Operating Room Scheduling via Answer Set Programming

Dodaro, Carmine; Galatà, Giuseppe; Maratea, Marco; Porro, Ivan

doi:10.1007/978-3-030-03840-3_33

Cited by 9 publications

(13 citation statements)

References 14 publications

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“…In this section we report the results of an empirical analysis of the CTS problem. Data are real world data from the San Martino Hospital for the CTS problem, while they have been randomly generated using parameters inspired by literature and real world data for the extended problem (e.g., from the work of Dodaro et al (2018) for the distribution of priorities, and from the Oncology Nurses Society for the number of nurses per patient). The generation of synthetic data for the extended problem is needed since real world data from the San Martino Hospital do not cover all the features, e.g., nurses and priorities, that we considered in the extended problem.…”

Section: Resultsmentioning

confidence: 99%

“…Thus, in addition to the requirements defined in Section 2, the solution scheduling has to guarantee that (i) each patient is assisted by exactly one nurse (Turkcan et al 2012); (ii) each nurse can assist from 1 to k patients for each time slot (Turkcan et al 2012); (iii) treatments cannot exceed the maximum quantity of drugs available for each day (Heshmat and Eltawil 2021); (iv) treatments cannot be scheduled at the latest available time slot, since some drugs might require a long time to be prepared (Huggins et al 2014); registrations with the highest priorities should be scheduled before other registrations (Dodaro et al 2018).…”

Section: Extended Ctsmentioning

confidence: 99%

“…Moreover, we consider a planning horizon of one week other than the daily actually employed in the hospital. Then, we enrich the problem and the related ASP encoding considering features often employed in other hospitals, considered in related papers (Turkcan et al 2012;Turkcan et al 2012;Heshmat and Eltawil 2021;Huggins et al 2014;Dodaro et al 2018), and/or desired by the S. Martino Hospital, i.e., we explicitly consider the availability of nurses and drugs or a limit to the starting time of treatments (Section 3.2). Both encoding are evaluated on real data of the San Martino Hospital (Section 4): results using the state-of-the-art ASP solver CLINGO (Gebser et al 2012) show that ASP is an effective solving methodology for solving all these variants of the presented CTS problem.…”

Section: Introductionmentioning

confidence: 99%

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An ASP-based Solution to the Chemotherapy Treatment Scheduling problem

Dodaro¹,

Galatà²,

Grioni³

et al. 2021

Preprint

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The problem of scheduling chemotherapy treatments in oncology clinics is a complex problem, given that the solution has to satisfy (as much as possible) several requirements such as the cyclic nature of chemotherapy treatment plans, maintaining a constant number of patients, and the availability of resources, e.g., treatment time, nurses, and drugs. At the same time, realizing a satisfying schedule is of upmost importance for obtaining the best health outcomes.In this paper we first consider a specific instance of the problem which is employed in the San Martino Hospital in Genova, Italy, and present a solution to the problem based on Answer Set Programming (ASP). Then, we enrich the problem and the related ASP encoding considering further features often employed in other hospitals, desirable also in S. Martino, and/or considered in related papers. Results of an experimental analysis, conducted on the real data provided by the San Martino Hospital, show that ASP is an effective solving methodology also for this important scheduling problem. Under consideration for acceptance in TPLP.

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Section: Resultsmentioning

confidence: 99%

Section: Extended Ctsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An ASP-based Solution to the Chemotherapy Treatment Scheduling problem

Dodaro¹,

Galatà²,

Grioni³

et al. 2021

Preprint

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“…Some problems could be extensions of already presented problems such as the Operating Room Scheduling. For example, starting from works such as [10], we extended it to enlarge the scope of the solver by including the management of the beds in special care units, for example, patients that have had complicated surgery would need a bed in specific areas, and the solver should assign the day of surgery considering also the bed available in these areas. Other special care units could be added too, e.g care units that are needed by infants or particularly ill patients.…”

Section: Goal Of the Research 31 Healthcare Domain Problemsmentioning

confidence: 99%

Planning and Scheduling in Digital Health with Answer Set Programming

Mochi¹

2022

Electron. Proc. Theor. Comput. Sci.

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In the hospital world there are several complex combinatory problems, and solving these problems is important to increase the degree of patients' satisfaction and the quality of care offered. The problems in the healthcare are complex since to solve them several constraints and different type of resources should be taken into account. Moreover, the solutions must be evaluated in a small amount of time to ensure the usability in real scenarios. We plan to propose solutions to these kind of problems both expanding already tested solutions and by modelling solutions for new problems, taking into account the literature and by using real data when available. Solving these kind of problems is important but, since the European Commission established with the General Data Protection Regulation that each person has the right to ask for explanation of the decision taken by an AI, without developing Explainability methodologies the usage of AI based solvers e.g. those based on Answer Set programming will be limited. Thus, another part of the research will be devoted to study and propose new methodologies for explaining the solutions obtained.

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“…We already mentioned in the introduction that ASP has been already successfully used for solving hard combinatorial and application problems in several research areas. Concerning ORS, the problem has been already addressed in Dodaro et al . (2018) and 2019, but without taking into account beds, which instead are a fundamental resource to be considered.…”

Section: Related Workmentioning

confidence: 99%

Operating Room (Re)Scheduling with Bed Management via ASP

Dodaro¹,

Galatà²,

Khan³

et al. 2021

Theory and Practice of Logic Programming

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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.

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Operating Room Scheduling via Answer Set Programming

Cited by 9 publications

References 14 publications

An ASP-based Solution to the Chemotherapy Treatment Scheduling problem

An ASP-based Solution to the Chemotherapy Treatment Scheduling problem

Planning and Scheduling in Digital Health with Answer Set Programming

Operating Room (Re)Scheduling with Bed Management via ASP

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