Hostname: page-component-8448b6f56d-c4f8m Total loading time: 0 Render date: 2024-04-18T20:23:17.389Z Has data issue: false hasContentIssue false

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: dodaro@mat.unical.it)
GIUSEPPE GALATÀ
Affiliation:
SurgiQ srl, Genova, Italy (e-mail: giuseppe.galata@surgiq.com)
MUHAMMAD KAMRAN KHAN
Affiliation:
University of Genoa, Genova, Italy (e-mails: muhammad.kamrankhan@edu.unige.it, marco.maratea@unige.it)
MARCO MARATEA
Affiliation:
University of Genoa, Genova, Italy (e-mails: muhammad.kamrankhan@edu.unige.it, marco.maratea@unige.it)
IVAN PORRO
Affiliation:
SurgiQ srl, Genova, Italy (e-mail: ivan.porro@surgiq.com)

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

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

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.

References

Abedini, A., Ye, H. and Li, W. 2016. Operating room planning under surgery type and priority constraints. Procedia Manufacturing 5, 15–25.Google Scholar
Alviano, M., Amendola, G., Dodaro, C., Leone, N., Maratea, M. and Ricca, F. 2019. Evaluation of disjunctive programs in WASP. In Proceedings of the 15th International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR 2019), M. Balduccini, Y. Lierler, and S. Woltran, Eds. Lecture Notes in Computer Science, vol. 11481. Springer, 241–255.Google Scholar
Alviano, M., Dodaro, C. and Maratea, M. 2017. An advanced answer set programming encoding for nurse scheduling. In Advances in Artificial Intelligence - Proceedings of the 16th International Conference of the Italian Association for Artificial Intelligence (AI*IA 2017), F. Esposito, R. Basili, S. Ferilli, and F. A. Lisi, Eds. Lecture Notes in Computer Science, vol. 10640. Springer, 468–482.Google Scholar
Alviano, M., Dodaro, C. and Maratea, M. 2018. Nurse (re)scheduling via answer set programming. Intelligenza Artificiale 12, 2, 109124.Google Scholar
Alviano, M., Dodaro, C., Marques-Silva, J. and Ricca, F. 2020. Optimum stable model search: Algorithms and implementation. Journal of Logic and Computation 30, 4. In press.CrossRefGoogle Scholar
Alviano, M., Faber, W. and Gebser, M. 2015. Rewriting recursive aggregates in answer set programming: Back to monotonicity. Theory and Practice of Logic Programming 15, 4–5, 559573.CrossRefGoogle Scholar
Amendola, G. 2018. Preliminary results on modeling interdependent scheduling games via answer set programming. In RiCeRcA@AI*IA. CEUR Workshop Proceedings, vol. 2272. CEUR-WS.org.Google Scholar
Amendola, G., Dodaro, C., Leone, N. and Ricca, F. 2016. On the application of answer set programming to the conference paper assignment problem. In Advances in Artificial Intelligence - Proceedings of the 15th International Conference of the Italian Association for Artificial Intelligence (AI*IA 2016), G. Adorni, S. Cagnoni, M. Gori, and M. Maratea, Eds. Lecture Notes in Computer Science, vol. 10037. Springer, 164–178.Google Scholar
Aringhieri, R., Landa, P., Soriano, P., Tànfani, E. and Testi, A. 2015. A two level metaheuristic for the operating room scheduling and assignment problem. Computers & Operations Research 54, 21–34.Google Scholar
Aringhieri, R., Landa, P. and Tànfani, E. 2015. Assigning surgery cases to operating rooms: A vns approach for leveling ward beds occupancies. In Proceedings of the 3rd International Conference on Variable Neighborhood Search (VNS 2014). Electronic Notes in Discrete Mathematics 47, 173–180.Google Scholar
