and transport). His current research seeks to resolve the central operational problem faced by many service-oriented organizations – how to match service capacity with customer demand. One particular application area of his research is health care, in which he addresses questions of how to deliver health services in a more timely, effective and efficient way. Trained as both an operations researcher and a data scientist, he draws methodological tools from stochastic modeling, optimization and statistics. Prior to joining the Carroll School of Management, he was on the faculty of Health Policy and Management Department at Columbia University, where he was the recipient of the 2014 Calderone Junior Faculty Research Prize.
Dr. Liu’s research has been published in leading academic journals such as Management Science, Operations Research, Manufacturing & Service Operations Management, Production and Operations Management, Health Services Research, Medical Care Research and Review, and Public Administration Review. His work has been recognized by a number of research awards, such as the Winner of 2018 POMS College of Healthcare Operations Management Best Paper Competition, the Honorable Mention of 2017 Chinese Scholars Association for Management Science and Engineering Annual Conference Best Paper Competition, and the Third Place in 2013 INFORMS Junior Faculty Interest Group Paper Competition. His research findings have received coverage in popular media outlets such as The Washington Post and been featured as a cover story in Crain’s New York Business.
He holds a Ph.D. in Operations Research and an M.S. in Statistics from the University of North Carolina at Chapel Hill, and a B.Eng. in Civil Engineering from Tsinghua University, Beijing, China.