Dynamic Surgery Management under Uncertainty

Elvan Gokalp, Nalan Gulpinar, Xuan Vinh Doan

Research output: Contribution to journalArticlepeer-review


Real-time surgery management involves a complex and dynamic decision-making process. The duration of surgeries in many cases cannot be known until the surgery has actually been completed. Furthermore, disruptions such as equipment failure or the arrival of a non-elective surgery can occur simultaneously. Thus, the assignment of surgeries needs to be updated, as and when disruptions occur, to minimize their effects. In this paper, we present a stochastic dynamic programming approach to the surgery allocation problem with multiple operating rooms under uncertainty. Given an elective list for the day, the dynamic optimization model minimizes the number of surgeries not carried out by the end of the shift and the total waiting times of patients during the day weighted according to their urgency level. Due to the curse of dimensionality, we apply an approximate dynamic programming algorithm to solve the stochastic dynamic surgery management model. Computational experiments are designed to demonstrate the performance of the proposed algorithm and its applicability to practical settings. The results show that the approximate dynamic programming algorithm provides a good approximation to the optimum policy and leads to some managerial insights.

Original languageEnglish
Pages (from-to)832-844
Number of pages13
JournalEuropean Journal of Operational Research
Issue number2
Early online date10 Jan 2023
Publication statusPublished - 1 Sept 2023


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