Surgical scheduling under uncertainty by approximate dynamic programming
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DOI: 10.1016/j.omega.2019.05.002
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Cited by:
- Ferreira, Cristiane & Figueira, Gonçalo & Amorim, Pedro, 2022. "Effective and interpretable dispatching rules for dynamic job shops via guided empirical learning," Omega, Elsevier, vol. 111(C).
- Heydar, Mojtaba & Mardaneh, Elham & Loxton, Ryan, 2022. "Approximate dynamic programming for an energy-efficient parallel machine scheduling problem," European Journal of Operational Research, Elsevier, vol. 302(1), pages 363-380.
- Çelik, Batuhan & Gul, Serhat & Çelik, Melih, 2023. "A stochastic programming approach to surgery scheduling under parallel processing principle," Omega, Elsevier, vol. 115(C).
- Kraul, Sebastian & Erhard, Melanie & Brunner, Jens O., 2024. "Optimizing physician schedules with resilient break assignments," Omega, Elsevier, vol. 129(C).
- Gökalp, E. & Gülpınar, N. & Doan, X.V., 2023. "Dynamic surgery management under uncertainty," European Journal of Operational Research, Elsevier, vol. 309(2), pages 832-844.
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Keywords
Surgical scheduling; Scheduling under uncertainty; Approximate dynamic programming; Approximate policy iteration algorithm;All these keywords.
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