Balduccini, M. 2011. Industrial-size scheduling with ASP+CP. In Logic Programming and Nonmonotonic Reasoning - 11th International Conference, LPNMR 2011, Vancouver, Canada, May 16–19, 2011. Proceedings. Lecture Notes in Computer Science, vol. 6645. Springer, 284–296.Google Scholar
Baral, C. 2003. Knowledge Representation, Reasoning and Declarative Problem Solving. Cambridge University Press.CrossRefGoogle Scholar
Brewka, G., Eiter, T. and Truszczynski, M. 2011. Answer set programming at a glance. Communications of the ACM 54, 12, 92103.CrossRefGoogle Scholar
Buccafurri, F., Leone, N. and Rullo, P. 2000. Enhancing disjunctive datalog by constraints. IEEE Transactions on Knowledge and Data Engineering 12, 5, 845860.CrossRefGoogle Scholar
Calimeri, F., Faber, W., Gebser, M., Ianni, G., Kaminski, R., Krennwallner, T., Leone, N., Maratea, M., Ricca, F. and Schaub, T. 2020. ASP-Core-2 input language format. Theory and Practice of Logic Programming 20, 2, 294309.CrossRefGoogle Scholar
Calimeri, F., Faber, W., Gebser, M., Ianni, G., Kaminski, R., Krennwallner, T., Leone, N., Ricca, F. and Schaub, T. 2013. ASP-Core-2 Input Language Format. https://www.mat.unical.it/aspcomp2013/files/ASP-CORE-2.03c.pdf Google Scholar
Calimeri, F., Gebser, M., Maratea, M. and Ricca, F. 2016. Design and results of the Fifth Answer Set Programming Competition. Artificial Intelligence 231, 151–181.Google Scholar
Dodaro, C., Galatà, G., Khan, M. K., Maratea, M. and Porro, I. 2019. An ASP-based solution for operating room scheduling with beds management. In Proceedings of the Third International Joint Conference on Rules and Reasoning (RuleML+RR 2019), P. Fodor, M. Montali, D. Calvanese, and D. Roman, Eds. Lecture Notes in Computer Science, vol. 11784. Springer, 67–81.Google Scholar
Dodaro, C., Galatà, G., Maratea, M. and Porro, I. 2018. Operating room scheduling via answer set programming. In Advances in Artificial Intelligence - Proceedings of the 17th International Conference of the Italian Association for Artificial Intelligence (AI*IA 2018), C. Ghidini, B. Magnini, A. Passerini, and P. Traverso, Eds. Lecture Notes in Computer Science, vol. 11298. Springer, 445–459.Google Scholar
Dodaro, C., Galatà, G., Maratea, M. and Porro, I. 2019. An ASP-based framework for operating room scheduling. Intelligenza Artificiale 13, 1, 6377.CrossRefGoogle Scholar
Dodaro, C. and Maratea, M. 2017. Nurse scheduling via answer set programming. In Proceedings of the 14th International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR 2017), M. Balduccini and T. Janhunen, Eds. Lecture Notes in Computer Science, vol. 10377. Springer, 301–307.Google Scholar
Faber, W., Pfeifer, G. and Leone, N. 2011. Semantics and complexity of recursive aggregates in answer set programming. Artificial Intelligence 175, 1, 278298.CrossRefGoogle Scholar
Falkner, A. A., Friedrich, G., Schekotihin, K., Taupe, R. and Teppan, E. C. 2018. Industrial applications of answer set programming. Knstliche Intelligenz 32, 2–3, 165176.CrossRefGoogle Scholar
Ferraris, P. 2011. Logic programs with propositional connectives and aggregates. ACM Transactions on Computational Logic 12, 4, 25.Google Scholar
Gebser, M., Harrison, A., Kaminski, R., Lifschitz, V. and Schaub, T. 2015. Abstract gringo. Theory Practice of Logic Programming 15, 4–5, 449463.CrossRefGoogle Scholar
