Research on the Impact of Intelligent Optimization Algorithms on Low-Carbon City Distribution Issues

DOI: https://doi.org/10.62517/jbdc.202501306

Author(s)

Lilong Xu*

Affiliation(s)

School of Computer and Artificial Intelligence, Lanzhou Industrial University, Lanzhou, Gansu, China *Corresponding Author

Abstract

Driven by the rapid urbanization and the "dual carbon" goals, the low-carbon transformation of urban distribution has become a key issue in green development. Currently, the proportion of carbon emissions from urban logistics continues to rise. Issues such as unreasonable routes, high vehicle empty load rates, and extensive scheduling in traditional distribution models exacerbate the pressure to reduce emissions, urgently requiring technological solutions. Intelligent optimization algorithms construct optimization models by simulating natural laws, enabling distribution decision-making optimization under complex constraints. Their core role lies in reducing carbon emissions through precise decision-making. Research has shown that they can affect low-carbon distribution from multiple levels: at the route level, they can shorten ineffective mileage through multi-constraint optimization, reducing carbon emissions by 10%-20%; at the scheduling level, they optimize vehicle selection and loading rates, increasing the utilization rate of new energy vehicles by over 40%; at the collaborative level, they balance "low carbon - cost - timeliness" through multi-objective optimization, outputting optimal solutions. However, the application of algorithms is limited by data quality, dynamic adaptability, and implementation costs. In the future, technological integration and measure coordination are needed to unleash their value in low-carbon distribution and provide support for the transformation of urban green logistics.

Keywords

Intelligent Optimization Algorithm; Low-Carbon City Distribution; Route Optimization; Vehicle Scheduling; Multi-Objective Optimization

References

[1] Farina M, Amato P. On the optimal solution definition for many criteria optimization problems. Toronto: Fuzzy Information Processing Society, Nafips Meeting of the North American, 2002. [2] Xu Yunsheng. Research on Evolutionary Algorithms for Solving High-dimensional Multi-objective Optimization Problems. Hunan Institute of Science and Technology, 2025. [3] Su Fengming. The Concept and Implem-entation Approaches of Low-Carbon Transportation. Transportation Deve-lopment, 2010(5):13-17. [4] Xia Yinghui, Xia Yangkun, Xu Yuhuan, et al. A Review of Low-Carbon Vehicle Routing Problem and Its Solution Algorithm. Logistics Science and Technology, 2023:1-7. [5] Liu Jianchang, Li Fei, Wang Honghai, Li Tianjun. A Review of Evolutionary High-dimensional Multi-objective Optimization Algorithms. Control and Decision, 2018, 33 (5):879-887. [6] Sun Jing, Gong Dunwei. Research Progress of Evolutionary High-dimensional Multi-objective Optimization. Control Theory and Applications, 2018, 35(7):928-938. [7] Zhou Chong, Dai Guangming, Wang Maocai. Enhanced Dominance and Densi-ty Celection Based Evolutionary Algori-thm for Many- objective Optimization Problems. Appl Intell, 2017, 48: 992-1012. [8] Yuan Y, Xu H, Yao X. A New Dominance Relation-based Evolutionary Algorithm for Many-objective Optimization. IEEE Trans Evol Comput, 2016, 20(1):16-37. [9] He Z, Yen GG, Zhang J. Fuzzy-based Pareto Optimality for Many- objective Evolutionary Algorithms. IEEE Trans Evol Comput, 2014, 18(2):269-285. [10] Yuan Tian. Research and Application of Multi-Objective Optimization Algorithm Based on MOEA/D. Chongqing University, 2022. [11] Li K, Deb K, Zhang Q. An Evolutionary Many-objective Optimization Algorithm Based on Dominance and Decomposition. IEEE Trans Evol Comput. 2015, 19(5): 694-716. [12] Qin Hao. Research on High-dimensional Multi-objective Optimization Evolutionary Algorithm Based on Metrics and Decomposition. Nanchang Hangkong University, 2022. [13] Han Min, He Yong, Zheng Danchen. Reference point-based High-dimensional multi-objective Particle Swarm optimization algorithm. Control and Decision, 2017, 32(4): 607-612. [14] Zhang Xianzi, Liu Yanmin, Liu Jun, etc. Hybrid Strategy Multi-objective Particle Swarm Optimization Algorithm Embedd-ed with Circle Mapping. Journal of Zunyi Normal University, 2023, 25(04):89-95. [15] Gao Zijian, Chu Liangyong. Research on Multi-Vehicle Type Open Vehicle Routing Problem Considering Order Priority with Time Window Science Technology and Engineering, 2024, 24(06):2521-2529. [16] Wang Chao, Yuan Jiehong. Distributed Parallel Genetic Algorithm for Solving Multi-Vehicle Routing Problem. Logistics Science and Technology, 2019, 42(10):18-20.