Abstract
Appointment scheduling assigns start times in a session or consultation block to a set of tasks that share a common resource. For a generic repeated appointment scheduling problem, we study the trade-off between waiting for an appointment and waiting at the appointed time. Assuming that being scheduled later during a session implies that one has to wait longer for service (on average), it is often beneficial to choose a consultation block further away. We study this trade-off both when the patients have no information on how many patients are already scheduled in future consultation blocks and when they can observe the future block schedules. By some numerical examples, we find that in both cases the rational choice considerably changes between consecutive appointment blocks, patients favouring later blocks when the next appointment block starts in the near future. We also compare the rational choice with the socially optimal schedule and find that socially optimal scheduling can significantly reduce the waiting cost.





Similar content being viewed by others
References
Cayirli, T., & Veral, E. (2003). Outpatient scheduling in health care: A review of literature. Production and Operations Management, 12(4), 519–549.
Chun, Y., Mitra, M., & Mutuswami, S. (2019). Egalitarianism in the queueing problem. Journal of Mathematical Economics, 81, 48–56.
Chun, Y., Park, N., & Yengin, D. (2016). Coincidence of cooperative game theoretic solutions in the appointment problem. International Journal of Game Theory, 45(3), 699–708.
Curiel, I., Pederzoli, G., & Tijs, S. (1989). Sequencing games. European Journal of Operational Research, 40(3), 344–351.
De Vuyst, S., Bruneel, H., & Fiems, D. (2014). Computationally efficient evaluation of appointment schedules in health care. European Journal of Operational Research, 237(3), 1142–1154.
Debo, L. G., Parlour, C., & Rajan, U. (2012). Signaling quality via queues. Management Science, 58(5), 876–891.
Feldman, J., Liu, N., Topaloglu, H., & Ziya, S. (2014). Appointment scheduling under patient preference and no-show behavior. Operations Research, 62(4), 794–811.
Fiems, D., & Prabhu, B. (2020). Macroscopic modelling and analysis of rush-hour congestion. In Proceedings of the 13th EAI international conference on performance evaluation methodologies and tools.
Fiems, D., Prabhu, B., & De Turck, K. (2019). Travel times, rational queueing and the macroscopic fundamental diagram of traffic flow. Physica A, 524, 412–421.
Golitschek, M. V. (1975). Linear approximation by exponential sums on finite intervals. Bulletin of the American Mathematical Society, 81(2), 443–445.
Gupta, D., & Denton, B. (2008). Appointment scheduling in health care: Challenges and opportunities. IIE Transactions, 40(9), 800–819.
Gupta, D., & Wang, L. (2008). Revenue management for a primary-care clinic in the presence of patient choice. Operations Research, 56(3), 576–592.
Harper, P. R., & Gamlin, H. M. (2003). Reduced outpatient waiting times with improved appointment scheduling: A simulation modelling approach. Or Spectrum, 25(2), 207–222.
Hassin, R., & Haviv, M. (2009). To queue or not to queue. Amsterdam: Kluwer.
Haviv, M., & Roughgarden, T. (2007). The price of anarchy in an exponential multi-server. Operations Research Letters, 35(4), 421–426.
Jain, R., Juneja, S., & Shimkin, N. (2011). The concert queueing game: To wait or to be late. Discrete Event Dynamical Systems, 21, 103–138.
Kortbeek, N., Zonderland, M. E., Braaksma, A., Vliegen, I. M. H., Boucherie, R. J., Litvak, N., & Hans, E. W. (2014). Designing cyclic appointment schedules for outpatient clinics with scheduled and unscheduled patient arrivals. Performance Evaluation, 80, 5–26.
Koutsoupias, E., & Papadimitriou, C. (1999). Worst-case equilibria. In: Annual symposium on theoretical aspects of computer science (pp. 404–413). Springer.
Liu, N., Finkelstein, S. R., Kruk, M. E., & Rosenthal, D. (2017). When waiting to see a doctor is less irritating: Understanding patient preferences and choice behavior in appointment scheduling. Management Science, 64(5), 1975–1996.
Luo, J., Kulkarni, V. G., & Ziya, S. (2015). A tandem queueing model for an appointment-based service system. Queueing Systems, 79(1), 53–85.
Maniquet, F. (2003). A characterization of the Shapley value in queueing problems. Journal of Economic Theory, 109, 90–103.
Mehrotra, A., Keehl-Markowitz, L., & Ayanian, J. Z. (2008). Implementing open-access scheduling of visits in primary care practices: A cautionary tale. Annals of Internal Medicine, 148(12), 915–922.
Murray, M., & Berwick, D. M. (2003). Advanced access: Reducing waiting and delays in primary care. JAMA, 289(8), 1035–1040.
Naor, P. (1969). The regulation of queue size by levying tolls. Econometrica, 37(1), 15–24.
Osadchiy, N., & KC, D. (2017). Are patients patient? The role of time to appointment in patient flow. Production and Operations Management, 26(3), 469–490.
Patrick, J., Puterman, M. L., & Queyranne, M. (2008). Dynamic multipriority patient scheduling for a diagnostic resource. Operations Research, 56(6), 1507–1525.
Polsky, D., Richards, M., Basseyn, S., Wissoker, D., Kenney, G. M., Zuckerman, S., & Rhodes, K. V. (2015). Appointment availability after increases in medicaid payments for primary care. New England Journal of Medicine, 372(6), 537–545.
Robinson, L. W., & Chen, R. R. (2010). A comparison of traditional and open-access policies for appointment scheduling. Manufacturing & Service Operations Management, 12(2), 330–346.
Rose, K. D., Ross, J. S., & Horwitz, L. I. (2011). Advanced access scheduling outcomes: A systematic review. Archives of Internal Medicine, 171(13), 1150–1159.
Sampson, F., Pickin, M., O’Cathain, A., Goodall, S., & Salisbury, C. (2008). Impact of same-day appointments on patient satisfaction with general practice appointment systems. British Journal of General Practice, 58(554), 641–643.
Sundar, D. K., & Ravikumar, K. (2014). An actor-critic algorithm for multi-agent learning in queue-based stochastic games. Neurocomputing, 127, 258–265.
Wales, D. J., & Doye, J. P. K. (1997). Global optimization by basin-hopping and the lowest energy structures of Lennard–Jones clusters containing up to 110 atoms. Journal of Physical Chemistry A, 101, 5111–5116.
Wang, J., & Fung, R. Y. K. (2015). Dynamic appointment scheduling with patient preferences and choices. Industrial Management & Data Systems, 115(4), 700–717.
Wang, J., & Zhang, F. (2018). Equilibrium analysis of the observable queues with balking and delayed repairs. Applied Mathematics and Computation, 2716–2729, 2011.
Wang, W.-Y., & Gupta, D. (2011). Adaptive appointment systems with patient preferences. Manufacturing & Service Operations Management, 13(3), 373–389.
Zacharias, C., & Armony, M. (2016). Joint panel sizing and appointment scheduling in outpatient care. Management Science, 63(11), 3978–3997.
Zander, A. (2016). Modeling indirect waiting times with an M/D/1/K/N queue. In Proceedings of the second KSS research workshop, Karlsruhe, Germany.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Deceuninck, M., De Vuyst, S., Claeys, D. et al. Appointment games with unobservable and observable schedules. Ann Oper Res 307, 93–110 (2021). https://doi.org/10.1007/s10479-021-04168-z
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10479-021-04168-z