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Simulation-based approximate policy iteration for dynamic patient scheduling for radiation therapy

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  • Yasin Gocgun

    (Istanbul Kemerburgaz University)

Abstract

We study radiation therapy scheduling problem where dynamically and stochastically arriving patients of different types are scheduled to future days. Unlike similar models in the literature, we consider cancellation of treatments. We formulate this dynamic multi-appointment patient scheduling problem as a Markov Decision Process (MDP). Since the MDP is intractable due to large state and action spaces, we employ a simulation-based approximate dynamic programming (ADP) approach to approximately solve our model. In particular, we develop Least-square based approximate policy iteration for solving our model. The performance of the ADP approach is compared with that of a myopic heuristic decision rule.

Suggested Citation

  • Yasin Gocgun, 2018. "Simulation-based approximate policy iteration for dynamic patient scheduling for radiation therapy," Health Care Management Science, Springer, vol. 21(3), pages 317-325, September.
    • Handle: RePEc:kap:hcarem:v:21:y:2018:i:3:d:10.1007_s10729-016-9388-9
    DOI: 10.1007/s10729-016-9388-9
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    10. Yasin Gocgun & Martin Puterman, 2014. "Dynamic scheduling with due dates and time windows: an application to chemotherapy patient appointment booking," Health Care Management Science, Springer, vol. 17(1), pages 60-76, March.
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    Cited by:

    1. Silva, Thiago A.O. & de Souza, Mauricio C., 2020. "Surgical scheduling under uncertainty by approximate dynamic programming," Omega, Elsevier, vol. 95(C).
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    3. Tu-San Pham & Louis-Martin Rousseau & Patrick Causmaecker, 2022. "A two-phase approach for the Radiotherapy Scheduling Problem," Health Care Management Science, Springer, vol. 25(2), pages 191-207, June.
    4. Shao, Kaining & Fan, Wenjuan & Lan, Shaowen & Kong, Min & Yang, Shanlin, 2023. "A column generation-based heuristic for brachytherapy patient scheduling with multiple treatment sessions considering radioactive source decay and time constraints," Omega, Elsevier, vol. 118(C).
    5. Kaining Shao & Wenjuan Fan & Zishu Yang & Shanlin Yang & Panos M. Pardalos, 2022. "A column generation approach for patient scheduling with setup time and deteriorating treatment duration," Operational Research, Springer, vol. 22(3), pages 2555-2586, July.

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