Instruments and Systems: Monitoring, Control, and Diagnostics Annotation << Back Hybrid Model for Complex Assessment of Transport and Logistics Systems Parameters under Uncertainty A.V. BOZHENYUK, O.V. KOSENKO, I.A. DUBCHAK The article is devoted to the development of an approach to the comprehensive assessment of the parameters of transport and logistics systems under conditions of multifactor uncertainty. A comprehensive analysis of modern methodological approaches is given, which allowed us to identify the limitations of traditional deterministic, probabilistic and fuzzy models. Particular attention is paid to the classification of uncertainty types, including epistemic, stochastic, linguistic and dynamic, which allowed us to structure risk factors for subsequent modeling. The main result of the study is a hybrid model that combines the apparatus of fuzzy logic for processing expert assessments and probabilistic methods for analyzing quantitative data. The model is implemented as a multi-level architecture that includes modules for probabilistic processing, fuzzy analysis, data integration, and decision making. A comprehensive indicator for assessing parameters is proposed that combines fuzzy and probabilistic characteristics, with the ability to adapt to various types of distributions. The practical significance of the study is confirmed by a series of computational examples demonstrating the application of the model for assessing the reliability of routes under various uncertainty scenarios. A comparative analysis of methods (hybrid, probabilistic, fuzzy, machine learning) revealed the advantages of the proposed approach in balancing accuracy, interpretability, and requirements for the initial data. The proposed solutions can be used to increase the sustainability of transport and logistics systems, minimize risks and optimize logistics processes in an unstable external environment. Keywords: Transport and logistics system, uncertainty, hybrid model, fuzzy logic, fuzzy graph, probabilistic analysis, complex indicator, multivariate analysis. DOI: 10.25791/pribor.9.2025.1614 Pp. 32-41.