Appointment window scheduling with wait-dependent abandonment for elective inpatient admission
Yuwei Lu,
Zhibin Jiang,
Na Geng,
Shan Jiang and
Xiaolan Xie
International Journal of Production Research, 2022, vol. 60, issue 19, 5977-5993
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.
Date: 2022
References: Add references at CitEc
Citations:
Downloads: (external link)
http://hdl.handle.net/10.1080/00207543.2021.1977407 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:taf:tprsxx:v:60:y:2022:i:19:p:5977-5993
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TPRS20
DOI: 10.1080/00207543.2021.1977407
Access Statistics for this article
International Journal of Production Research is currently edited by Professor A. Dolgui
More articles in International Journal of Production Research from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().