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

Mahdi Yousefi Nejad Attari; Mohsen Ahmadi; Ali Ala; Elham Moghadamnia

doi:10.1016/j.compbiomed.2022.105649

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

Comput Biol Med. 2022 Aug:147:105649. doi: 10.1016/j.compbiomed.2022.105649. Epub 2022 May 22.

Authors

Mahdi Yousefi Nejad Attari¹, Mohsen Ahmadi², Ali Ala³, Elham Moghadamnia⁴

Affiliations

¹ Department of Industrial Engineering, Bonab Branch, Islamic Azad University, Bonab, Iran.
² Department of Industrial Engineering, Urmia University of Technology, Urmia, Iran.
³ Department of Industrial Engineering and Management, Shanghai Jiao Tong University, Shanghai, China. Electronic address: kanban@sjtu.edu.cn.
⁴ Department of Technology Management, Faculty of Management, Science and Research Branch, Islamic Azad University, Tehran, Iran.

PMID: 35665622
DOI: 10.1016/j.compbiomed.2022.105649

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.

Keywords: And disruption; Healthcare network location; Limited capacity; NSGA-II Algorithm; Particle swarm optimization; Robust stochastic model.

MeSH terms

Algorithms*
Health Services
Humans
Models, Theoretical*