Developing a bi-objective resilience relief logistic considering operational and disruption risks: a post-earthquake case study in Iran.

Today, according to the occurrence of numerous disasters in allover over the world, designing the proper and comprehensive plan for relief logistics has received a lot of attention from crisis managers and people. Besides, considering resilience capability along with operational and disruption risks leads to the robustness of the humanitarian relief chain (HRC), and this comprehensive framework ensures the essential supplies delivery to the beneficiaries and is close to real-world problems. The resilience parameters used for the second objective are obtained by a strong Best Worst Method (BWM). Another supposition of the model is the consideration of uncertainty in all stages of the proposed problem. Moreover, the multiple disasters (sub-sequent minor post disasters) which can increase the initial demand are considered. Furthermore, the proposed model is solved using three well-known metaheuristic algorithms includes non-dominated sorting genetic algorithm (NSGA-II), network reconfiguration genetic algorithm (NRGA), and multi-objective particle swarm optimization (MOPSO), and their performance is compared by several standard multi-objective measure metrics. Finally, the obtained results show the robustness of the proposed approaches, and some directions for future researches are provided.

A Regionalization Model to Increase Equity of Access to Maternal and Neonatal Care Services in Iran.

OBJECTIVES: Access to maternal and neonatal care services (MNCS) is an important goal of health policy in developing countries. In this study, we proposed a 3-level hierarchical location-allocation model to maximize the coverage of MNCS providers in Iran. METHODS: First, the necessary criteria for designing an MNCS network were explored. Birth data, including gestational age and birth weight, were collected from the data bank of the Iranian Maternal and Neonatal Network national registry based on 3 service levels (I, II, and III). Vehicular travel times between the points of demand and MNCS providers were considered. Alternative MNCS were mapped in some cities to reduce access difficulties. RESULTS: It was found that 130, 121, and 86 MNCS providers were needed to respond to level I, II, and III demands, respectively, in 373 cities. Service level III was not available in 39 cities within the determined travel time, which led to an increased average travel time of 173 minutes to the nearest MNCS provider. CONCLUSIONS: This study revealed inequalities in the distribution of MNCS providers. Management of the distribution of MNCS providers can be used to enhance spatial access to health services and reduce the risk of neonatal mortality and morbidity. This method may provide a sustainable healthcare solution at the policy and decision-making level for regional, or even universal, healthcare networks.