RSDM-AHSnet: Designing a robust stochastic dynamic model to allocating health service network under disturbance situations with limited capacity using algorithms NSGA-II and PSO. (August 2022)

• Record Type: Journal Article
• Authors: Yousefi Nejad Attari, Mahdi; Ahmadi, Mohsen; Ala, Ali; Moghadamnia, Elham
• Is Part Of: Computers in biology and medicine. Volume 147(2022)
• Publication Date: 2022-08
• Subjects: Healthcare network location -- Robust stochastic model -- Limited capacity -- NSGA-II Algorithm -- Particle swarm optimization -- And disruption; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2022.105649
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: In the present study, health services networks were classified into low-level hospitals (provision of public health services) and high-level hospitals (providing specialized health services), which are at risk of being disrupted. They refer the patients to high-level hospitals for inpatient visits or emergencies by ambulance. In the present case, patients are divided into two categories: high priority (the category in which immediate service delivery is needed) and low priority. A stochastic robust dynamic mathematical model for location and allocation of health network regarding limited capacity and disturbance is developed to reduce the total costs and include the basic features of a real problem such as limited capacity. Regarding limited capacity for hospitals, the health network needs redefinition of different layers in the disturbance situation. In this study, we reduce the total costs by reducing hospital costs and costs such as transportation and service to patients. Two metaheuristic algorithms consisting of Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Particle Swarm Optimization (PSO) have been applied to solve the model. Taguchi method design minimizes the cost of parameter tuning, including the level of factors related to the proposed. The results showed the method's applicability for large-scale problems that could evaluate different tools for decision-makers to select effective management strategies in constructing a dependable and robust healthcare network. For example, the total cost is minimized in conditions considered in the genetic algorithm, the population parameter at the highest level, 150, the intersection parameters, and the probability of mutation at the lowest level, 0.7 and 0.1. Highlighted: Designing a Robust Stochastic Dynamic Model to Allocating Health Service Network. Two metaheuristic algorithms consisting of Non-dominated Sorting NSGA-II and PSO have been applied to solve the model. Taguchi method design minimizes the cost of parameter tuning, including the level of factors related to the proposed.