Using Distributed Agents for Patient Scheduling

  • Graham Billiau
  • Chee Fon Chang
  • Aditya Ghose
  • Alexis Andrew Miller
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7057)


Ensuring optimum use of scarce resources is one of the largest challenges facing health providers today. However it is not easy to generate an optimised schedule, as the health system is unusually and highly dynamic. Scheduling systems must be extremely flexible while still producing an efficient, acceptable schedule. Furthermore the scheduling system should be able to cross health boundaries inside and outside hospitals to perform load sharing.

To solve this problem we propose an encoding of the patient scheduling problem as a dynamic distributed constraint optimisation problem and show how it can be solved using Support Based Distributed Optimisation. The resulting system will be able to generate good schedules and update them in real time. It is also able to maintain privacy across hospital boundaries to enable load balancing.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Cohen, S.: The pursuit of efficiency: Automation in health care. Hospital and Healthcare ManagementGoogle Scholar
  2. 2.
    Cascardo, D.C.: Smart scheduling: The key to practice efficiency. Medscape Today (2000)Google Scholar
  3. 3.
    Van Houdenhoven, M., van Oostrum, J.M., Hans, E.W., Wullink, G., Kazemier, G.: Improving operating room efficiency by applying bin-packing and portfolio techniques to surgical case scheduling. In: Anesthesia & Analgesia (2007)Google Scholar
  4. 4.
    Foundation, Q., (accessed August 20, 2010)
  5. 5.
    Schiex, T., Fargier, H., Verfaillie, G.: Valued constraint satisfaction problems: Hard and easy problems. In: Proceedings of the 15th International Joint Conference on Artificial Intelligence, Montreal, Canada (1995)Google Scholar
  6. 6.
    Yokoo, M., Durfee, E.H., Ishida, T., Kuwabara, K.: The distributed constraint satisfaction problem - formalization and algorithms. IEEE Transactions on Knowledge and Data Engineering 10(5), 673–685 (1998)CrossRefGoogle Scholar
  7. 7.
    Verfaillie, G., Jussien, N.: Constraint solving in uncertain and dynamic environments: A survey. Constraints 10(3), 253–281 (2005)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Petcu, A., Faltings, B.: R-dpop: Optimal solution stability in continuous-time optimization. In: IAT 2007 (November 2007)Google Scholar
  9. 9.
    Mertens, K.: An Ant-Based Approach for Solving Dynamic Constraint Optimization Problems. PhD thesis, Katholieke Universiteit Leuven (December 2006)Google Scholar
  10. 10.
    Billiau, G., Ghose, A.: Sbdo: A new robust approach to dynamic distributed constraint optimisation. In: Yang, J.-J., Yokoo, M., Ito, T., Jin, Z., Scerri, P. (eds.) PRIMA 2009. LNCS, vol. 5925, pp. 641–648. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  11. 11.
    Billiau, G., Chang, C.F., Ghose, A.: Sbdo: A New Robust Approach to Dynamic Distributed Constraint Optimisation. In: Desai, N., Liu, A., Winikoff, M. (eds.) PRIMA 2010. LNCS(LNAI), vol. 7057, pp. 11–26. Springer, Heidelberg (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Graham Billiau
    • 1
  • Chee Fon Chang
    • 1
  • Aditya Ghose
    • 1
  • Alexis Andrew Miller
    • 2
  1. 1.Decision Systems lab, Center for Oncology InformaticsIllawarra Medical & Health Research Institute University of WollongongAustralia
  2. 2.Illawarra Cancer Care CentreWollongong HospitalWollongongAustralia

Personalised recommendations