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December 19, 2025Open Access

MCAH-ACO: A Multi-Criteria Adaptive Hybrid Ant Colony Optimization for Last-Mile Delivery Vehicle Routing

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Authors

DCDaniel I. ChuUniversity of Alabama at BirminghamXCXinyu ChenCentral South UniversityLBLin-Yuan BaiPLA Army Engineering University

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Overview

Algorithm shows reduced costs and safety-critical events in last-mile delivery routing, indicating improved vehicle efficiency.

Key Points

  • The research aims to develop a balanced routing solution for last-mile delivery that considers multiple criteria.
  • Developed MCAH-ACO, an optimization algorithm for vehicle routing.
  • Utilized a Multiple Traveling Salesman Problem framework.
  • Implemented multi-criteria pheromone decomposition and adaptive weight balancing.
  • Incorporated 2-opt local search with an elite archive for solution diversity.
  • Achieved 12.3% lower total cost compared to the baseline.
  • Recorded 18.7% fewer safety-critical events than the strongest baseline.
  • Maintained competitive runtime.

Cite This Study

Chu et al. (2025) studied this question.

synapsesocial.com/papers/69449a922f0218eca9508618https://doi.org/10.20944/preprints202512.1327.v1
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Also Consider

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  1. 1Hybrid metaheuristic optimization for multi-criteria truck–drone collaborative routing in sustainable supply chains2026
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  3. 3An adaptive and hybrid Ant colony optimization algorithm for multi-constrained QoS routing in MANETs2026
  4. 4An Improved ACO for the Urban Logistics Delivery Problem Considering Dynamic Traffic Conditions and Customer Service Quality2026
  5. 5Ant-Inspired Route Optimization for Last Mile Delivery2024 · 6 citations