F-DeNETS: A Hybrid Methodology for Complex Multi-Criteria Decision-Making Under Uncertainty

In the modern business environment, where uncertainty and complexity make decision-making difficult, the need for robust, transparent and adaptable support tools is highlighted. The proposed method, named Flexible Decision Navigator for Evaluating Trends and Strategies (F-DeNETS), offers a complementary perspective to classic Artificial Intelligence (AI), Big Data and Multi-Criteria Decision-Making (MCDM) tools. Despite their broad use, these methods frequently suffer from critical sensitivities in the weighting of criteria and the handling of uncertainty, leading to compromised reliability and limited practical utility in environments with limited data availability. To bridge this gap, F-DeNETS integrates intuition and uncertainty into a transparent and statistically grounded process. It introduces a balanced approach that combines statistical evidence with human judgment, extending the boundaries of classic AI, Big Data and MCDM methods. Classic MCDM methods, although useful, are sometimes limited by subjectivity, staticity and dependence on large volumes of data. To fill this gap, F-DeNETS, a hybrid framework combining Fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL), Non-Asymptotic Fuzzy Estimators (NAFEs) and Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), transforms expert judgments into statistically sound fuzzy quantifications, incorporates dynamic adaptation to new data, reduces bias and enhances reliability. A numerical application from the shipping industry demonstrates that F-DeNETS offers a flexible and interpretable methodology for optimal decisions in environments of high uncertainty.