Dynamic Agent-based Scheduling of Treatments: Evidence from the Dutch Youth Health Care Sector

  • Erik GiesenEmail author
  • Wolfgang Ketter
  • Rob Zuidwijk
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9433)


We use agent-based simulation to compare the performance of four scheduling policies in youth health care. The policies deploy push/pull and centralized/decentralized concepts. The simulation model represents an authentic business case and is parameterized with actual market data. The model incorporates, among other things, non-stationary Poisson arrival processes, reneging and return mechanisms, and care provider’s client preferences. We have identified that performance measurement in youth health care should not be focused on queue lengths alone, which is presently the case, but should include a case difficulty parameter as well. The simulation results, together with contextual data obtained from stakeholder interviews, indicate that a push strategy with a centralized queue suits the sector best, which is different from the current real-world situation. This policy ensures a higher level of fairness in treatment provision because the care providers are compelled to take their share in treating the difficult and economically less attractive cases. The complexity of the case cannot be captured by current queuing theory methods. Our simulation approach incorporates these complexities, which turn out to be relevant for the scheduling policy decision. We validate the model and strategies using real market data and field expert discussions.


Agent-based simulation Resource allocation Youth health care Preference behavior Policy scheduling 


  1. 1.
    Agarwal, R., Guodong (Gordon), G., DesRoches, C., Jha, A.K.: The digital transformation of healthcare: Current status and the road ahead. Inf. Syst. Res. 20(4), 796–809 (2010)CrossRefGoogle Scholar
  2. 2.
    Andriessen, S., Besseling, J.: Jongeren zijn steeds vaker niet normaal. Jeugd Beleid 2(1), 87–95 (2008)Google Scholar
  3. 3.
    Armony, M., Plambeck, E., Seshadri, S.: Sensitivity of optimal capacity to customer impatience in an unobservable m/m/s queue (why you shouldnt shout at the dmv). Manufact. Serv. Oper. Manage. 11(1), 19–32 (2009)CrossRefGoogle Scholar
  4. 4.
    Bagust, A., Place, M., Posnett, J.W.: Dynamics of bed use on accommodating emergency admissions: Stochastic simulation model. Br. Med. J. X, 319 (1999)Google Scholar
  5. 5.
    Bichler, M., Gupta, A., Ketter, W.: Designing smart markets. Inf. Syst. Res. 21(4), 688–699 (2010)CrossRefGoogle Scholar
  6. 6.
    Britan, G.R., Ferrer, J.C., e Oliveira, P.R.: Managing customer experiences: Perspectives on the temporal aspects of service encounters. Manuf. Servi. Oper. Manage. 1(1), 61–83 (2008)Google Scholar
  7. 7.
    Brown, L., Gans, N., Mandelbaum, A., Sakov, A., Shen, H., Zeltyn, S., Zhao, L.: Statistical analysis of a telephone call center. J. Am. Stat. Assoc. 100(469), 36–50 (2005)CrossRefzbMATHGoogle Scholar
  8. 8.
    Collins, J., Ketter, W., Gini, M.: A multi-agent negotiation testbed for contracting tasks with temporal and precedence constraints. Int. J. Electron. Commer. 7(1), 35–57 (2002)Google Scholar
  9. 9.
    Collins, J., Ketter, W., Sadeh, N.: Pushing the limits of rational agents: the trading agent competition for supply chain management. AI Mag. 31(2), 63–80 (2010)Google Scholar
  10. 10.
    Devaraj, S., Kohli, R.: Information technology payoff in the health-care industry: a longitudinal study. J. Manage. Inform. Syst. 16(4), 41–67 (2000)CrossRefGoogle Scholar
  11. 11.
