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Time-Dependent Vehicle Routing Optimization Incorporating Pollution Reduction Using Hybrid Gray Wolf Optimizer and Neural Networks

Author

Listed:
  • Zhongneng Ma

    (School of Electric Power Engineering, South China University of Technology, Guangzhou 510641, China)

  • Ching-Tsung Jen

    (Department of Global Business, Chinese Culture University, Taipei 111396, Taiwan)

  • Adel Aazami

    (Institute for Transport and Logistics Management, Department of Global Business and Trade, Vienna University of Economics and Business, 1020 Vienna, Austria)

Abstract

Road transport is a major contributor to air pollution, necessitating sustainable solutions for urban logistics. This study presents a time-dependent vehicle routing problem (VRP) model aimed at minimizing fuel consumption and greenhouse gas emissions while addressing stochastic customer demands. By incorporating key environmental factors such as road gradients, vehicle load, temperature, wind direction, and asphalt type, the proposed model provides a comprehensive approach to reducing transportation-related pollutants. To solve the computationally complex problem, a hybrid algorithm combining the gray wolf optimizer (GWO) and the multilayer perceptron (MLP) neural network is introduced. The algorithm demonstrates superior performance, achieving an error rate of less than 2% for medium-scale problems and significantly reducing fuel and driver costs. Sensitivity analyses reveal the profound impact of environmental parameters, with wind speed and direction altering optimal routing in over 80% of cases for large-scale instances. This research advances green logistics by integrating dynamic environmental considerations into routing decisions, balancing economic objectives with sustainability. The proposed model and algorithm offer a scalable solution to real-world challenges, enabling policymakers and logistics planners to improve environmental outcomes while maintaining operational efficiency.

Suggested Citation

  • Zhongneng Ma & Ching-Tsung Jen & Adel Aazami, 2025. "Time-Dependent Vehicle Routing Optimization Incorporating Pollution Reduction Using Hybrid Gray Wolf Optimizer and Neural Networks," Sustainability, MDPI, vol. 17(11), pages 1-27, May.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:11:p:4829-:d:1663253
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    References listed on IDEAS

    as
    1. Bektas, Tolga & Laporte, Gilbert, 2011. "The Pollution-Routing Problem," Transportation Research Part B: Methodological, Elsevier, vol. 45(8), pages 1232-1250, September.
    2. Yu Li & Xiaoxiao Lin & Jingsen Liu, 2021. "An Improved Gray Wolf Optimization Algorithm to Solve Engineering Problems," Sustainability, MDPI, vol. 13(6), pages 1-23, March.
    3. Ramos, Tânia Rodrigues Pereira & Gomes, Maria Isabel & Barbosa-Póvoa, Ana Paula, 2014. "Economic and environmental concerns in planning recyclable waste collection systems," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 62(C), pages 34-54.
    4. Erfan Babaee Tirkolaee & Alireza Goli & Abbas Mardani, 2023. "A novel two-echelon hierarchical location-allocation-routing optimization for green energy-efficient logistics systems," Annals of Operations Research, Springer, vol. 324(1), pages 795-823, May.
    Full references (including those not matched with items on IDEAS)

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