An AI-Driven Approach to Green and Resilient Vehicle Routing: A Comparative Study of Genetic Algorithms and Reinforcement Learning
DOI:
https://doi.org/10.54097/qsmkb612Keywords:
Green and Resilient Logistics; Dynamic Vehicle Routing; Carbon Emission Reduction; Deep Reinforcement Learning.Abstract
Green logistics has emerged as a pivotal pillar of sustainable supply chain management, especially in dynamic and uncertain operational environments. However, existing research rarely integrates green objectives and supply chain resilience into a unified vehicle routing optimization framework, leaving a critical methodological gap for addressing real-world logistics uncertainties. To bridge this gap, this study aims to propose an AI-driven methodological framework for green and resilient vehicle routing, and conducts a comparative evaluation between Genetic Algorithm (GA) and reinforcement learning (RL) based on the Solomon C101 benchmark dataset. The optimization objectives are to minimize total travel distance, operational cost and carbon emissions under vehicle capacity constraints. Furthermore, dynamic disruption scenarios—including node unavailability, demand surges and vehicle breakdowns—are constructed to assess the system resilience performance. Experimental results demonstrate that RL significantly outperforms GA in static baseline scenarios, with stable and consistent solution quality across independent runs. Under dynamic disruptions, RL exhibits superior adaptive capacity with faster recovery speed and steady solution performance, whereas GA presents pronounced solution variability and noticeable performance degradation. These findings validate the effectiveness of reinforcement learning as a robust and scalable solution for realizing green and resilient logistics in dynamic and uncertain environments.
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References
[1] International Energy Agency (IEA). Transport Sector CO₂ Emissions. Paris: IEA, 2023.
[2] Fahimnia, B., Sarkis, J., & Davarzani, H. (2015). Green supply chain management: A review and bibliometric analysis. International Journal of Production Economics, 162, 101–114.
[3] Ivanov, D., & Dolgui, A. (2020). Viability of intertwined supply networks: extending the supply chain resilience angles towards survivability. International Journal of Production Research, 58(10), 2904–2915.
[4] Tang, C. S. (2006). Robust strategies for mitigating supply chain disruptions. International Journal of Logistics Research and Applications, 9(1), 33–45.
[5] Benjaafar, S., Li, Y., & Daskin, M. (2013). Carbon footprint and the management of supply chains. Management Science, 59(1), 22–38.
[6] Waller, M. A., & Fawcett, S. E. (2013). Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management. Journal of Business Logistics, 34(2), 77–84.
[7] Ivanov, D. (2021). Supply chain viability and the COVID-19 pandemic: a conceptual and formal generalisation of four major adaptation strategies. Annals of Operations Research, 306, 421–442.
[8] Tao, F., Qi, Q., Liu, A., & Kusiak, A. (2018). Data-driven smart manufacturing. Journal of Manufacturing Systems, 48, 157–169.
[9] Toth, P., & Vigo, D. (2014). Vehicle Routing: Problems, Methods, and Applications. SIAM.
[10] Holland, J. H. (1992). Adaptation in Natural and Artificial Systems. MIT Press.
[11] Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. Proceedings of IEEE International Conference on Neural Networks, 1942–1948.
[12] Dorigo, M., & Stützle, T. (2004). Ant Colony Optimization. MIT Press.
[13] Talbi, E. G. (2009). Metaheuristics: From Design to Implementation. Wiley.
[14] Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.
[15] Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.
[16] Friedman, J. H. (2001). Greedy function approximation: a gradient boosting machine. Annals of Statistics, 29(5), 1189–1232.
[17] LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.
[18] Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.
[19] Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press.
[20] Nazari, M., Oroojlooy, A., Snyder, L., & Takáč, M. (2018). Reinforcement learning for solving the vehicle routing problem. Advances in Neural Information Processing Systems (NeurIPS).
[21] F. Tao, M. Zhang, and A. Y. C. Nee, Digital Twin Driven Smart Manufacturing. Academic Press, 2019.
[22] Atzori, L., Iera, A., & Morabito, G. (2010). The Internet of Things: A survey. Computer Networks, 54(15), 2787–2805.
[23] Ivanov, D., Dolgui, A., & Sokolov, B. (2019). The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics. International Journal of Production Research, 57(3), 829–846.
[24] Brown, T. B., et al. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems (NeurIPS).
[25] Bommasani, R., et al. (2021). On the opportunities and risks of foundation models. Stanford Center for Research on Foundation Models Report.
[26] Kool, W., van Hoof, H., & Welling, M. (2019). Attention, learn to solve routing problems. In Proceedings of the International Conference on Learning Representations (ICLR).
[27] Nazari, M., Oroojlooy, A., Snyder, L., & Takáč, M. (2018). Reinforcement learning for solving the vehicle routing problem. In Advances in Neural Information Processing Systems (NeurIPS), 31, 9860–9870.
[28] Ivanov, D., & Dolgui, A. (2020). A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0. Production Planning & Control, 31(14), 1189–1204.
[29] Tang, C. S. (2006). Perspectives in supply chain risk management. International Journal of Production Economics, 103(2), 451–488.
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