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Machine learning multi-objective optimization for time-dependent green vehicle routing problem

Author

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  • Nyako, Sidonie Ienra
  • Tayachi, Dalila
  • Abdelaziz, Fouad Ben

Abstract

This study explores the Multi-Objective Time-Dependent Green Vehicle Routing Problem (MOTDGVRP) within the framework of energy economics, focusing on optimizing fuel consumption and transportation efficiency. Our model minimizes total distance, transportation time, and fuel consumption, all of which are critical for reducing energy costs and environmental impact. We introduce methodologies for calculating travel time and fuel consumption, considering time-dependent speed variations across different periods. Key factors influencing fuel consumption include vehicle load, dynamic traffic speeds, and distance traveled, reflecting real-world energy use in logistics. Given the NP-hard nature of the problem, we employ Non-dominated Sorting Genetic Algorithm 2 (NSGA-2) and an NSGA-2 enhanced with Machine Learning (MLNSGA-2) to optimize routing decisions. The originality of this study lies in the integration of machine learning (ML) in vehicle routing optimization,which enhances solution quality and accelerates computational performance. While ML applications in routing are growing, their use in Vehicle Routing related models remains novel. Additionally, the model accounts for time-dependent speed variations, addressing real-world traffic dynamics that significantly impact fuel consumption and delivery efficiency. The combination of ML-enhanced optimization with time-sensitive routing presents a new approach to energy-efficient transportation. From an energy economics perspective, our findings provide valuable insights for optimizing energy use in logistics, reducing operational costs, and promoting sustainable transportation. The integration of machine learning-driven optimization offers a scalable method for enhancing energy efficiency in supply chains, contributing to both economic and environmental objectives.

Suggested Citation

  • Nyako, Sidonie Ienra & Tayachi, Dalila & Abdelaziz, Fouad Ben, 2025. "Machine learning multi-objective optimization for time-dependent green vehicle routing problem," Energy Economics, Elsevier, vol. 148(C).
    • Handle: RePEc:eee:eneeco:v:148:y:2025:i:c:s0140988325004554
    DOI: 10.1016/j.eneco.2025.108628
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    References listed on IDEAS

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    1. G. Clarke & J. W. Wright, 1964. "Scheduling of Vehicles from a Central Depot to a Number of Delivery Points," Operations Research, INFORMS, vol. 12(4), pages 568-581, August.
    2. Raeesi, Ramin & Zografos, Konstantinos G., 2019. "The multi-objective Steiner pollution-routing problem on congested urban road networks," Transportation Research Part B: Methodological, Elsevier, vol. 122(C), pages 457-485.
    3. Suzuki, Yoshinori & Lan, Bo, 2018. "Cutting fuel consumption of truckload carriers by using new enhanced refueling policies," International Journal of Production Economics, Elsevier, vol. 202(C), pages 69-80.
    4. Sun, Peng & Veelenturf, Lucas P. & Dabia, Said & Van Woensel, Tom, 2018. "The time-dependent capacitated profitable tour problem with time windows and precedence constraints," European Journal of Operational Research, Elsevier, vol. 264(3), pages 1058-1073.
    5. Nemec-Boehm, Rebecca L. & Cash, Sean B. & Anderson, Bruce T. & Ahmed, Selena & Griffin, Timothy S. & Orians, Colin M. & Robbat, Albert Jr. & Stepp, Richard A. & Han, Wenyan, 2014. "Climate change, the monsoon, and tea yields in China," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 170486, Agricultural and Applied Economics Association.
    6. Bilgin, Doga & Ellwanger, Reinhard, 2024. "A simple model of global fuel consumption," Energy Economics, Elsevier, vol. 130(C).
    7. Ruibin Bai & Xinan Chen & Zhi-Long Chen & Tianxiang Cui & Shuhui Gong & Wentao He & Xiaoping Jiang & Huan Jin & Jiahuan Jin & Graham Kendall & Jiawei Li & Zheng Lu & Jianfeng Ren & Paul Weng & Ning Xu, 2023. "Analytics and machine learning in vehicle routing research," International Journal of Production Research, Taylor & Francis Journals, vol. 61(1), pages 4-30, January.
    8. Christian L. E. Franzke, 2014. "Nonlinear climate change," Nature Climate Change, Nature, vol. 4(6), pages 423-424, June.
    9. Asghari, Mohammad & Mirzapour Al-e-hashem, S. Mohammad J., 2021. "Green vehicle routing problem: A state-of-the-art review," International Journal of Production Economics, Elsevier, vol. 231(C).
    10. Demir, Emrah & Bektaş, Tolga & Laporte, Gilbert, 2014. "A review of recent research on green road freight transportation," European Journal of Operational Research, Elsevier, vol. 237(3), pages 775-793.
    11. Mohammad Asghari & Seyed Mohammad Javad Mirzapour Al-E-Hashem, 2021. "Green vehicle routing problem: A state-of-the-art review," Post-Print hal-03182944, HAL.
    12. Fouad Ben Abdelaziz & Hatem Masri & Houda Alaya, 2017. "A recourse goal programming approach for airport bus routing problem," Annals of Operations Research, Springer, vol. 251(1), pages 383-396, April.
    13. Franceschetti, Anna & Demir, Emrah & Honhon, Dorothée & Van Woensel, Tom & Laporte, Gilbert & Stobbe, Mark, 2017. "A metaheuristic for the time-dependent pollution-routing problem," European Journal of Operational Research, Elsevier, vol. 259(3), pages 972-991.
    14. Zhang, Jianghua & Zhao, Yingxue & Xue, Weili & Li, Jin, 2015. "Vehicle routing problem with fuel consumption and carbon emission," International Journal of Production Economics, Elsevier, vol. 170(PA), pages 234-242.
    15. Soysal, Mehmet & Bloemhof-Ruwaard, Jacqueline M. & Bektaş, Tolga, 2015. "The time-dependent two-echelon capacitated vehicle routing problem with environmental considerations," International Journal of Production Economics, Elsevier, vol. 164(C), pages 366-378.
    16. Ichoua, Soumia & Gendreau, Michel & Potvin, Jean-Yves, 2003. "Vehicle dispatching with time-dependent travel times," European Journal of Operational Research, Elsevier, vol. 144(2), pages 379-396, January.
    17. Marius M. Solomon, 1987. "Algorithms for the Vehicle Routing and Scheduling Problems with Time Window Constraints," Operations Research, INFORMS, vol. 35(2), pages 254-265, April.
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    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • L91 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Transportation: General
    • Q53 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Air Pollution; Water Pollution; Noise; Hazardous Waste; Solid Waste; Recycling
    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise

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