Journal of Scheduling

, Volume 19, Issue 4, pp 377–389 | Cite as

Dynamic patient admission scheduling with operating room constraints, flexible horizons, and patient delays

  • Sara Ceschia
  • Andrea Schaerf


We revisit and extend the patient admission scheduling problem, in order to make it suitable for practical applications. The main novelty is that we consider constraints on the utilisation of operating rooms for patients requiring a surgery. In addition, we propose a more elaborate model that includes a flexible planning horizon, a complex notion of patient delay, and new components of the objective function. We design a solution approach based on local search, which explores the search space using a composite neighbourhood. In addition, we develop an instance generator that uses real-world data and statistical distributions so as to synthesise realistic and challenging case studies, which are made available on the web along with our solutions and the validator. Finally, we perform an extensive experimental evaluation of our solution method including statistically principled parameter tuning and an analysis of some features of the model and their corresponding impact on the objective function.


Patient admission scheduling Patient bed assignment  Operating rooms Local search 



We thank Paolo Sivilotti for proof-reading the paper, and providing some useful comments. Sara Ceschia acknowledges support from Consorzio per l’AREA di ricerca scientifica e tecnologica di Trieste and Fondo Sociale Europeo in Friuli Venezia Giulia under the program “S.H.A.R.M.— Supporting Human Assets in Research and Mobility”.


