Every day, hospital staff do the best they can to navigate the daily chaos of bed management by making educated guesses as to what is going to happen over the course of the day. Relying on team huddles throughout the day, staff pore over Excel or paper spreadsheets to predict how many beds will open up and when. They try to estimate demand for those beds by the time of day, unsure when to deploy "surge capacity." On some days, this method works out well. However, more often than not, the staff's best efforts result in long patient waits, unwanted staff overtime, and ultimately lower access to care.

The Operating Room Scheduling (ORS) problem is the task of assigning patients to operating rooms, taking into account different specialties, lengths and priority scores of each planned surgery, operating room session durations, and the availability of beds for the entire length of stay both in the Intensive Care Unit and in the wards. A proper solution to the ORS problem is of primary importance for the healthcare service quality and the satisfaction of patients in hospital environments. In this paper we first present a solution to the problem based on Answer Set Programming (ASP). The solution is tested on benchmarks with realistic sizes and parameters, on three scenarios for the target length on 5-day scheduling, common in small-medium sized hospitals, and results show that ASP is a suitable solving methodology for the ORS problem in such setting. Then, we also performed a scalability analysis on the schedule length up to 15 days, which still shows the suitability of our solution also on longer plan horizons. Moreover, we also present an ASP solution for the rescheduling problem, i.e. when the off-line schedule cannot be completed for some reason. Finally, we introduce a web framework for managing ORS problems via ASP that allows a user to insert the main parameters of the problem, solve a specific instance, and show results graphically in real-time.