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An ASP-based Solution for Operating Room Scheduling with Beds Management

  • Carmine Dodaro
  • Giuseppe Galatà
  • Muhammad Kamran Khan
  • Marco MarateaEmail author
  • Ivan Porro
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11784)

Abstract

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 utmost importance for the quality of the health-care and the satisfaction of patients in hospital environments. In this paper we present an improved solution to the problem based on Answer Set Programming (ASP) that, differently from a recent one, takes explictly into account beds management. Results of an experimental analysis, conducted on benchmarks with realistic sizes and parameters, show that ASP is a suitable solving methodology for solving also such improved problem version.

References

  1. 1.
    Abedini, A., Ye, H., Li, W.: Operating room planning under surgery type and priority constraints. Procedia Manufact. 5, 15–25 (2016)CrossRefGoogle Scholar
  2. 2.
    Alviano, M., Dodaro, C., Maratea, M.: An advanced answer set programming encoding for nurse scheduling. In: Esposito, F., Basili, R., Ferilli, S., Lisi, F. (eds.) AI*IA 2017 Advances in Artificial Intelligence. LNCS, pp. 468–482. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-70169-1_35CrossRefGoogle Scholar
  3. 3.
    Alviano, M., Dodaro, C., Maratea, M.: Nurse (re)scheduling via answer set programming. Intelligenza Artificiale 12(2), 109–124 (2018)CrossRefGoogle Scholar
  4. 4.
    Alviano, M., Dodaro, C., Marques-Silva, J., Ricca, F.: Optimum stable model search: Algorithms and implementation. J. Log. Comput.  https://doi.org/10.1093/logcom/exv061 (in press)
  5. 5.
    Amendola, G.: Preliminary results on modeling interdependent scheduling games via answer set programming. In: RiCeRcA@AI*IA CEUR Workshop Proceedings, vol. 2272. CEUR-WS.org (2018)Google Scholar
  6. 6.
    Amendola, G.: Solving the stable roommates problem using incoherent answer set programs. In: RiCeRcA@AI*IA CEUR Workshop Proceedings, vol. 2272. CEUR-WS.org (2018)Google Scholar
  7. 7.
    Amendola, G., Dodaro, C., Leone, N., Ricca, F.: On the application of answer set programming to the conference paper assignment problem. In: Adorni, G., Cagnoni, S., Gori, M., Maratea, M. (eds.) AI*IA 2016. LNCS (LNAI), vol. 10037, pp. 164–178. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-49130-1_13CrossRefGoogle Scholar
  8. 8.
    Aringhieri, R., Landa, P., Soriano, P., Tànfani, E., Testi, A.: A two level metaheuristic for the operating room scheduling and assignment problem. Comput. Oper. Res. 54, 21–34 (2015)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Aringhieri, R., Landa, P., Tànfani, E.: Assigning surgery cases to operating rooms: A VNS approach for leveling ward beds occupancies. Electron. Notes Discrete Math. 47, 173–180 (2015).  https://doi.org/10.1016/j.endm.2014.11.023MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Baral, C.: Knowledge Representation, Reasoning and Declarative Problem Solving. Cambridge University Press, Cambridge (2003).  https://doi.org/10.1017/CBO9780511543357CrossRefzbMATHGoogle Scholar
  11. 11.
    Brewka, G., Eiter, T., Truszczynski, M.: Answer set programming at a glance. Commun. ACM 54(12), 92–103 (2011)CrossRefGoogle Scholar
  12. 12.
    Buccafurri, F., Leone, N., Rullo, P.: Enhancing disjunctive datalog by constraints. IEEE Trans. Knowl. Data Eng. 12(5), 845–860 (2000)CrossRefGoogle Scholar
  13. 13.
    Calimeri, F., et al.: ASP-Core-2 Input Language Format (2013). https://www.mat.unical.it/aspcomp2013/files/ASP-CORE-2.01c.pdf
  14. 14.