  1. Beliën, J., & Demeulemeester, E. (2007). Building cyclic master surgery schedules with leveled resulting bed occupancy. European Journal of Operational Research, 176(2), 1185–1204.CrossRefGoogle Scholar
  2. Beliën, J., Demeulemeester, E., & Cardoen, B. (2008). A decision support system for cyclic master surgery scheduling with multiple objectives. Journal of Scheduling, 12(2), 147–161.CrossRefGoogle Scholar
  3. Bilgin, B., Demeester, P., Misir, M., Vancroonenburg, W., & Vanden Berghe, G. (2012). One hyper-heuristic approach to two timetabling problems in health care. Journal of Heuristics, 18(3), 401–434.CrossRefGoogle Scholar
  4. Birattari, M. (2004). The problem of tuning metaheuristics as seen from a machine learning perspective. PhD thesis, Université Libre de Bruxelles, Belgium.Google Scholar
  5. Blake, J. T., & Carter, M. W. (1997). Surgical process scheduling: a structured review. Journal of the Society for Health Systems, 5(3), 17–30.Google Scholar
  6. Cardoen, B., Demeulemeester, E., & Beliën, J. (2010). Operating room planning and scheduling: A literature review. European Journal of Operational Research, 201(3), 921–932.CrossRefGoogle Scholar
  7. Ceschia, S., & Schaerf, A. (2011). Local search and lower bounds for the patient admission scheduling problem. Computers & Operations Research, 38(10), 1452–1463.CrossRefGoogle Scholar
  8. Ceschia, S., & Schaerf, A. (2012). Modeling and solving the dynamic patient admission scheduling problem under uncertainty. Artificial Intelligence in Medicine, 56(3), 199–205.CrossRefGoogle Scholar
  9. Chow, V. S., Puterman, M. L., Salehirad, N., Huang, W., & Atkins, D. (2011). Reducing surgical ward congestion through improved surgical scheduling and uncapacitated simulation. Production and Operations Management, 20(3), 418–430.CrossRefGoogle Scholar
  10. Cioppa, T. M., & Lucas, T. W. (2007). Efficient nearly orthogonal and space-filling latin hypercubes. Technometrics, 49(1), 45–55.CrossRefGoogle Scholar
  11. Demeester, P. (2009). Patient admission scheduling website. Accessed 24 September 2013.
  12. Demeester, P., Souffriau, W., De Causmaecker, P., & Vanden Berghe, G. (2010). A hybrid tabu search algorithm for automatically assigning patients to beds. Artificial Intelligence in Medicine, 48(1), 61–70.CrossRefGoogle Scholar
  13. Fei, H., Chu, C., Meskens, N., & Artiba, A. (2008). Solving surgical cases assignment problem by a branch-and-price approach. International Journal of Production Economics, 112(1), 96–108.CrossRefGoogle Scholar
  14. Fei, H., Meskens, N., & Chu, C. (2010). A planning and scheduling problem for an operating theatre using an open scheduling strategy. Computers & Industrial Engineering, 58(2), 221–230.CrossRefGoogle Scholar
  15. Gartner, D., & Kolisch, R. (2014). Scheduling the hospital-wide flow of elective patients. European Journal of Operational Research, 233(3), 689–699.CrossRefGoogle Scholar
  16. Guerriero, F., & Guido, R. (2011). Operational research in the management of the operating theatre: a survey. Health Care Management Science, 14(1), 89–114.CrossRefGoogle Scholar
  17. Hans, E.W., & Vanberkel, P.T. (2012). Operating theatre planning and scheduling. In: Handbook of healthcare system Scheduling, vol 168, Springer, chap 5, pp 105–130.Google Scholar
  18. Hans, E. W., Wullink, G., van Houdenhoven, M., & Kazemier, G. (2008). Robust surgery loading. European Journal of Operational Research, 185(3), 1038–1050.CrossRefGoogle Scholar
  19. Harper, P. R., & Shahani, A. K. (2002). Modelling for the planning and management of bed capacities in hospitals. The Journal of the Operational Research Society, 53(1), 11–18.CrossRefGoogle Scholar
  20. Hulshof, P. J., Boucherie, R. J., Hans, E. W., & Hurink, J. L. (2013). Tactical resource allocation and elective patient admission planning in care processes. Health Care Management Science, 16(2), 152–166.CrossRefGoogle Scholar
  21. Hulshof, P. J. H., Kortbeek, N., Boucherie, R. J., Hans, E. W., & Bakker, P. J. M. (2012). Taxonomic classification of planning decisions in health care: A structured review of the state of the art in OR/MS. Health Systems, 1(2), 129–175.CrossRefGoogle Scholar
  22. Hutzschenreuter, AK., Bosman, PAN., Blonk-Altena, I., van Aarle, J., & La Poutré, H. (2008). Agent-based patient admission scheduling in hospitals. In: Proceedings of the 7th International Joint Conference on Autonomous Agents and Multiagent Systems: Industrial Track, International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC, AAMAS ’08, pp 45–52.Google Scholar
  23. Jebali, A., Hadjalouane, A., & Ladet, P. (2006a). Operating rooms scheduling. International Journal of Production Economics, 99(1–2), 52–62. doi: 10.1016/j.ijpe.2004.12.006.CrossRefGoogle Scholar
  24. Kirkpatrick, S., Gelatt, C. D, Jr, & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220, 671–680.CrossRefGoogle Scholar
  25. van Laarhoven, P. J. M., & Aarts, E. H. L. (1987). Simulated annealing: Theory and applications. New York: Kluwer Academic Publishers.CrossRefGoogle Scholar
  26. Lamiri, M., Grimaud, F., & Xie, X. (2009). Optimization methods for a stochastic surgery planning problem. International Journal of Production Economics, 120(2), 400–410.CrossRefGoogle Scholar
  27. Marazzi, A., Paccaud, F., Ruffieux, C., & Beguin, C. (1998). Fitting the distributions of length of stay by parametric models. Medical Care, 36(6), 915–27.CrossRefGoogle Scholar
  28. Min, D., & Yih, Y. (2010a). An elective surgery scheduling problem considering patient priority. Computers & Operations Research, 37(6), 1091–1099.Google Scholar
  29. Min, D., & Yih, Y. (2010b). Scheduling elective surgery under uncertainty and downstream capacity constraints. European Journal of Operational Research, 206(3), 642–652.CrossRefGoogle Scholar
  30. Nunes, L. G. N., de Carvalho, S. V., & de Cassia Meneses Rodrigues, R. (2009). Markov decision process applied to the control of hospital elective admissions. Artificial Intelligence in Medicine, 47(2), 159–171.CrossRefGoogle Scholar
  31. Oostrum, J. M., Bredenhoff, E., & Hans, E. W. (2009). Suitability and managerial implications of a master surgical scheduling approach. Annals of Operations Research, 178(1), 91–104.CrossRefGoogle Scholar
  32. Patrick, J., Puterman, M. L., & Queyranne, M. (2008). Dynamic multipriority patient scheduling for a diagnostic resource. Operations Research, 56(6), 1507–1525.CrossRefGoogle Scholar
  33. Range, T., Lusby, R., & Larsen, J. (2014). A column generation approach for solving the patient admission scheduling problem. European Journal of Operational Research, 235(1), 252–264.CrossRefGoogle Scholar
  34. Riise, A., & Burke, E. K. (2011). Local search for the surgery admission planning. Journal of Heuristics, 17, 389–414.CrossRefGoogle Scholar
  35. Schmidt, R., Geisler, S., & Spreckelsen, C. (2013). Decision support for hospital bed management using adaptable individual length of stay estimations and shared resources. BMC Medical Informatics and Decision Making, 13, 3.CrossRefGoogle Scholar
  36. Testi, A., & Tànfani, E. (2009). Tactical and operational decisions for operating room planning: Efficiency and welfare implications. Health Care Management Science, 12(4), 363–373.CrossRefGoogle Scholar
  37. Vancroonenburg, W., De Causmaecker, P., & Vanden Berghe, G. (2012). Patient-to-room assignment planning in a dynamic context. In: Proceedings of the 9th International Conference on the Practice and Theory of Automated Timetabling (PATAT-2012), pp 193–208.Google Scholar
  38. Vancroonenburg, W., De Causmaecker, P., Spieksma, F., & Vanden Berghe, G. (2013). Scheduling elective patient admissions considering room assignment and operating theatre capacity constraints. In: Proceedings of the 5th International Conference on Applied Operational Research, Lecture Notes in Management Science, vol 5, pp 153–158.Google Scholar
  39. Vijayakumar, B., Parikh, P. J., Scott, R., Barnes, A., & Gallimore, J. (2013). A dual bin-packing approach to scheduling surgical cases at a publicly-funded hospital. European Journal of Operational Research, 224(3), 583–591.CrossRefGoogle Scholar
  40. Vissers, J., Bertrand, J., & De Vries, G. (2001). A framework for production control in health care organizations. Production Planning & Control, 12(6), 591–604.CrossRefGoogle Scholar
  41. Vissers, J. M., Adan, I. J., & Dellaert, N. P. (2007). Developing a platform for comparison of hospital admission systems: An illustration. European Journal of Operational Research, 180(3), 1290–1301.CrossRefGoogle Scholar
  42. Wullink, G., Houdenhoven, M., Hans, E. W., Oostrum, J. M., Lans, M., & Kazemier, G. (2007). Closing emergency operating rooms improves efficiency. Journal of Medical Systems, 31(6), 543–546.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.DIEGMUniversity of UdineUdineItaly

Personalised recommendations