用机器学习预测手术时长，提升手术排程的可行性与鲁棒性
Improving ASP-based ORS Schedules through Machine Learning Predictions
- 用历史数据训练模型预测手术时长，生成初步排程
- 将预测置信度作为约束输入，优化排程鲁棒性
- 适合医疗调度研究者和智能排程系统开发者
手术室调度（ORS）问题需优化每日手术安排，涉及手术起始时间、资源分配等多重约束。基于答案集编程（ASP）的解决方案虽有效，但仅能验证编码正确性，无法生成预排程，且结果缺乏鲁棒性。本文融合归纳与演绎方法：先利用机器学习从历史数据中预测手术时长，生成初步排程；再将预测置信度作为额外输入，动态更新ASP编码，生成更稳健的排程。在意大利拉斯1号医院（ASL1 Liguria）的历史数据上验证了该方法的有效性。
原文摘要 · Abstract (English)
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).
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