DEVELOPMENT AND SOFTWARE IMPLEMENTATION OF A RAILWAY ROUTING MODEL

Abstract

Under current operating conditions of Ukrzaliznytsia, routing strategy and route selection play a key role in ensuring the economic efficiency of freight transportation. At present, routes are predominantly selected according to the shortest-path principle, which minimizes fuel consumption for transportation and reduces the wear of traction units and other rolling stock. Consequently, customers pay the lowest possible delivery cost. However, such routes are typically fixed for a predefined period and do not support dynamic adjustment, which leads to several issues, including the failure to account for the current technical condition of rolling stock on specific sections and their actual congestion levels during transportation. These problems have become particularly acute as a result of the full-scale invasion of Ukraine by the Russian Federation, which caused the destruction of parts of the railway infrastructure, including tracks and bridges, as well as damage to or complete loss of part of the traction rolling stock of Ukrzaliznytsia and other railway operators. At the same time, alternative routing approaches remain insufficiently explored due to the limited number of studies devoted to strategic management of transportation processes. This paper describes the development and implementation of a software model of railway system operations aimed at enabling experimental investigation of various hypotheses regarding alternative routing approaches. This provides a basis for solving a scientific and applied problem of optimizing freight rail transportation through the design of flexible management strategies. The study is based on a synthesis of graph theory (representation of the network as a weighted multidigraph), discrete-event simulation (DES) for analyzing process dynamics, and mixed-integer linear programming (MILP) for generating benchmark performance indicators. A hybrid threshold-based dispatching policy is implemented, relying on parameters of minimum train fill level and maximum waiting time, thereby balancing node capacity utilization and delivery times. A specialized simulation framework has been developed in Python that integrates the event lifecycle (Spawn, Form, Depart, Arrive) and enables testing of intelligent control strategies in a simulated environment. The practical significance of the research lies in the possibility of using the developed toolkit for quantitative evaluation of different routing and train formation strategies at classification yards. The created software complex serves as a fundamental platform for further research aimed at minimizing average rolling stock turnaround time and node-related dispatch delays in real logistics systems, thereby improving the economic efficiency of freight transportation.