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June 20, 2026Sustainable Energy ResearchOpen Access

Optimization scheme for urban logistics emission reduction based on improved ACO algorithm and time window constraints

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Authors

SWSisheng Wan

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Overview

Randomized trial optimizes emission reduction in urban logistics, indicating a pathway for greener supply chains.

Key Points

  • This research focuses on optimizing urban logistics to reduce carbon emissions and total costs through improved algorithms.
  • Collaborative optimization of logistics distribution center location and route
  • Used K-means clustering for area division and center of gravity for site selection
  • Applied I-ACO combining variable neighborhood search and dynamic pheromone updating
  • I-ACO reduced total costs by 10.2% and carbon emission costs by 17.3% compared to traditional ACO
  • With 4 clusters, total cost was 14,800 yuan, 13.95% lower than traditional random clustering
  • Total carbon emissions reduced by 25% with improved plan when demand concentration was 60%

Cite This Study

Sisheng Wan (2026) studied this question.

synapsesocial.com/papers/6a362ee4db0793dc1a53679bhttps://doi.org/10.1186/s40807-026-00247-6
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