An application of interactive fuzzy optimization model for redesigning supply chain for resilience.

Supply chain disruptions compel professionals all over the world to consider alternate strategies for addressing these issues and remaining profitable in the future. In this study, we considered a four-stage global supply chain and designed the network with the objectives of maximizing profit and minimizing disruption risk. We quantified and modeled disruption risk as a function of the geographic diversification of facilities called supply density (evaluated based on the interstage distance between nodes) to mitigate the risk caused by disruptions.

A systematic literature review of quantitative models for sustainable supply chain management.

Supply chain management is the basis for the execution of operations, being considered as the core of the business function in the 21st century. On the other hand, at present, factors such as the reduction of natural resources, the search for competitive advantages, government laws and global agreements, have generated a greater interest in the sustainable development, which, in order to achieve it, industries need to rethink and plan their supply chain considering a path of sustainability. So sustainable supply chain management emerges as a means to integrate stakeholders' concern for profit and cost reduction with environmental and social requirements, attracting significant interest among managers, researchers and practitioners.

Financial risk of increasing the follow-up period of breast cancer treatment currently covered by the Social Protection System in Health in México.

Background: The objective of this work is to estimate the financial impact of increasing the monitoring period for breast cancer, which is financed by the Sistema de Protección Social en Salud (SPSS-Social Protection System in Health).

Methods: A micro-simulation model was developed to monitor a cohort of patients with breast cancer, and also an estimation was made on the probability of surviving the monitoring period financed by the SPSS. Using the Monte Carlo simulation, the maximum expected cost was estimated to broaden such monitoring.