    Fletcher, A., Halsall, D., Huxham, S., Worthington, D.: The dh accident and emergency department model: A national generic model used locally. J. Oper. Res. Soc. 58, 1554–1562 (2007)CrossRefGoogle Scholar
  12. 12.
    Goldman, R.D., Macpherson, A., Schuh, S., Mulligan, C., Pirie, J.: Patients who leave the pediatric emergency department without being seen: case-control study. Can. Med. Assoc. J. 172(1), 39–43 (2005)CrossRefGoogle Scholar
  13. 13.
    Goodacre, S., Webster, A.: Who waits longest in the emergency department and who leaves without being seen? Emerg. Med. J. 22(2), 93 (2005)CrossRefGoogle Scholar
  14. 14.
    Gorunescu, F., McClean, S.I., Millard, P.H.: A queueing model for bed-occupancy management and planning of hospitals. J. Oper. Res. Soc. 53, 19–24 (2002)CrossRefzbMATHGoogle Scholar
  15. 15.
    Gupta, D., Denton, B.: Appointment scheduling in health care: Challenges and opportunities. IIE Trans. 40(9), 800–819 (2008)CrossRefGoogle Scholar
  16. 16.
    Hevner, A.R., March, S.T., Park, J., Ram, S.: Design science in information systems research. Manage. Inf. Syst. Q. 28(1), 75–106 (2004)Google Scholar
  17. 17.
    Jacobs, P.H.M., Lang, N.A., Verbraeck, A.: D-sol: A distributed java based discrete event simulation architecture. X, ed., In: Proceedings of the 2002 Winter Simulation Conference. San Diego, pp. 793–800. ISBN 0-7803-7614-5 (2002)Google Scholar
  18. 18.
    Kaplan, R.S., Norton, D.P.: The balanced scorecard: Measures that drive performance. Harvard Bus. Rev. 83(7), 172–180 (2005)Google Scholar
  19. 19.
    Ketter, W., Collins, J., Reddy, P.: Power TAC: A competitive economic simulation of the smart grid. Energy Econ. 39, 262–270 (2013)CrossRefGoogle Scholar
  20. 20.
    Liu, N., Ziya, S., Kulkarni, V.G.: Dynamic scheduling of outpatient appointments under patient no-shows and cancellations. Manuf. Serv. Oper. Manag. 12(2), 347–364 (2010)Google Scholar
  21. 21.
    Netherlands National News Agency, NANP. 2008. Millions of additional funding for youth careGoogle Scholar
  22. 22.
    Postl, B.D.: Final report of the federal advisor on wait times. Technical Report, Health Canada (2006)Google Scholar
  23. 23.
    Rachlis, M.: Public solutions to health care wait lists. Technical Report, Canadian Centre for Policy Alternatives (2005)Google Scholar
  24. 24.
    Ridge, J.C., Jones, S.K., Nielsen, M.S., Shahani, A.K.: Capacity planning for intensive care units. Eur. J. Oper. Res. 105, 346–355 (1998)CrossRefzbMATHGoogle Scholar
  25. 25.
    Robinson, L.W., Chen, R.R.: Estimating the implied value of the customer’s waiting time. Manuf. Serv. Oper. Manage. 13(1), 53–57 (2011)Google Scholar
  26. 26.
    Saulnier, M., Shortt, S., Gruenwoldt, E.: The taming of the queue: Toward a cure for health care wait times. Technical Report, Canadian Medical Association (2004)Google Scholar
  27. 27.
    Van Mieghem, J.: Dynamic scheduling with convex delay costs. Ann. Appl. Probab. 5(3), 809–833 (1995)MathSciNetCrossRefzbMATHGoogle Scholar
  28. 28.
    Welch, P.D.: On the problem of the initial transient in steady-state simulation. IBM Watson Research Center (1981)Google Scholar
  29. 29.
    Welch, P.D.: The statistical analysis of simulation results. The computer performance modeling handbook 268–328 (1983)Google Scholar
  30. 30.
    Wooldridge, M., Jennings, N.R.: Intelligent agents: Theory and practice. Knowl. Eng. Rev. 10(2), 115–152 (1995)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.INITI8RotterdamThe Netherlands
  2. 2.Rotterdam School of ManagementRotterdamThe Netherlands

Personalised recommendations