Optimal Equipment Capacity Planning in the Neonatal Intensive Care Unit with Simulation-Optimization Approach

Year 2024, , 895 - 910, 01.06.2024

Müfide Narlı , Yusuf Kuvvetli , Ali Kokangül

https://doi.org/10.35378/gujs.1247829

Abstract

Capacity planning should be performed to balance investment costs and benefits of investing to meet the current and future demand in intensive care units. Having a high capacity to increase patient admission will lead to unutilized capacity in some periods, thereby increasing costs. On the other hand, patient admission requests from inborn and transported patients might be rejected due to lack of equipment. It should be considered in terms of cost-effectiveness and patient health; therefore, optimal equipment capacity must be determined. In this study, the optimal capacity planning problem has been considered for the neonatal intensive care unit of a hospital adopting the simulation-optimization approach. A discrete event simulation model is proposed for a neonatal intensive care unit in Adana, Turkey. Then, the optimization model identified the optimal numbers of incubators, ventilators, and nitric oxide devices to maximize equipment efficiency and minimize total inborn patient rejection and transport ratios. Three different resource allocations are presented, and the best is obtained from these three objectives as 72 incubators, 35 ventilators, and three nitric oxide devices. The application results obtained have revealed that the rejection and transport rate, which is found to be 1.12% in the current situation, can be reduced to 0.2% with different numbers of equipment and that equipment efficiency can be achieved with optimal numbers of equipment. The results of the study can help the decision-makers when minimum transport and rejection ratios are critical which almost intensive care units are required. Furthermore, the proposed simulation-optimization model can be adapted to different neonatal intensive care units having the same characteristics.

Keywords

Simulation-optimization, Capacity planning, Modelling, Neonatal intensive care

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