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Approximate dynamic programming for capacity allocation in the service industry

Listed author(s):
  • Schütz, Hans-Jörg
  • Kolisch, Rainer
Registered author(s):

    We consider a problem where different classes of customers can book different types of service in advance and the service company has to respond immediately to the booking request confirming or rejecting it. The objective of the service company is to maximize profit made of class-type specific revenues, refunds for cancellations or no-shows as well as cost of overtime. For the calculation of the latter, information on the underlying appointment schedule is required. In contrast to most models in the literature we assume that the service time of clients is stochastic and that clients might be unpunctual. Throughout the paper we will relate the problem to capacity allocation in radiology services. The problem is modeled as a continuous-time Markov decision process and solved using simulation-based approximate dynamic programming (ADP) combined with a discrete event simulation of the service period. We employ an adapted heuristic ADP algorithm from the literature and investigate on the benefits of applying ADP to this type of problem. First, we study a simplified problem with deterministic service times and punctual arrival of clients and compare the solution from the ADP algorithm to the optimal solution. We find that the heuristic ADP algorithm performs very well in terms of objective function value, solution time, and memory requirements. Second, we study the problem with stochastic service times and unpunctuality. It is then shown that the resulting policy constitutes a large improvement over an “optimal” policy that is deduced using restrictive, simplifying assumptions.

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    Article provided by Elsevier in its journal European Journal of Operational Research.

    Volume (Year): 218 (2012)
    Issue (Month): 1 ()
    Pages: 239-250

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    Handle: RePEc:eee:ejores:v:218:y:2012:i:1:p:239-250
    DOI: 10.1016/j.ejor.2011.09.007
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    1. Singh, Sumeetpal S. & Tadic, Vladislav B. & Doucet, Arnaud, 2007. "A policy gradient method for semi-Markov decision processes with application to call admission control," European Journal of Operational Research, Elsevier, vol. 178(3), pages 808-818, May.
    2. Vandaele, Nico & Van Nieuwenhuyse, Inneke & Cupers, Sascha, 2003. "Optimal grouping for a nuclear magnetic resonance scanner by means of an open queueing model," European Journal of Operational Research, Elsevier, vol. 151(1), pages 181-192, November.
    3. Sabine Sickinger & Rainer Kolisch, 2009. "The performance of a generalized Bailey–Welch rule for outpatient appointment scheduling under inpatient and emergency demand," Health Care Management Science, Springer, vol. 12(4), pages 408-419, December.
    4. Gosavi, Abhijit, 2004. "Reinforcement learning for long-run average cost," European Journal of Operational Research, Elsevier, vol. 155(3), pages 654-674, June.
    5. Tapas K. Das & Abhijit Gosavi & Sridhar Mahadevan & Nicholas Marchalleck, 1999. "Solving Semi-Markov Decision Problems Using Average Reward Reinforcement Learning," Management Science, INFORMS, vol. 45(4), pages 560-574, April.
    6. Yigal Gerchak & Diwakar Gupta & Mordechai Henig, 1996. "Reservation Planning for Elective Surgery Under Uncertain Demand for Emergency Surgery," Management Science, INFORMS, vol. 42(3), pages 321-334, March.
    7. Nan Liu & Serhan Ziya & Vidyadhar G. Kulkarni, 2010. "Dynamic Scheduling of Outpatient Appointments Under Patient No-Shows and Cancellations," Manufacturing & Service Operations Management, INFORMS, vol. 12(2), pages 347-364, September.
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