Dynamic capacity planning of hospital resources under COVID-19 uncertainty using approximate dynamic programming

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Gokalp E., Cakir M. S., SATIŞ H.

JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY, cilt.75, sa.1, ss.13-25, 2024 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 75 Sayı: 1
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1080/01605682.2023.2168570
  • Dergi Adı: JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus, IBZ Online, International Bibliography of Social Sciences, Periodicals Index Online, ABI/INFORM, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Compendex, Computer & Applied Sciences, INSPEC, zbMATH
  • Sayfa Sayıları: ss.13-25
  • Anahtar Kelimeler: dynamic programming, health services, simulation, Stochastic programming
  • Gazi Üniversitesi Adresli: Evet

Özet

COVID-19 pandemic has resulted in an inflow of patients into the hospitals and overcrowding of healthcare resources. Healthcare managers increased the capacities reactively by utilizing expensive but quick methods. Instead of this reactive capacity expansion approach, we propose a proactive approach considering different realizations of demand uncertainties in the future due to COVID-19. For this purpose, a stochastic and dynamic model is developed to find the right amount of capacity increase in the most critical hospital resources. Due to the problem size, the model is solved with Approximate Dynamic Programming. Based on the data collected in a large tertiary hospital in Turkey, the experiments show that ADP performs better than a benchmark myopic heuristic. Finally, sensitivity analysis is performed to explore the impact of different epidemic dynamics and cost parameters on the results.