<![CDATA[Operating room scheduling is a complex problem due to the limited availability of surgeons, nurses, and operating rooms, as well as the variability in surgery durations. Inaccurate predictions or scheduling may cause conflicts such as overlapping surgeon schedules, violations of contamination level restrictions, and unavailability of nurses or rooms, ultimately reducing the quality of hospital services. This study integrates multiprocedure surgery duration prediction using machine learning with scheduling optimization based on genetic algorithms. The prediction model considers the American Society of Anesthesiologists (ASA) physical status classification, patient profiles, and sets of surgical procedures variables. Scheduling optimization employs a lexicographic approach with three main objectives: minimizing patient waiting time, nurse overtime, and operating room idle time, while ensuring surgeon presence during critical phases and nurse availability according to shifts. The results show that the Catboost algorithm achieves the best prediction performance. Incorporating the ASA variable reduces prediction errors by 33.880 minutes in MAE and 55.575 minutes in RMSE compared to model without the ASA feature. The optimization model successfully eliminates all scheduling conflicts, ensuring full compliance with medical procedure constraints. Recovery bed utilization remains efficient, with a maximum of five units used, representing less than 50% of the total capacity.]]>
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