University of Twente Student Theses
Robust surgery loading using mixture distributions
Gonzalez Sanabria, Emma (2024) Robust surgery loading using mixture distributions.
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| Abstract: | This paper examines the impact of applying mixture distributions to surgery scheduling while addressing uncertainty in surgery durations. Slack time is introduced as an additional backup time, beyond the expected sum of surgery durations for each operating room (OR), making schedules more robust. We compare two different methods to calculate slack time: one, where surgery durations are assumed to follow a normal distribution, and another where surgeries follow a mixture distribution of normal random variables. We apply these models to three different scheduling methods to analyze their performance. Simulations have been conducted to test the implementation of the different schedules produced, utilizing a generated waiting list from real data. We find that the schedules produced using mixture distributions generally reduce total overtime, but may lead to a less efficient OR utilization. The regret-based random sampling method applied to the mixture distribution model effectively minimizes overtime and improves scheduling outcomes in terms of overtime. However, it highlights a trade-off between operating room availability and overtime. |
| Item Type: | Essay (Bachelor) |
| Faculty: | EEMCS: Electrical Engineering, Mathematics and Computer Science |
| Subject: | 31 mathematics, 44 medicine |
| Programme: | Applied Mathematics BSc (56965) |
| Link to this item: | https://purl.utwente.nl/essays/101312 |
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