Models and algorithms for data processing in transport logistics of agricultural regions using multi-criteria evolutionary algorithms
Keywords:
simulation modeling, logistics optimization, cargo transportation, genetic algorithm, supply chainAbstract
Models and algorithms for data interaction based on a multi-criteria evolutionary algorithm are a promising direction for improving transport logistics in rural areas. Their use allows not only to increase the efficiency of processes, but also to provide higher quality of customer service. In the context of market variability and the need for rapid adaptation to new conditions, such approaches are becoming especially relevant. The future of transport logistics in agriculture depends on the ability to integrate modern technologies and optimization methods, which will allow efficient resource management and cost minimization. Evolutionary algorithms are one of the effective methods for solving problems that are computationally complex, have a large dimension and search space. The optimization process, the main task on the hybrid version of the evolutionary algorithm, for use in freight transportation planning problems. The paper proposes a new approach to solving optimization problems in logistics based on a hybrid genetic algorithm with fuzzy set integration. This method is aimed at improving the quality of solutions and accelerating the convergence process, especially in complex and uncertain conditions.
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