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Scheduling Patients in Hospitals Based on Multi-agent Systems

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Modern Advances in Applied Intelligence (IEA/AIE 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8481))

Abstract

Scheduling patients in a hospital is a challenging issue as it calls for a sustainable architecture and an effective scheduling scheme that can dynamically schedule the available resources. The objectives of this paper are to propose a viable and systematic approach to develop a distributed cooperative problem solver for scheduling patients based on MAS to minimize the patient stay in a hospital. Our solution approach combines contract net protocol (CNP), multi-agent system architecture, process specification model based on time Petri net (TPN) and optimization theories. Our system first automatically formulate patient scheduling problem based on the TPN models. The patient scheduling problem is decomposed into a number of subproblems that are solved by cooperative agents. A collaborative algorithm is invoked by individual agents to optimize the schedules locally based on a problem formulation automatically obtained by the Petri net models. To realize our solution methodology, we proposed a scheme for publication and discovery of agents’ services based on FIPA compliant multi-agent system platform. We develop a scheduling system to solve the dynamic patient scheduling problem.

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References

  1. Decker, K., Li, J.: Coordinated hospital patient scheduling. In: International Conference on Multi Agent Systems, Paris, July 3-7, pp. 104–111 (1998)

    Google Scholar 

  2. Kutanoglu, E., David Wu, S.: On combinatorial auction and Lagrangean relaxation for distributed resource scheduling. IIE Transactions 31, 813–826 (1999)

    Google Scholar 

  3. Oddi, A., Cesta, A.: Toward interactive scheduling systems for managing medical resources. Artificial Intelligence in Medicine 20, 113–138 (2000)

    Article  Google Scholar 

  4. Daknou, A., Zgaya, H., Hammadi, S., Hubert, H.: A dynamic patient scheduling at the emergency department in hospitals. In: 2010 IEEE Workshop on Health Care Management, February 18-20, pp. 1–6 (2010)

    Google Scholar 

  5. Spyropoulos, C.D.: AI planning and scheduling in the medical hospital environment. Artificial Intelligence in Medicine 20, 101–111 (2000)

    Article  Google Scholar 

  6. Nilsson, N.J.: Artificial Intelligence: A New Synthesis. Morgan Kaufmann Publishers, Inc., SanFrancisco (1998)

    MATH  Google Scholar 

  7. Ferber, J.: Multi-Agent Systems, An Introduction to Distributed Artificial Intelligence. Addison-Wesley, Reading (1999)

    Google Scholar 

  8. Durfee, E.H., Lesser, V.R., Corkill, D.D.: Trends in cooperative distributed problem solving. IEEE Transactions on Knowledge and Data Engineering 1(1), 63–83 (1989)

    Article  Google Scholar 

  9. Russel, S.J., Norvig, P.: Artificial Intelligence—A Modern Approach, 2nd edn. Pearson Education Asia Limited (2006)

    Google Scholar 

  10. Workflow Management Coalition, XPDL support and resources (2009), http://www.wfmc.org/xpdl.html

  11. Object Management Group, Business process modeling notation (2009), http://www.bpmn.org

  12. OASIS, Web services business process execution language version 2.0 (2009), http://docs.oasis-open.org/wsbpel/2.0/OS/wsbpel-v2.0-OS.html

  13. Hsieh, F.-S., Lin, J.-B.: Temporal Reasoning in Multi-agent Workflow Systems Based on Formal Models. In: Pan, J.-S., Chen, S.-M., Nguyen, N.T. (eds.) ACIIDS 2012, Part I. LNCS, vol. 7196, pp. 33–42. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  14. Smith, R.G.: The Contract net protocol: high-level communication and control in a distributed problem solver. IEEE Transactions on Computers 29, 1104–1113 (1980)

    Article  Google Scholar 

  15. Murata, T.: Petri Nets: Properties, Analysis and Applications. Proceedings of the IEEE 77(4), 541–580 (1989)

    Article  Google Scholar 

  16. Berthomieu, B., Menasche, M.: An Enumerative Approach for Analyzing Time Petri Nets. In: Proc. Ninth Intenational Federation of Information Processing (IFIP) World Computer Congress, vol. 9, pp. 41–46 (September 1983)

    Google Scholar 

  17. Conry, S.E., Kuwabara, K., Lesser, V.R., Meyer, R.A.: Multistage negotiation for distributed constraint satisfaction. IEEE Transactions on Systems, Man and Cybernetics 21(6), 1462–1477 (1991)

