Abstract
This research proposes an algorithm to solve overbooking scheduling problem for outpatient hospitals with multiple providers and high patient demand. Assuming there is reward and system cost for serving each patient, and the decision maker in the hospital can decide the amount of resources to assign to each patient. Regardless of the random patient arrivals and departures, a novel model of the Continuous-Time Markov Decision Process (CTMDP) is explored, our objective in this paper is to find an optimal policy to achieve maximum total discounted expected reward starting from any initial states. Further more, to solve the computation complexity of CTMDP models when the action and system state space is large, genetic algorithm (GA) is proposed to search the optimal solution, which can be calculated in a parallel way thus reducing the computation time for the optimal policy.
W. Ni and J. Wang—These authors contributed equally to this work. This work was supported in part by Jiang** Education Department under Grant No. GJJ191688.
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Ni, W. et al. (2022). Optimal Overbooking Appointment Scheduling in Hospitals Using Evolutionary Markov Decision Process. In: Pan, L., Cui, Z., Cai, J., Li, L. (eds) Bio-Inspired Computing: Theories and Applications. BIC-TA 2021. Communications in Computer and Information Science, vol 1565. Springer, Singapore. https://doi.org/10.1007/978-981-19-1256-6_22
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DOI: https://doi.org/10.1007/978-981-19-1256-6_22
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