The purpose of this study is to evaluate Exogenous Risk Factors (ERFs) affecting Key Performance Indicators (KPIs) in automotive supply chains, aiming to enhance resilience against global disruptions. The primary research question focuses on identifying and prioritizing ERFs that pose the...

This study aims to determine the most important component of economic freedom and to rank the performance of countries using multiple criteria decision-making tools within the context of fuzzy set theory. To do so, we utilize a combination of fuzzy entropy weighting and fuzzy technique for order...

We estimate the difference in direct healthcare costs of individuals diagnosed with diabetes depending on their glucose level, considering different timespans and subgroups. Using data from administrative registers of 285,450 individuals in Catalonia from 2013 to 2017, we used a fuzzy regression...

Supply Chain Disaster Management (SCDM) is essential for mitigating disruptions, ensuring business continuity, and maintaining a competitive advantage. This study introduces a comprehensive decision model to assess disaster management readiness within the upstream supply chain. Through an...

Traffic congestion (TC) disrupts everyday life, elevating stress levels, extending commute durations, diminishing productivity, hampering air quality, and reducing the overall quality of life. Congestion exacerbates already-existing problems in densely populated cities and makes efficient urban...

In the present study, we model the migration process and the changes in the market environment. The migration process is being modeled as an F -inhomogeneous semi-Markov process with fuzzy states. The evolution of the migration process takes place within a stochastic market environment with...

Recent disruptions have led to a growing interest in studying the social dimension of sustainability and its relationship to resilience within supply chains. Social sustainability is characterized as complex, often offering anomalous data and confounding variables that are impossible to...