Data-driven analytical framework for optimization of technical and operational efficiency in liner shipping

The operation of maritime transport systems is characterized by significant variability in cargo flows, which directly affects the vessel utilization rate and the overall operational performance of the fleet. This study presents an analytical framework based on data analysis designed to optimize the technical and operational efficiency of container ships under conditions of uncertain market demand where proposed approach integrates local and aggregate performance indicators with a stochastic representation of fluctuations in container flows. The mathematical model for evaluating vessel performance has been developed, which includes the spatial allocation of containers across route segments and an optimization procedure implemented using nonlinear programming methods and accounts for a set of operational constraints, including vessel carrying capacity and demand limits, ensuring an adequate representation of real-world liner shipping conditions. Scenario analysis and simulation modeling revealed significant discrepancies between projected and actual operational results, showing that efficiency gains come primarily from a more rational distribution of existing cargo flows rather than from increasing total transport volumes. The developed toolkit has practical value for shipping companies, as it allows for improving the quality of management decisions regarding fleet deployment, route planning, and capacity management under conditions of unstable demand.