On the use of partitioning for scheduling of surgeries in the inpatient surgical department

Abstract

creases the elective cancellation rate. Second, the ability to use the available capacity of the ORs as much as possible is key to reducing elective waiting times. Third, partitioning might slightly worsen the waiting times of emergency patients, while the slightly negative impact on emergency patients decreases when the number of ORs is higher. Fourth, the beneficial impact of partitioning on elective patients increases with an increased patient demand. Last, for the settings considered in this study there was no benefit in partitioning the elective patients into more than two groups.

Capacity and surgery partitioning: An approach for improving surgery scheduling in the inpatient surgical department

“…Meanwhile, partitioning tends to increase the OR overtime over pooling, but this can be reduced by imposing a stricter tolerance on overtime. Similarly to previous findings (Wang et al 2022), we show that the more predictable surgery group can be efficiently performed. In the partitioning, the overall surgery schedule is robust against uncertain surgery durations and non-elective arrivals.…”

“…There are articles that cluster surgeries or surgery types in homogeneous surgery groups and then schedule them according to those surgery groups (Hans et al 2008, Visintin et al 2017, Benchoff et al 2017, Heider et al 2020, Schneider et al 2020, Wang et al 2022. These articles are similar to the main idea of ours since they create the partitioning of surgery types and OR blocks to a large degree.…”

“…Our study also goes beyond Wang et al (2022) who investigate the concept of partitioning in scheduling inpatient surgeries by using a simulation study. First, they partition surgery types based on the surgery duration variability, while we consider both the surgery duration variability and length.…”

“…One of the major problems associated with operational surgery scheduling is uncertainty in terms of surgery durations and patient arrivals. Various operations research methods have been applied to deal with the uncertainty in surgery scheduling, e.g., simulation (Ozen et al 2016, Samudra et al 2017, Bai et al 2022, Wang et al 2022, stochastic optimization models (Rath et al 2017, Khaniyev et al 2020, Zhang et al 2021, Bai et al 2022, robust optimization (Rath et al 2017, Bandi andGupta 2020), heuristics (Freeman et al 2016, Jung et al 2019, Khaniyev et al 2020, Bai et al 2022 and Markov decision process (Huh et al 2013, Liu et al 2019, Zhang et al 2021. Our research also uses MDP, but we are different from the literature in the following aspects.…”

“…Due to this complexity, a simulation model that simulates the execution of the surgery schedule is developed to approximate P(c t ) and all performance measures by a sufficiently large number of runs. Since the simulation model shares a similar conceptual model as the one in Wang et al (2022) where the model has experienced thorough verification and validation, we believe that the developed model can yield an accurate approximation of those parameters. The simulation model is described in Section 1 of Supplementary Materials.…”