Gebser, M., Kaminski, R., Kaufmann, B., Ostrowski, M., Schaub, T. and Wanko, P. 2016. Theory solving made easy with clingo 5. In Proceedings of ICLP (Technical Communications), M. Carro, A. King, N. Saeedloei, and M. D. Vos, Eds. OASICS, vol. 52. Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, 2:1–2:15.Google Scholar
Gebser, M., Kaufmann, B. and Schaub, T. 2012. Conflict-driven answer set solving: From theory to practice. Artificial Intelligence 187, 52–89.Google Scholar
Gebser, M., Maratea, M. and Ricca, F. 2017a. The design of the seventh answer set programming competition. In LPNMR, M. Balduccini and T. Janhunen, Eds. Lecture Notes in Computer Science, vol. 10377. Springer, 3–9.Google Scholar
Gebser, M., Maratea, M. and Ricca, F. 2017b. The sixth answer set programming competition. Journal of Artificial Intelligence Research 60, 41–95.Google Scholar
Gebser, M., Obermeier, P., Schaub, T., Ratsch-Heitmann, M. and Runge, M. 2018. Routing driverless transport vehicles in car assembly with answer set programming. Theory Practice of Logic Programming 18, 3–4, 520534.CrossRefGoogle Scholar
Gelfond, M. and Lifschitz, V. 1988. The stable model semantics for logic programming. In Proceedings of the Fifth International Conference and Symposium (ICLP/SLP 1988) (2 Volumes). MIT Press, 1070–1080.Google Scholar
Gelfond, M. and Lifschitz, V. 1991. Classical negation in logic programs and disjunctive databases. New Generation Computing 9, 3/4, 365–386.Google Scholar
Giunchiglia, E., Maratea, M. and Tacchella, A. 2002. Dependent and independent variables in propositional satisfiability. In Proceedings of the European Conference on Logics in Artificial Intelligence (JELIA 2002), S. Flesca, S. Greco, N. Leone, and G. Ianni, Eds. Lecture Notes in Computer Science, vol. 2424. Springer, 296–307.Google Scholar
Giunchiglia, E., Maratea, M. and Tacchella, A. 2003. (In)Effectiveness of look-ahead techniques in a modern SAT solver. In Proceedings of the 9th International Conference on Principles and Practice of Constraint Programming (CP 2003), F. Rossi, Ed. Lecture Notes in Computer Science, vol. 2833. Springer, 842–846.Google Scholar
Landa, P., Aringhieri, R., Soriano, P., Tànfani, E. and Testi, A. 2016. A hybrid optimization algorithm for surgeries scheduling. Operations Research for Health Care 8, 103–114.Google Scholar
Molina-Pariente, J. M., Hans, E. W., Framinan, J. M. and Gomez-Cia, T. 2015. New heuristics for planning operating rooms. Computers & Industrial Engineering 90, 429–443.Google Scholar
Niemelä, I. 1999. Logic Programs with stable model semantics as a constraint programming paradigm. Annals of Mathematics and Artificial Intelligence 25, 3-4, 241273.CrossRefGoogle Scholar
Ricca, F., Grasso, G., Alviano, M., Manna, M., Lio, V., Iiritano, S. and Leone, N. 2012. Team-building with answer set programming in the Gioia-Tauro seaport. Theory and Practice of Logic Programming 12, 3, 361–381.Google Scholar
Rosa, E. D., Giunchiglia, E. and Maratea, M. 2008. A new approach for solving satisfiability problems with qualitative preferences. In ECAI, M. Ghallab, C. D. Spyropoulos, N. Fakotakis, and N. M. Avouris, Eds. Frontiers in Artificial Intelligence and Applications, vol. 178. IOS Press, 510–514.Google Scholar
Zhang, J., Dridi, M. and Moudni, A. E. 2017. A stochastic shortest-path MDP model with dead ends for operating rooms planning. In Proceedings of the 23rd International Conference on Automation and Computing (ICAC 2017). IEEE, 1–6.Google Scholar