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Robust optimisation of operating theatre schedules

Rowse, Elizabeth Louise 2015. Robust optimisation of operating theatre schedules. PhD Thesis, Cardiff University.
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Abstract

Hospitals in the UK are increasingly having to cancel a large proportion of elective operations due to the unavailability of beds on hospital wards for post-operative recovery. The availability of post-operative beds is therefore critical to the scheduling of surgical procedures and the throughput of patients in a hospital. The focus of this research is to investigate, via data-driven modelling, systematic reasons for the unavailability of beds and to demonstrate how the Master Surgery Schedule (MSS) can be constructed using Operational Research techniques to minimise the number of cancellations of elective operations. Statistical analysis of data provided by the University Hospital of Wales, Cardiff was performed, providing information on patient demand and length of stay distributions. A two-stage modelling process was developed to construct and simulate an MSS that minimises the number of cancellations. The first stage involves a novel set partitioning based optimisation model that incorporates operating room and bed constraints. The second stage simulates the resulting optimal schedule to provide measures on how well the schedule would perform if implemented. The results from this two-stage model provide insights into when best to schedule surgical specialties and how best the beds are distributed between wards. Two optimisation under uncertainty techniques are then employed to incorporate the uncertainty associated with the bed requirements into the optimisation process. A robust optimisation (RO) approach that uses protection functions in each bed constraint is developed. Investigations into varying levels of protection are performed in order to gain insight into the so called `price of robustness'. Results show that MSSs that are constructed from protecting more of the uncertainty result in fewer cancellations and a smaller probability of requiring more beds than are available. The deterministic optimisation model is then extended to become a scenario-based optimisation model in which more scenarios of bed requirement are incorporated into a single optimisation model. Results show that as more scenarios are included, a more robust schedule is generated and fewer cancellations are expected. Results from the different approaches are compared to assess the benefits of using RO techniques. Future research directions following from this work are discussed, including the construction of the MSS based on sub-specialties and investigation of different working practices within the case study hospital.

Item Type: Thesis (PhD)
Status: Unpublished
Schools: Mathematics
Subjects: Q Science > QA Mathematics
Uncontrolled Keywords: Robust optimisation, healthcare, set partitioning
Last Modified: 08 Nov 2016 05:47
URI: http://orca.cf.ac.uk/id/eprint/80684

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