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Operating Room (Re)Scheduling with Bed Management via ASP

arXiv 2105.02283 ↗First posted 5 May 2021cs.AI cs.LOcited by 4

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Abstract (arXiv)

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. Under consideration in Theory and Practice of Logic Programming (TPLP).

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 3 of its 44 references that are in the map. First posted on arXiv 5 May 2021.

Builds on

3 of 44 references.

References · 3

  1. 2019ASP-Core-2 Input Language Formatcited by 77
  2. 2018Routing Driverless Transport Vehicles in Car Assembly with Answer Set Programmingcited by 7
  3. 2014The Design of the Fifth Answer Set Programming Competitioncited by 2

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Citing papers per year: 2023: 2; 2024: 0; 2025: 2.

4 papers.