Multiobjective Optimization for Vehicle Routing Optimization Problem in Low-Carbon Intelligent Transportation

Abstract

The work aims to reduce the energy consumption and carbon emissions generated during the urban logistics transportation and distribution and make the actual path planning flexible. Based on Vehicle Routing Problem (VRP), the routing problem of distribution vehicles is optimized under satisfying customers’ cargo demand and time requirements. Because Non-dominated Sorting Genetic Algorithm (NSGA-II) reduces the complexity of non-inferior sorting genetic algorithm and is characterized by fast running speed and good convergence, it is deeply improved. NSGA-II algorithm based on Multifactorial Evolutionary Algorithm (MFEA) (M-NSGA-II) is proposed. In terms of the solution of the stability of the optimal values of four target functions, including distribution cost, customer satisfaction, fuel conservation, and carbon emission, the lowest distribution costs of M-NSGA-II algorithm in ten experiments were all lower than those of other three standard algorithms. The solution duration of M-NSGA-II algorithm was 85.2s and the corresponding average frontier value amounted to 20. The multi-objective path optimization model designed is of great value for reducing carbon emissions under satisfying customers’ cargo demand and time requirements.