Stochastic Integer Programming in Healthcare Delivery

Chapter
First Online:
Part of the Springer Optimization and Its Applications book series (SOIA, volume 74)

Abstract

This chapter reviews different ways that stochastic integer programming has been used to improve efficiency and efficacy in healthcare delivery. For the purpose of this study healthcare delivery is divided into two areas: resource allocation and operations. In each area the stochastic components are identified and the algorithms and solution techniques that have been proposed in the literature are described. We conclude the discussion with the current challenges and open questions.

Keywords

Facility Location Facility Location Problem Demand Node Post Anesthesia Care Unit Stochastic Programming Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References

  1. 1.
    Achieving operating room efficiency through process integration. Technical report, Health Care Financial Management Association Report (2005)Google Scholar
  2. 2.
    Batun, S., Denton, B.T., Huschka, T.R., Schaefer, A.J., The Benefit of Pooling Operating Rooms Under Uncertainty, INFORMS Journal on Computing, 23(2), 220–237 (2011)Google Scholar
  3. 3.
    Brotcorne, L., Laporte, G., Semet, F.: Ambulance location and relocation models. Eur. J. Oper. Res. 147(3), 451–463 (2003)MathSciNetMATHCrossRefGoogle Scholar
  4. 4.
    Caroe, C.C., Schultz, R.: Dual decomposition in stochastic integer programming. Oper. Res. Lett. 24, 37–45 (1999)MathSciNetMATHCrossRefGoogle Scholar
  5. 5.
    Daskin, M.S., Dean, L.K.: In: Brandeau M.L., Sainfort F., Pierskalla W.P. (eds.) Location of Healthcare Facilities. Operations Research and Health Care: A Handbook of Methods and Applications, pp. 43–76. Kluwer Academic Publishers, USA (2004).Google Scholar
  6. 6.
    Daiki, M., Yuehwern, Y.: Scheduling elective surgery patients under uncertainty and downstream capacity constraints. Eur. J. Oper. Res. 206, 642–652 (2010)MATHCrossRefGoogle Scholar
  7. 7.
    Denton, B.T., Miller, A., Balasubramanian, H., Huschka, T., Optimal Allocation of Surgery Blocks to Operating Rooms Under Uncertainty, Operations Research 58(4), 802–816 (2010).Google Scholar
  8. 8.
    Denton, B.T., Viapiano, J., Vogl, A.: Optimization of surgery sequencing and scheduling decisions under uncertainty. Health Care Manage. Sci. 10(1), 13–24 (2007)CrossRefGoogle Scholar
  9. 9.
    Doerner, K.F., Gutjahr, W.J., Hartl, R.F., Karall, M., Reimann, M.: Heuristic solution of an extended double-coverage ambulance location problem for Austria. Central Eur. J. Oper. Res. 13, 325–340 (2005)MATHGoogle Scholar
  10. 10.
    Erdogan, S.A., Denton, B.T.: Surgery planning and scheduling: a literature review, to be submitted to Wiley Encyclopedia of Operations Research and Management Science, March (2009)Google Scholar
  11. 11.
    Erdoğan, G., Erkut, E., Ingolfsson, A., Laporte, G.: Scheduling ambulance crews for maximum coverage. J. Oper. Res. Soc. 61, 543–550 (2010)CrossRefGoogle Scholar
  12. 12.
    Ingolfsson, A., Budge, S., Erkut, E.: Optimal ambulance location with random delays and travel times. Health Care Manage. Sci. 11, 262–274 (2008)CrossRefGoogle Scholar
  13. 13.
    Jonnalagadda, R., Walrond, E.R., Hariharan, S., Walrond, M., Prasad, C.: Evaluation of the reasons for cancellation and delays of surgical procedures in a developing country. Int. J. Clin. Pract. 59(6), 716–720 (2005)CrossRefGoogle Scholar
  14. 14.
    Kall, P., Wallace, S.W.: Stochastic Programming. Wiley-Interscience, New York (1994)MATHGoogle Scholar
  15. 15.
    Keller, B.D., Bayraksan, G.: Scheduling jobs sharing multiple resources under uncertainty: a stochastic programming approach. IIE Trans. Oper. Eng. 42(1), 16–30 (2010)CrossRefGoogle Scholar
  16. 16.
    Mancilla, C., Storer, R.H.: A sample average approximation approach to stochastic appointment sequencing and scheduling, IIE Transactions, 44(8), 655–670 (2012)Google Scholar
  17. 17.
    Mancilla, C., Storer, R.H.: Stochastic sequencing of surgeries for a single surgeon operating in parallel operating rooms, technical report Lehigh University (2010)Google Scholar
  18. 18.
    Lamiri, M., Dreo, J., Xie, X.: Operating Room Planning with Random Surgery Times, Proceedings of the 3rd Annual IEEE Conference on Automation Science and Engineering, Scottsdale, AZ, USA (2007)Google Scholar
  19. 19.
    Lamiri, M., Xie, X.L., Dolgui, A., Grimaud, F.: A stochastic model for operating room planning with elective and emergency demand for surgery. Eur. J. Oper. Res. 185, 1026–1037 (2008)MathSciNetMATHCrossRefGoogle Scholar
  20. 20.
    Özaltın, O., Johnson, M., Schaefer, A.: Senior Center Network Redesign Under Demand Uncertainty, under reviewGoogle Scholar
  21. 21.
    Punnakitikashem, P., Rosenberger, J.M., Behan, D.B.: Stochastic programming for nurse assignment. Comput. Optim. Appl. 40(3), 321–349 (2008)MathSciNetMATHCrossRefGoogle Scholar
  22. 22.
    Shapiro, A., Dentcheva, D., Ruszczy´nski, A.: Lectures on Stochastic Programming: Modeling and Theory. To be published by SIAM, Philadelphia (2009)Google Scholar
  23. 23.
    Van Oostrum, J.M., Van Houdenhoven, M., Hurink, J.L., Hans, E.W., Wullink, G., Kazemier, G.: A master surgery scheduling approach for cyclic scheduling in operating room departments. OR Spectrum 30(2), 355–374 (2008)MATHCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Lehigh UniversityBethlehemUSA

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