Academic Journal
Timeslot allocation for waiting list control.
| Title: | Timeslot allocation for waiting list control. |
|---|---|
| Authors: | Vromans, R. F. M.1,2 (AUTHOR), van Essen, J. T.3 (AUTHOR) j.t.vanessen@tudelft.nl, van der Vlugt, Y. M.3 (AUTHOR), Carlier, M.2 (AUTHOR) |
| Source: | OR Spectrum. Sep2026, Vol. 48 Issue 3, p787-825. 39p. |
| Subject Terms: | *Markov processes, *Resource allocation, *Scheduling, Time, Outpatient services in hospitals, Reinforcement learning |
| Geographic Terms: | Netherlands |
| Abstract: | As pressure on the healthcare system increases, patients who require surgery experience longer access times to pre- and post-operative appointments and surgery. Hospitals can control their waiting lists by allocating timeslots to the different types of appointments they discern. To inform patients about their appointments in a timely manner, they need to make this decision several weeks in advance. However, the precise consequences of the timeslot allocation on the future waiting list are uncertain, as not all patients follow the same treatment pathway. Furthermore, as these planning decisions are made weeks in advance, they are based on an uncertain prediction of future waiting lists. In this paper, methods are developed with the aim to support hospitals in optimizing their timeslot allocation to reduce patient access times and utilize all available capacity in the outpatient department and operating room. The problem is modelled as a Markov decision process (MDP). However, as the state, decision and outcome spaces grow exponentially in size, even for a single surgeon, an exact solution cannot be determined. We thus compare four alternative solution methods to the static allocation method that is common in hospitals. Least-squares policy iteration is used to find an approximate solution, an (integer) linear program is formulated to solve a deterministic variant of the MDP, several heuristic decision rules are investigated, and a hybrid method is proposed that statically allocates a percentage of timeslots and then dynamically allocates the remaining timeslots with the linear program when sufficient information is available to effectively deal with variability. The solution methods are tested on a case study at the orthopedic care chain of the Sint Maartenskliniek hospital in the Netherlands. Simulation results indicate that in balanced capacity scenarios, static allocation achieves the highest performance when planning more than 4 weeks ahead. In contrast, in unbalanced systems, steering capacity toward patient groups incurring the highest costs yields better outcomes. The hybrid method offers flexibility as it can be adapted to both balanced and unbalanced situations. For the case study, we find that statically allocating 60% of timeslots and dynamically allocating the remainder 4 weeks in advance provides the best results in terms of meeting access time targets and efficient resource utilization. [ABSTRACT FROM AUTHOR] |
| Copyright of OR Spectrum is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Business Source Index |
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| Items | – Name: Title Label: Title Group: Ti Data: Timeslot allocation for waiting list control. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Vromans%2C+R%2E+F%2E+M%2E%22">Vromans, R. F. M.</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22van+Essen%2C+J%2E+T%2E%22">van Essen, J. T.</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> j.t.vanessen@tudelft.nl</i><br /><searchLink fieldCode="AR" term="%22van+der+Vlugt%2C+Y%2E+M%2E%22">van der Vlugt, Y. M.</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Carlier%2C+M%2E%22">Carlier, M.</searchLink><relatesTo>2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22OR+Spectrum%22">OR Spectrum</searchLink>. Sep2026, Vol. 48 Issue 3, p787-825. 39p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Markov+processes%22">Markov processes</searchLink><br />*<searchLink fieldCode="DE" term="%22Resource+allocation%22">Resource allocation</searchLink><br />*<searchLink fieldCode="DE" term="%22Scheduling%22">Scheduling</searchLink><br /><searchLink fieldCode="DE" term="%22Time%22">Time</searchLink><br /><searchLink fieldCode="DE" term="%22Outpatient+services+in+hospitals%22">Outpatient services in hospitals</searchLink><br /><searchLink fieldCode="DE" term="%22Reinforcement+learning%22">Reinforcement learning</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Netherlands%22">Netherlands</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: As pressure on the healthcare system increases, patients who require surgery experience longer access times to pre- and post-operative appointments and surgery. Hospitals can control their waiting lists by allocating timeslots to the different types of appointments they discern. To inform patients about their appointments in a timely manner, they need to make this decision several weeks in advance. However, the precise consequences of the timeslot allocation on the future waiting list are uncertain, as not all patients follow the same treatment pathway. Furthermore, as these planning decisions are made weeks in advance, they are based on an uncertain prediction of future waiting lists. In this paper, methods are developed with the aim to support hospitals in optimizing their timeslot allocation to reduce patient access times and utilize all available capacity in the outpatient department and operating room. The problem is modelled as a Markov decision process (MDP). However, as the state, decision and outcome spaces grow exponentially in size, even for a single surgeon, an exact solution cannot be determined. We thus compare four alternative solution methods to the static allocation method that is common in hospitals. Least-squares policy iteration is used to find an approximate solution, an (integer) linear program is formulated to solve a deterministic variant of the MDP, several heuristic decision rules are investigated, and a hybrid method is proposed that statically allocates a percentage of timeslots and then dynamically allocates the remaining timeslots with the linear program when sufficient information is available to effectively deal with variability. The solution methods are tested on a case study at the orthopedic care chain of the Sint Maartenskliniek hospital in the Netherlands. Simulation results indicate that in balanced capacity scenarios, static allocation achieves the highest performance when planning more than 4 weeks ahead. In contrast, in unbalanced systems, steering capacity toward patient groups incurring the highest costs yields better outcomes. The hybrid method offers flexibility as it can be adapted to both balanced and unbalanced situations. For the case study, we find that statically allocating 60% of timeslots and dynamically allocating the remainder 4 weeks in advance provides the best results in terms of meeting access time targets and efficient resource utilization. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of OR Spectrum is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s00291-026-00858-x Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 39 StartPage: 787 Subjects: – SubjectFull: Markov processes Type: general – SubjectFull: Resource allocation Type: general – SubjectFull: Scheduling Type: general – SubjectFull: Time Type: general – SubjectFull: Outpatient services in hospitals Type: general – SubjectFull: Reinforcement learning Type: general – SubjectFull: Netherlands Type: general Titles: – TitleFull: Timeslot allocation for waiting list control. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Vromans, R. F. M. – PersonEntity: Name: NameFull: van Essen, J. T. – PersonEntity: Name: NameFull: van der Vlugt, Y. M. – PersonEntity: Name: NameFull: Carlier, M. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 01716468 Numbering: – Type: volume Value: 48 – Type: issue Value: 3 Titles: – TitleFull: OR Spectrum Type: main |
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