Dynamic Capacity Planning of Hospital Resources under COVID-19 Uncertainty using Approximate Dynamic Programming

Elvan Gokalp, Selim M. Cakir, Hasan Satis

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Abstract

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.

Original languageEnglish
Pages (from-to)13-25
JournalJournal of the Operational Research Society
Volume75
Issue number1
Early online date19 Jan 2023
DOIs
Publication statusPublished - 31 Jan 2024

Keywords

  • Stochastic programming
  • dynamic programming
  • health services
  • simulation

ASJC Scopus subject areas

  • Statistics, Probability and Uncertainty
  • Modelling and Simulation
  • Strategy and Management
  • Management Science and Operations Research

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