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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.25357 |
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Table of Contents:
- Uncertainty in surgery durations continues to be difficult to account for in operating room scheduling. In particular, it remains complex to accurately incorporate uncertainty in surgical overtime constraints within mixed-integer linear programming (MILP) models. Therefore, we propose a method that integrates feedforward neural networks (FNNs) into MILP models to approximate the total surgery duration in these overtime constraints. The proposed approach is evaluated using real-life hospital data and compared against two classical approaches: scenario-based modelling and piecewise linear function approximations. We demonstrate that with a relatively small FNN, we achieve competitive operating room schedules in terms of both solution quality and computational performance. The FNN-based approach is the most computationally efficient with an optimality gap lower than 2% in all cases, achieves the highest operating room utilization in six out of eight considered cases, and on average produces simulated overtime probabilities closest to the predefined target.