Top

Published in:

2020 | OriginalPaper | Chapter

Learning Probabilistic Constraints for Surgery Scheduling Using a Support Vector Machine

Author : Thomas Philip Runarsson

Published in: Learning and Intelligent Optimization

Publisher: Springer International Publishing

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

The problem of generating surgery schedules is formulated as a mathematical model with probabilistic constraints. The approach presented is a new method for tackling probabilistic constraints using machine learning. The technique is inspired by models that use slacks in capacity planning. Essentially support vector classification is used to learn a linear constraint that will replace the probabilistic constraint. The data used to learn this constraint is labeled using Monte Carlo simulations. This data is iteratively discovered, during the optimization procedure, and augmented to the training set. The linear support vector classifier is then updated during search, until a feasible solution is discovered. The stochastic surgery model presented is inspired by real challenges faced by many hospitals today and tested on real-life data.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

previous chapter Irreducible Bin Packing: Complexity, Solvability and Application to the Routing Open Shop

next chapter Exact Algorithm for One Cardinality-Weighted 2-Partitioning Problem of a Sequence

Cappanera, P., Visintin, F., Banditori, C.: Addressing conflicting stakeholders’ priorities in surgical scheduling by goal programming. Flex. Serv. Manuf. J. 30(1), 252–271 (2018). https://doi.org/10.1007/s10696-016-9255-5CrossRef

Cardoen, B., Demeulemeester, E., Beliën, J.: Operating room planning and scheduling: a literature review. Eur. J. Oper. Res. 201(3), 921–932 (2010). https://doi.org/10.1016/j.ejor.2009.04.011. http://www.sciencedirect.com/science/article/pii/S0377221709002616CrossRefMATH

Denton, B., Miller, A.J., Balasubramanian, H.J., Huschka, T.R.: Optimalallocation of surgery blocks to operating rooms under uncertainty. Oper. Res. 58(4–part–1), 802–816 (2010). https://doi.org/10.1287/opre.1090.0791MathSciNetCrossRefMATH

Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: LIBLINEAR: a library for large linear classification. J. Mach. Learn. Res. 9(Aug), 1871–1874 (2008)MATH

Guido, R., Conforti, D.: A hybrid genetic approach for solving an integrated multi-objective operating room planning and scheduling problem. Comput. Oper. Res. 87(Supplement C), 270–282 (2017). https://doi.org/10.1016/j.cor.2016.11.009. http://www.sciencedirect.com/science/article/pii/S0305054816302684MathSciNetCrossRefMATH

Gurobi Optimization, Inc.: Gurobi optimizer reference manual (2018). http://www.gurobi.com

Hans, E., Wullink, G., van Houdenhoven, M., Kazemier, G.: Robust surgery loading. Eur. J. Oper. Res. 185(3), 1038–1050 (2008). https://doi.org/10.1016/j.ejor.2006.08.022.<Go to ISI>://WOS:000251070500011CrossRefMATH

Kroer, L.R., Foverskov, K., Vilhelmsen, C., Hansen, A.S., Larsen, J.: Planning and scheduling operating rooms for elective and emergency surgeries with uncertain duration. Oper. Res. Health Care (2018). https://doi.org/10.1016/j.orhc.2018.03.006CrossRef

Lamiri, M., Xie, X., Dolgui, A., Grimaud, F.: A stochastic model for operating room planning with elective and emergency demand for surgery. Eur. J. Oper. Res. 185(3), 1026–1037 (2008). https://doi.org/10.1016/j.ejor.2006.02.057. http://www.sciencedirect.com/science/article/pii/S0377221706005832MathSciNetCrossRefMATH

10.

Marques, I., Captivo, M.E.: Different stakeholders’ perspectives for a surgical case assignment problem: deterministic and robust approaches. Eur. J. Oper. Res. 261(1), 260–278 (2017). https://doi.org/10.1016/j.ejor.2017.01.036. http://www.sciencedirect.com/science/article/pii/S0377221717300711MathSciNetCrossRefMATH

11.

Min, D., Yih, Y.: Scheduling elective surgery under uncertainty and downstream capacity constraints. Eur. J. Oper. Res. 206(3), 642–652 (2010)MathSciNetCrossRef

12.

Molina-Pariente, J.M., Hans, E.W., Framinan, J.M.: A stochastic approach for solving the operating room scheduling problem. Flex. Serv. Manuf. J. (2016). https://doi.org/10.1007/s10696-016-9250-xCrossRef

13.

Neyshabouri, S., Berg, B.P.: Two-stage robust optimization approach to elective surgery and downstream capacity planning. Eur. J. Oper. Res. 260(1), 21–40 (2017). https://doi.org/10.1016/j.ejor.2016.11.043. http://www.sciencedirect.com/science/article/pii/S0377221716309687MathSciNetCrossRefMATH

14.

van Oostrum, J.M., Van Houdenhoven, M., Hurink, J.L., Hans, E.W., Wullink, G., Kazemier, G.: A master surgical scheduling approach for cyclic scheduling in operating room departments. OR Spectr. 30(2), 355–374 (2008). https://doi.org/10.1007/s00291-006-0068-xMathSciNetCrossRefMATH

15.

Riise, A., Mannino, C., Burke, E.K.: Modelling and solving generalised operational surgery scheduling problems. Comput. Oper. Res. 66, 1–11 (2016)MathSciNetCrossRef

16.

Samudra, M., Van Riet, C., Demeulemeester, E., Cardoen, B., Vansteenkiste, N., Rademakers, F.E.: Scheduling operating rooms: achievements, challenges and pitfalls. J. Sched. 19(5), 493–525 (2016). https://doi.org/10.1007/s10951-016-0489-6MathSciNetCrossRefMATH

17.

Spangler, W.E., Strum, D.P., Vargas, L.G., May, J.H.: Estimating procedure times for surgeries by determining location parameters for the lognormal model. Health Care Manag. Sci. 7(2), 97–104 (2004)CrossRef

Title: Learning Probabilistic Constraints for Surgery Scheduling Using a Support Vector Machine
Author: Thomas Philip Runarsson
Publisher: Springer International Publishing
Book: Learning and Intelligent Optimization
Print ISBN: 978-3-030-38628-3

Electronic ISBN: 978-3-030-38629-0

Copyright Year: 2020
DOI: https://doi.org/10.1007/978-3-030-38629-0_10

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner