ANALYSIS OF APPROACHES TO MODELING UNCERTAINTY IN LOGISTICS INFORMATION SYSTEMS

The article examines uncertainty modeling in logistics information systems, focusing on stochastic, statistical, interval-based, and fuzzy methods. Their applications in transport flow management, demand forecasting, and resource optimization are analyzed. Integration into modern software is explored: statistical methods in PostgreSQL, stochastic modeling in AnyLogic, interval-based approaches in MATLAB and Python, and fuzzy algorithms via scikit-fuzzy. The choice depends on uncertainty type, computational resources, and accuracy needs. Statistical models handle large datasets, stochastic methods model complex scenarios, interval-based techniques ensure stability, and fuzzy systems support subjective decision-making. Future research should develop hybrid approaches and apply machine learning for optimal method selection.