Improving ASP-based ORS Schedules through Machine Learning Predictions

TL;DR

This paper enhances ASP-based operating room scheduling by integrating machine learning to predict surgery durations and improve schedule robustness, addressing limitations in current ASP solutions for real-world data.

Contribution

It introduces a novel integration of machine learning predictions with ASP to generate provisional and more robust OR schedules from historical data.

Findings

Machine learning accurately predicts surgery durations.

Predicted durations improve schedule robustness.

Integration confirms viability on real hospital data.

Abstract

The Operating Room Scheduling (ORS) problem deals with the optimization of daily operating room surgery schedules. It is a challenging problem subject to many constraints, like to determine the starting time of different surgeries and allocating the required resources, including the availability of beds in different department units. Recently, solutions to this problem based on Answer Set Programming (ASP) have been delivered. Such solutions are overall satisfying but, when applied to real data, they can currently only verify whether the encoding aligns with the actual data and, at most, suggest alternative schedules that could have been computed. As a consequence, it is not currently possible to generate provisional schedules. Furthermore, the resulting schedules are not always robust. In this paper, we integrate inductive and deductive techniques for solving these issues. We first employ machine learning algorithms to predict the surgery duration, from historical data, to compute provisional schedules. Then, we consider the confidence of such predictions as an additional input to our problem and update the encoding correspondingly in order to compute more robust schedules. Results on historical data from the ASL1 Liguria in Italy confirm the viability of our integration. Under consideration in Theory and Practice of Logic Programming (TPLP).