@article{4644,
  author = {Gong Ning},
  title = {Advanced Heuristic Modeling for Customer value In Green Transportation},
  journal = {Journal of Information Technology Review},
  year = {2026},
  volume = {17},
  number = {1},
  doi = {https://doi.org/10.6025/jitr/2026/17/1/10-21},
  url = {https://www.dline.info/jitr/fulltext/v17n1/jitrv17n1_2.pdf},
  abstract = {Customer value evaluation is a critical task for new energy vehicle enterprises seeking to optimize resource
allocation, improve customer relationship management, and enhance long term profitability under
increasingly competitive market conditions. Traditional customer value models often rely on static parameters
and heuristic weighting schemes, which struggle to scale effectively under large customer volumes and high
dimensional data. To address these limitations, this paper proposes a customer value evaluation model
based on an improved genetic algorithm (GA). Building on the classical GA framework, an adaptive genetic
algorithm is introduced in which crossover and mutation probabilities are dynamically adjusted according
to population fitness characteristics, thereby enhancing global search capability and convergence stability.
The proposed model encodes customer characteristic parameters as chromosomes and employs a fitnessdriven
evolutionary process to identify optimal parameter combinations for customer value assessment.
Simulation experiments conducted on a representative new energy vehicle enterprise dataset demonstrate
that the improved genetic algorithm converges faster and achieves higher accuracy than the traditional GA.
The results confirm that the proposed approach effectively improves the precision and robustness of customer
value evaluation, providing a scalable and intelligent decision support tool for enterprise customer
management.},
}