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The results of the analysis of algorithmic models of machine learning application to the freight transportation process are given in this paper. Analysis of existing research allowed discovering a range of advantages in the application of computational intelligence in logistic systems, including increasing the accuracy of forecasting, reduction of transport costs, increasing the efficiency of cargo delivery, risks reduction, and search for key performance factors. In the research process, the main directions of application of algorithmic models of machine learning were determined.

The article is devoted to the problem of effective use of cross-docking as a technology of cargo delivery with increased time requirements, which allows to resolve the contradictions of guaranteed delivery time ensuring and the efficiency of the existing fleet of trucks. The process of delivery organization is considered as the ordering on the transport network of many discrete freight flows in the form of their phases. If qualitative and / or quantitative changes do not occur from phase to phase with the flow, then the tact of such flow is constant.