Khalilzadeh Mohammad, Bahari Arman
CENTRUM Católica Graduate Business School, Pontificia Universidad Católica del Perú, Lima, Peru.
Department of Industrial Engineering, Faculty of Industry and Mining, University of Sistan and Baluchestan, Zahedan, Iran.
Health Serv Insights. 2023 Aug 30;16:11786329231195690. doi: 10.1177/11786329231195690. eCollection 2023.
To prevent the great dangers caused by emergency situations, providing rapid and high-quality emergency aid highly depends on the location of emergency medical centers. The purpose of this research is to present a multi-objective mathematical programing model based on the minimum P-envy algorithm to locate and construct emergency medical services (EMS). Maximizing the coverage in order to increase the probability of survival of different categories of patients, minimizing the costs of constructing EMS and optimizing the ratio of regions having the right to emergency medical services is one of the fundamental challenges in the health care system of countries. In this paper, a model for maximum utilization of EMS considering budget limitations is presented. In this study, since the problem is NP-Hard, the Genetic Algorithm (GA) and Simulated Annealing (SA) algorithm were used to solve this problem. The parameters of the metaheuristic algorithms were tuned using the Taguchi method. Several instance problems were solved to compare the performance of 2 algorithms. The results demonstrate that the validity of the proposed model. Also, the mean of the solutions obtained by GA for small, medium, and large-size problems are better than the SA algorithm. Also, the GA algorithm obtained more efficient solutions than the SA algorithm.
为防止紧急情况造成巨大危害,提供快速且高质量的紧急援助在很大程度上取决于急救医疗中心的选址。本研究的目的是提出一种基于最小P-嫉妒算法的多目标数学规划模型,用于急救医疗服务(EMS)的选址与建设。在各国医疗保健系统中,最大化覆盖范围以提高不同类型患者的生存概率、最小化急救医疗服务建设成本以及优化有权获得急救医疗服务的区域比例是一项基本挑战。本文提出了一个考虑预算限制的急救医疗服务最大利用模型。在本研究中,由于该问题是NP难问题,因此使用遗传算法(GA)和模拟退火(SA)算法来解决此问题。使用田口方法对元启发式算法的参数进行了调整。通过求解几个实例问题来比较这两种算法的性能。结果证明了所提模型的有效性。此外,遗传算法针对小、中、大型问题所获得解的均值优于模拟退火算法。而且,遗传算法比模拟退火算法获得了更有效的解。