Data-Driven Approaches to Energy Utilization Efficiency Enhancement in Intelligent Logistics

With the rapid development of intelligent logistics, new challenges and opportunities are presented for energy utilization efficiency improvement. This study explores the feasibility and effectiveness of using data-driven methods to improve energy utilization efficiency in an intelligent logistics environment and provides theoretical support and practical guidance for achieving the sustainable development of optimized logistics management procedures. First, a dataset was established by collecting relevant data in the optimized logistics management procedure, including transportation information and energy consumption data. Then, data analysis and mining techniques are used to conduct an in-depth dataset analysis to reveal the influencing factors of energy utilization efficiency and potential optimization directions. Then, strategies and methods for energy utilization efficiency improvement are designed by combining intelligent optimization algorithms. Finally, simulation experiments and case studies are utilized to verify the effectiveness and feasibility of the proposed methods. The results show that using data-driven methods can significantly improve the energy utilization efficiency of optimized logistics management procedures, reduce logistics costs, and enhance the sustainability and competitiveness of the system. Through in-depth analysis and empirical research, a series of actionable optimization strategies are proposed, providing new ideas and methods for optimizing energy and logistics management procedures. These results significantly promote the sustainable development of optimized logistics management procedures and enhance competitiveness.

@article{Long2024,
  title     = {Data-Driven Approaches to Energy Utilization Efficiency Enhancement in Intelligent Logistics},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {8},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Xuan Long},
  doi       = {10.14569/IJACSA.2024.0150850},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150850}
}