چكيده به لاتين
The goal of health officials (agent) is to solve the problems of patients and cure them as well as reassuring them about the curing procedure via the designing of efficient and responsive systems from which people as their main stakeholders expect a lot. Because of unexpected events such as system distruptions, the reliability of systems at the designing stage is very important. Since resilient design of health and curing networks is an effective component of social satisfaction, especially in health and curing networks, reliability is of a great importance. Nowadays, quantitative models are widely used for preparedness against the risk of disruption by managers and researchers of the health management field. Because of high imapct of health management systems on the public satisfaction, a sustainable design is very important. Lack of a mathematical model in which human resource allocation, patient flow, and network design can be considered in simultaneously, is the motivation of this study. The aim of this research is to present a mathematical model which maximize service level through reducing the number of unmet patients, waiting patients, and patients transferred among different hospitals in a health network. It was assumed that the network is a multi-hospital network supervised by a unique public management and each hospital has some departments; furthermore, several diseases were considered that each of them needs several separate steps to be cured. Particle Swarm Optimization (PSO) metaheuristic algorithm is developed because of complexity and then validated in comparison to exact methods in small size. Next, the developed algorithm is employed to solve a case study. The case study composed of three hospitals –under the supervision of Shahid Beheshti Univresity of Medical Sciences, Tehran, Iran- and three most common cancers. Mont Carlo simulation was used to produce disruption event scenarios as a result of data limitation. Dominated scenarios was identified based on a scenario reduction model. Finally, the results showed the effectiveness of the model.
