Appointment window scheduling with wait-dependent abandonment for elective inpatient admission

ABSTRACT

In this study, we propose a new appointment window scheduling (AWS) approach of informing customers of an admission window (AW) rather than the traditional appointment time. We provide a formal description of this AWS problem for only one kind of customer and propose a dedicated chance-constrained policy to assign AWs dynamically under the condition with fixed service capacity, different scales as well as status in different waiting stages, and wait-dependent abandonment. Numerical experiments show that customer satisfaction can be significantly improved (by reducing over 60% of wait-but-abandon events and by reducing 90% of departures caused by waiting beyond the AW), and server utilisation is slightly improved. And the improvements are more significant when systems are overloaded, and customers are more sensitive to online waiting than offline waiting. The AWS scenario can also be applied to other queueing systems as long as it is possible and profitable to let customers wait outside of the waiting area.

KEYWORDS:

• Application in healthcare systems
• operations research
• scheduling
• customer behaviour
• patient admission

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [grant numbers 71432006, 71801058, 61374095], and Guangxi Science and Technology Project [grant numbers 2018JJB110017, 2018AD19260].

Notes on contributors

Yuwei Lu

Yuwei Lu received the Ph.D. degree in industrial engineering from Shanghai Jiao Tong University, Shanghai, China, in 2018. She is currently an associate professor and the vice director of department Mechanical Engineering in the School of Mechanical and Automotive Engineering, Guangxi University of Science and Technology, Liuzhou, 545006, China. She is also currently engaged in Post-doctoral research in SAIC-GM-Wuling Automobile Co., Ltd. Her research has been published in the European Journal of Production Research, International Journal of Production Research, etc. Her research interests lie in optimisation, including appointment scheduling in healthcare and intelligent manufacturing in automobile.

Zhibin Jiang

Zhibin Jiang is currently a Full Professor in the Department of Management Science at Shanghai Jiao Tong University, China. He is also the dean of the Industrial and System Engineering Institute (Sino-US Global Logistics Institute, SUGLI) of Shanghai Jiao Tong University. He received the Ph.D. degree of Engineering Management from City University of Hong Kong. He has authored or coauthored over 100 articles in international journals such as Production and Operations Management, INFORMS Journal on Computing, IEEE Transactions on Automation Science and Engineering, Omega-The International Journal of Management Science, and International Journal of Production Research. He is currently serving as a fellow of Institute of Industrial and Systems Engineers and associate editor of International Journal of Production Research. His research interests include production and service operations management, healthcare service management, and logistics and supply chain management.

Na Geng

Na Geng received the Ph.D. degree in industrial engineering from the Ecole Nationale Superieure des Mines de Saint-Etienne, Saint-Étienne, France, and Shanghai Jiao Tong University (SJTU), Shanghai, China, in 2010. She is currently a full Professor with the Sino-US Global Logistics Institute, SJTU. Her research interests include production and service operations management. Dr. Geng has been an Associate Editor of the Flexible Service and Manufacturing journal since 2017, an Associate Editor of the IEEE Transactions on Automation and Science Engineering since 2021, and an Associate Editor of the Health Care Management Science since 2021.

Shan Jiang

Shan Jiang received the Ph.D. degree in Industrial and Systems Engineering from Rutgers University-New Brunswick, NJ, USA, where he develops data-driven predictive models for risk analysis and uses deep learning for adaptive system control. His main research interests are stochastic modelling, optimisation, simulation, and control. He is currently a data science lead in supply chain at Johnson & Johnson. He has co-authorized papers on IEEE ITS, IISE Transactions, IEEE TASE, INFORMS Journal on Computing, International Journal of Production Research and Journal of Medical System.

Xiaolan Xie

Xiaolan Xie is currently a Professor of Industrial Engineering at the Centre CIS, Mines Saint-Étienne, Saint-Étienne, France. He is the author/a coauthor of more than 300 publications, including more than 120 journal articles and six books. His research interests include healthcare operations management and data analytics. Dr. Xie was the Founding Chair of the Technical Committee on Automation in Health Care Management of the IEEE Robotics & Automation Society. He is also the General Chair of the IEEE International Conference on Automation Science and Engineering (CASE) 2021, an Editor of IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, and an Associate Editor of the International Journal of Production Research.