Traffic congestion, road gradients and vehicle load significantly contribute to carbon emissions in urban logistics. However, existing research lacks a comprehensive consideration of these factors. This study introduces and formulates a time-dependent low-carbon vehicle routing problem on road network, which integrates the spatial–temporal complexity of these factors for more accurate emission estimation, and introduces path flexibility into route optimization strategy. The model’s effectiveness and stability are validated through experiments on synthetic instances and a real-world case. By solving the problem with a tailored time-dependent adaptive large neighborhood search approach, the proposed model mitigates the underestimation associated with models that omit these factors, making low-carbon optimization in urban logistics more comprehensive and realistic. Furthermore, a departure time analysis reveals that strategically scheduling the departure time can yield significant additional emission reductions of up to 14.9% on weekdays and 18.9% on weekends. This provides a validated, data-driven framework for enhancing the sustainability of urban logistics.
https://doi.org/10.1016/j.cie.2026.112311Cite as:
@article{Luo_2026,
title={Time-dependent low-carbon vehicle routing problem for urban logistics: integrating traffic congestion, road gradient and vehicle load},
volume={221},
ISSN={0360-8352},
url={http://dx.doi.org/10.1016/j.cie.2026.112311},
DOI={10.1016/j.cie.2026.112311},
journal={Computers & Industrial Engineering},
publisher={Elsevier BV},
author={Luo, Chenxin and Tao, Fengming and Yuan, Beifei},
year={2026},
month=Nov,
pages={112311}
}