Hybrid Technology for Synthesis of Transport-Logistics Systems Based on Machine Learning and Simulation
DOI:
https://doi.org/10.52575/2687-0932-2024-51-3-670-681Keywords:
machine learning, artificial neural networks, simulation, transport and logistics system, railway transportAbstract
The article raises the issues of integration of intelligent information technologies and computer modeling for solving complex applied problems. The authors propose to use the flexible capabilities of modern artificial neural networks and simulation in the tasks of planning transport and logistics systems. In particular, the task of developing software tools for information and analytical support of transport logistics management of a distributed production association is considered. The article focuses on solving the applied problem of planning effective configurations of the railway transport and logistics system. To solve this problem, the authors propose an original hybrid technology for the synthesis of multicomponent transport and logistics systems based on machine learning and simulation modeling. A brief overview of neural network methods and technologies used to solve applied problems of operational management and planning in transport logistics is provided. The authors propose a formalization of the task of planning the configuration of a transport and logistics system in a general formulation and in a particular case. The developed computer model for simulating scenarios for the implementation of a vehicle traffic plan on a given configuration of a transport and logistics network is considered.
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