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Operating Room (Re)Scheduling with Bed Management via ASP

Published online by Cambridge University Press:  14 July 2021

CARMINE DODARO
Affiliation:
University of Calabria, Genova, Italy (e-mail: [email protected])
GIUSEPPE GALATÀ
Affiliation:
SurgiQ srl, Genova, Italy (e-mail: [email protected])
MUHAMMAD KAMRAN KHAN
Affiliation:
University of Genoa, Genova, Italy (e-mails: [email protected], [email protected])
MARCO MARATEA
Affiliation:
University of Genoa, Genova, Italy (e-mails: [email protected], [email protected])
IVAN PORRO
Affiliation:
SurgiQ srl, Genova, Italy (e-mail: [email protected])

Abstract

The Operating Room Scheduling (ORS) problem is the task of assigning patients to operating rooms (ORs), taking into account different specialties, lengths, and priority scores of each planned surgery, OR session durations, and the availability of beds for the entire length of stay (LOS) both in the Intensive Care Unit (ICU) 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, that is, when the offline 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.

Type
Original Article
Copyright
© The Author(s), 2021. Published by Cambridge University Press

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Footnotes

*

This paper is an extended and revised version of a conference paper appearing in the proceedings of the 3rd International Joint Conference on Rules and Reasoning (RuleML+RR 2019) Dodaro et al. (2019).

Disclaimer: Two of the authors of this paper, Ivan Porro and Giuseppe Galatà, have business interest in SurgiQ.

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