Abstract
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.
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References
Cohen, S.: The pursuit of efficiency: Automation in health care. Hospital and Healthcare Management
Cascardo, D.C.: Smart scheduling: The key to practice efficiency. Medscape Today (2000)
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)
Foundation, Q., http://www.qefoundation.org (accessed August 20, 2010)
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)
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)
Verfaillie, G., Jussien, N.: Constraint solving in uncertain and dynamic environments: A survey. Constraints 10(3), 253–281 (2005)
Petcu, A., Faltings, B.: R-dpop: Optimal solution stability in continuous-time optimization. In: IAT 2007 (November 2007)
Mertens, K.: An Ant-Based Approach for Solving Dynamic Constraint Optimization Problems. PhD thesis, Katholieke Universiteit Leuven (December 2006)
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)
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)
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Billiau, G., Chang, C.F., Ghose, A., Miller, A.A. (2012). Using Distributed Agents for Patient Scheduling. In: Desai, N., Liu, A., Winikoff, M. (eds) Principles and Practice of Multi-Agent Systems. PRIMA 2010. Lecture Notes in Computer Science(), vol 7057. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25920-3_40
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DOI: https://doi.org/10.1007/978-3-642-25920-3_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25919-7
Online ISBN: 978-3-642-25920-3
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