Home > Research > Publications & Outputs > Constructing operating theatre schedules using ...

Electronic data

  • HS-MSS

    Rights statement: This is an Accepted Manuscript of an article published by Taylor & Francis in Health Systems on 27/07/2020, available online:  https://www.tandfonline.com/doi/full/10.1080/20476965.2020.1796530

    Accepted author manuscript, 442 KB, PDF document

    Embargo ends: 27/07/21

    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

Links

Text available via DOI:

View graph of relations

Constructing operating theatre schedules using partitioned graph colouring techniques

Research output: Contribution to journalJournal article

E-pub ahead of print
Close
<mark>Journal publication date</mark>27/07/2020
<mark>Journal</mark>Health Systems
Number of pages12
Publication StatusE-pub ahead of print
Early online date27/07/20
<mark>Original language</mark>English

Abstract

In hospitals, scheduled operations can often be cancelled in large numbers due to the unavailability of beds for post-operation recovery. Operating theatre scheduling is known to be an (Formula presented.) -hard optimisation problem. Previous studies have shown that the correct scheduling of surgical procedures can have a positive impact on the availability of beds in hospital wards, thereby allowing a reduction in number of elective operation cancellations. This study proposes an exact technique based on the partitioned graph colouring problem for constructing optimal master surgery schedules, with the goal of minimising the number of cancellations. The resultant schedules are then simulated in order to measure how well they cope with the stochastic nature of patient arrivals. Our results show that the utilisation of post-operative beds can be increased, whilst the number of cancellations can be decreased, which may ultimately lead to greater patient throughput and reduced waiting times. A scenario-based model has also been employed to integrate the stochastic-nature associated with the bed requirements into the optimisation process. The results indicate that the proposed model can lead to more robust solutions.

Bibliographic note

This is an Accepted Manuscript of an article published by Taylor & Francis in Health Systems on 27/07/2020, available online:  https://www.tandfonline.com/doi/full/10.1080/20476965.2020.1796530