    Article  MATH  Google Scholar 

  18. Marinagia, C.C., Spyropoulosa, C.D., Papatheodoroub, C., Kokkotos, S.: Continual planning and scheduling for managing patient tests in hospital laboratories. Artificial Intelligence in Medicine 20, 139–154 (2000)

    Article  Google Scholar 

  19. Güray Güler, M.: A hierarchical goal programming model for scheduling the outpatient clinics. Expert Systems with Applications 40(12), 4906–4914 (2013)

    Article  Google Scholar 

  20. McFarlane, D.C., Bussmann, S.: Developments in holo-nic production planning and control. International Journal of Production Planning and Control 11(6), 522–536 (2000)

    Article  Google Scholar 

  21. Parunak, H.V.D.: Manufacturing experiences with the contract net. In: Huhns, M. (ed.) Distributed Artificial Intelligence, pp. 285–310. Pitman, London (1987)

    Chapter  Google Scholar 

  22. Saremi, A., Jula, P., ElMekkawy, T., Wang, G.G.: Appointment scheduling of outpatient surgical services in a multistage operating room department. International Journal of Production Economics 141(2), 646–658 (2013)

    Article  Google Scholar 

  23. Sauré, A., Patrick, J., Tyldesley, S., Puterman, M.L.: Dynamic multi-appointment patient scheduling for radiation therapy. European Journal of Operational Research 223(2), 573–584 (2012)

    Article  MathSciNet  Google Scholar 

  24. Ceschia, S., Schaerf, A.: Modeling and solving the dynamic patient admission scheduling problem under uncertainty. Artificial Intelligence in Medicine 56(3), 199–205 (2012)

    Article  Google Scholar 

  25. Polyak, B.T.: Minimization of Unsmooth Functionals. USSR Computational Math. and Math. Physics 9, 14–29 (1969)

    Article  Google Scholar 

  26. van der Aalst, W.M.P.: The application of Petri nets to workflow management. J. Circuit. Syst. Comput. 8(1), 21–66 (1998)

    Article  Google Scholar 

  27. van der Aalst, W.M.P., Kumar, A.: A reference model for team-enabled workflow management systems. Data & Knowledge Engineering 38(3), 3355–3363 (2001)

    Google Scholar 

  28. Weske, M., van der Aalst, W.M.P., Verbeek, H.M.W.: Advances in business process management. Data & Knowledge Engineering 50(1), 1–8 (2004)

    Article  Google Scholar 

  29. Weber, M., Kindler, E.: The Petri Net Markup Language (2012), http://www2.informatik.hu-berlin.de/top/pnml/download/about/PNML_LNCS.pdf

  30. Billington, J., et al.: The Petri Net Markup Language: Concepts, Technology, and Tools. In: van der Aalst, W.M.P., Best, E. (eds.) ICATPN 2003. LNCS, vol. 2679, pp. 483–505. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  31. Edmonds, J., Karp, R.M.: Theoretical improvements in algorithmic efficiency for network flow problems. Journal of the ACM 19(2), 248–264 (1972)

    Article  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science and Information Engineering, Chaoyang University of Technology, 41349, Taichung, Taiwan

    Fu-Shiung Hsieh & Jim-Bon Lin

Authors
  1. Fu-Shiung Hsieh
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  2. Jim-Bon Lin
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Editor information

Editors and Affiliations

  1. Department of Computer Science, Texas State University, TX 78666, San Marcos, USA

    Moonis Ali

  2. Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, No. 415, Chien Kung Road, 80778, Kaohsiung, Taiwan

    Jeng-Shyang Pan

  3. Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan

    Shyi-Ming Chen

  4. Department of Electronics Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan (ROC)

    Mong-Fong Horng

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Hsieh, FS., Lin, JB. (2014). Scheduling Patients in Hospitals Based on Multi-agent Systems. In: Ali, M., Pan, JS., Chen, SM., Horng, MF. (eds) Modern Advances in Applied Intelligence. IEA/AIE 2014. Lecture Notes in Computer Science(), vol 8481. Springer, Cham. https://doi.org/10.1007/978-3-319-07455-9_4

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  • DOI: https://doi.org/10.1007/978-3-319-07455-9_4

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07454-2

  • Online ISBN: 978-3-319-07455-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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