Dynamic resectorization to improve utility of healthcare systems.

Balancing is an essential challenge in healthcare systems that requires effective strategies. This study aims to address this crucial issue by suggesting a practical approach. We show the potential of balancing a regional healthcare system to improve its utility. We consider a regional healthcare system comprising multiple hospitals with different sizes, capacities, quality of service, and accessibility. We define a utility function for the system based on the sectorization concept, which endeavors to form a balance between hospitals in terms of essential outputs such as waiting times and demands. The dynamic nature of the system means that this balance degrades over time, necessitating periodic sectorization, which is called resectorization. Our methodology stands out for incorporating resectorization as a dynamic strategy, enabling more flexible and responsive adaptations to continuously changing healthcare needs. Unlike previous studies, based on a system-oriented approach, our resectorization scenarios include the periodic closure of some hospitals. This enables us to enhance both the capacity and quality of healthcare facilities. Furthermore, in contrast to other studies, we investigate the states of diminishing demand throughout the resectorization process. To provide empirical insights, we conduct a simulation using data from a real-world case study. Our analysis spans multiple time periods, enabling us to dynamically quantify the utility of the healthcare system. The numerical findings demonstrate that substantial utility improvements are attainable through the defined scenarios. The study suggests a practical solution to the critical challenge of balancing issues in regional healthcare systems.

A comparison among optimization software to solve bi-objective sectorization problem.

In this study, we compare the performance of optimization software to solve the bi-objective sectorization problem. The used solution method is based on an approach that has not been used before in the literature on sectorization, in which, the bi-objective model is transformed into single-objective ones, whose results are regarded as ideal points for the objective functions in the bi-objective model. Anti-ideal points are also searched similarly. Then, using the ideal and anti-ideal points, the bi-objective model is redefined as a single-objective one and solved. The difficulties of solving the models, which are basically non-linear, are discussed. Furthermore, the models are linearized, in which case how the number of variables and constraints changes is discussed. Mathematical models are implemented in Python's Pulp library, Lingo, IBM ILOG CPLEX Optimization Studio, and GAMS software, and the obtained results are presented. Furthermore, metaheuristics available in Python's Pymoo library are utilized to solve the models' single- and bi-objective versions. In the experimental results section, benchmarks of different sizes are derived for the problem, and the results are presented. It is observed that the solvers do not perform satisfactorily in solving models; of all of them, GAMS achieves the best results. The utilized metaheuristics from the Pymoo library gain feasible results in reasonable times. In the conclusion section, suggestions are given for solving similar problems. Furthermore, this article summarizes the managerial applications of the sectorization problems.