https://dmait.sci-flag.com/ Decision Making and Artificial Intelligence Trends en daily 1 Tue, 23 Sep 2025 00:00:00 +0330 Tue, 23 Sep 2025 00:00:00 +0330 https://dmait.sci-flag.com/article_229756.html One of the most difficult challenges that students experience is the anxiety that they struggle with during their studies. Various factors affect or are affected by students' anxiety. Things like depression and self-esteem affect students' anxiety, and the level of anxiety also affects them. Also, students' health and their level of success during their studies are affected by their anxiety. In this article, the effects of anxiety on students' lives are first examined using visualization techniques. The effects of self-confidence and depression factors on students' anxiety are then analyzed using statistical approaches. Subsequently, a clustering analysis is performed using an unsupervised K-means machine learning approach. Based on the elbow method and silhouette score methods, an optimal number of k is determined to be five. These five clusters are students with different levels of self-esteem, depression, and anxiety. Then, the importance of external factors and subfactors affecting students' anxiety was investigated. This process was done using the fuzzy best-worst method. The most important category of factors is the environmental category environmental category with a weight of 41.5%. Also, the most significant factor is basic needs with global weight of 13%. https://dmait.sci-flag.com/article_231122.html Supporting managerial choices about demand fulfillment, cost control, and inventory control in supply chains requires effective risk management. This study creates a bi-objective optimization model that aims to reduce a supply chain network’s overall cost while also increasing its resilience by lowering unmet demand in the event of disruptions. The model integrates Additive Manufacturing (AM) as a versatile tool to reduce disruptions through on-demand production of critical parts and multi-level decision-making among manufacturers, distributors, and suppliers. The framework examines inflation-related exchange rate risks under three scenarios in order to address financial uncertainty. Blockchain-based smart contracts further improve supplier selection and transparency. The model uses the Benders decomposition algorithm to solve the resulting mixed-integer program efficiently, the ε-constraint method to handle the bi-objective formulation, and robust optimization to manage risk. A household supply chain network case study shows how integrating AM greatly increases resilience and cost effectiveness. When designing resilient networks, the results emphasize how crucial it is to take risk factors, multi-level supply chain structures, and technological enablers into account simultaneously.