    Calimeri, F., Gebser, M., Maratea, M., Ricca, F.: Design and results of the fifth answer set programming competition. Artif. Intell. 231, 151–181 (2016)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Dodaro, C., Galatà, G., Maratea, M., Porro, I.: Operating room scheduling via answer set programming. In: Ghidini, C., Magnini, B., Passerini, A., Traverso, P. (eds.) AI*IA 2018. LNCS (LNAI), vol. 11298, pp. 445–459. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-03840-3_33CrossRefGoogle Scholar
  16. 16.
    Dodaro, C., Maratea, M.: Nurse scheduling via answer set programming. In: Balduccini, M., Janhunen, T. (eds.) LPNMR 2017. LNCS (LNAI), vol. 10377, pp. 301–307. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-61660-5_27CrossRefzbMATHGoogle Scholar
  17. 17.
    Faber, W., Pfeifer, G., Leone, N.: Semantics and complexity of recursive aggregates in answer set programming. Artif. Intell. 175(1), 278–298 (2011)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Gebser, M., Kaminski, R., Kaufmann, B., Ostrowski, M., Schaub, T., Wanko, P.: Theory solving made easy with clingo 5. In: ICLP (Technical Communications). OASICS, vol. 52, pp. 2:1–2:15. Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik (2016)Google Scholar
  19. 19.
    Gebser, M., Kaufmann, B., Schaub, T.: Conflict-driven answer set solving: from theory to practice. Artif. Intell. 187, 52–89 (2012)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Gebser, M., Maratea, M., Ricca, F.: The sixth answer set programming competition. J. Artif. Intell. Res. 60, 41–95 (2017)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Gelfond, M., Lifschitz, V.: The stable model semantics for logic programming. In: Proceedings of the Fifth International Conference and Symposium, Seattle, Washington, 15–19 August 1988, vol. 2, pp. 1070–1080. MIT Press (1988)Google Scholar
  22. 22.
    Gelfond, M., Lifschitz, V.: Classical negation in logic programs and disjunctive databases. New Gener. Comput. 9(3/4), 365–386 (1991)CrossRefGoogle Scholar
  23. 23.
    Giunchiglia, E., Maratea, M., Tacchella, A.: Dependent and independent variables in propositional satisfiability. In: Flesca, S., Greco, S., Ianni, G., Leone, N. (eds.) JELIA 2002. LNCS (LNAI), vol. 2424, pp. 296–307. Springer, Heidelberg (2002).  https://doi.org/10.1007/3-540-45757-7_25CrossRefzbMATHGoogle Scholar
  24. 24.
    Giunchiglia, E., Maratea, M., Tacchella, A.: (In)Effectiveness of look-ahead techniques in a modern SAT solver. In: Rossi, F. (ed.) CP 2003. LNCS, vol. 2833, pp. 842–846. Springer, Heidelberg (2003).  https://doi.org/10.1007/978-3-540-45193-8_64CrossRefGoogle Scholar
  25. 25.
    Landa, P., Aringhieri, R., Soriano, P., Tànfani, E., Testi, A.: A hybrid optimization algorithm for surgeries scheduling. Oper. Res. Health Care 8, 103–114 (2016)CrossRefGoogle Scholar
  26. 26.
    Molina-Pariente, J.M., Hans, E.W., Framinan, J.M., Gomez-Cia, T.: New heuristics for planning operating rooms. Comput. Ind. Eng. 90, 429–443 (2015)CrossRefGoogle Scholar
  27. 27.
    Niemelä, I.: Logic programs with stable model semantics as a constraint programming paradigm. Ann. Math. Artif. Intell. 25(3–4), 241–273 (1999)MathSciNetCrossRefGoogle Scholar
  28. 28.
    Ricca, F., et al.: Team-building with answer set programming in the Gioia-Tauro seaport. Theory Pract. Logic Program. 12(3), 361–381 (2012)MathSciNetCrossRefGoogle Scholar
  29. 29.
    Zhang, J., Dridi, M., El Moudni, A.: A stochastic shortest-path MDP model with dead ends for operating rooms planning. In: ICAC, pp. 1–6. IEEE (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Carmine Dodaro
    • 1
  • Giuseppe Galatà
    • 2
  • Muhammad Kamran Khan
    • 3
  • Marco Maratea
    • 3
    Email author
  • Ivan Porro
    • 2
  1. 1.DEMACSUniversity of CalabriaRendeItaly
  2. 2.SurgiQ srlGenovaItaly
  3. 3.DIBRISUniversity of GenovaGenovaItaly

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