DOI: https://doi.org/10.63345/ijrmeet.org.v13.i6.1
Roshan Atulkumar Tathed
Harvard Business School
Boston, USA
orcid id: 0009-0003-8913-918X
Abstract
The logistics and supply chain industry faces increasing complexity due to rising demand for faster deliveries, fluctuating traffic conditions, and environmental sustainability goals. This study explores the integration of Artificial Intelligence (AI) frameworks, particularly deep reinforcement learning (DRL), for intelligent routing and operational optimization in logistics. AI offers a data-driven approach to enhance efficiency by optimizing route planning, resource allocation, and real-time decision-making across various supply chain tiers. The study aims to design scalable, robust, and explainable AI solutions that can adapt to uncertain conditions, manage multi-objective optimization (e.g., delivery time, cost, and carbon emissions), and ensure privacy in collaborative environments. Through simulation and real-world pilot implementations, the research evaluates the performance of AI-driven frameworks in improving delivery times, fuel consumption, and operational resilience. The results highlight the potential of AI in transforming logistics systems by offering smarter, more sustainable, and responsive operations. However, challenges such as model interpretability, data privacy, and real-time scalability remain, and the study provides insights into how these hurdles can be addressed. This research paves the way for AI-powered logistics systems capable of optimizing operational efficiency, reducing environmental impacts, and enhancing customer satisfaction in a highly dynamic environment.
Keywords
AI in logistics, deep reinforcement learning, intelligent routing, operational optimization, supply chain management, sustainability, real-time decision-making, scalability, privacy, multi-echelon supply chains.
References
- Chen, W., Men, Y., Fuster, N., Osorio, C., & Juan, A. A. (2024). Artificial intelligence in logistics optimization with sustainable criteria: A review. Sustainability, 16(21), 9145. https://doi.org/10.3390/su16219145 com
- Ieva, S., Bilenchi, I., Gramegna, F., Pinto, A., Scioscia, F., Ruta, M., & Loseto, G. (2025). Enhancing last-mile logistics: AI-driven fleet optimization, mixed reality, and large-language-model assistants for warehouse operations. Sensors, 25(9), 2696. https://doi.org/10.3390/s25092696 com
- Mohsen, B. M. (2024). AI-driven optimization of urban logistics in smart cities: Integrating autonomous vehicles and IoT for efficient delivery systems. Sustainability, 16(24), 11265. https://doi.org/10.3390/su162411265 com
- Ferreira, J. C., & Esperança, M. (2025). Enhancing sustainable last-mile delivery: The impact of electric vehicles and AI optimization on urban logistics. World Electric Vehicle Journal, 16(5), 242. https://doi.org/10.3390/wevj16050242 com
- Mozhdehi, A., Mohammadizadeh, M., & Wang, X. (2024). Edge-DIRECT: A deep‐reinforcement-learning method for solving heterogeneous electric vehicle routing problems with time-window constraints. arXiv Preprint. https://doi.org/10.48550/arXiv.2407.01615 org
- Konovalenko, A., & Hvattum, L. M. (2024). Optimizing a dynamic vehicle routing problem with deep reinforcement learning: Analyzing state-space components. Logistics, 8(4), 96. https://doi.org/10.3390/logistics8040096 com
- De Assis, R. F., Faria, A. F., Thomasset-Laperrière, V., Santa-Eulalia, L. A., Ouhimmou, M., & Ferreira, W. P. (2024). Machine learning in warehouse management: A survey. Procedia Computer Science, 232, 2790–2799. https://doi.org/10.1016/j.procs.2024.02.096 net
- Kosasih, E. E., Papadakis, E., Baryannis, G., & Brintrup, A. (2024). A review of explainable artificial intelligence in supply chain management using neurosymbolic approaches. International Journal of Production Research, 62(4), 1510–1540. https://doi.org/10.1080/00207543.2023.2281663 net
- Aylak, B. L. (2025). SustAI-SCM: Intelligent supply-chain process automation with agentic AI for sustainability and cost efficiency. Sustainability, 17(6), 2453. https://doi.org/10.3390/su17062453 com
- Roman, E.-A., Stere, A.-S., Roșca, E., Radu, A.-V., Codroiu, D., & Anamaria, I. (2025). State of the art of digital twins in improving supply chain resilience. Logistics, 9(1), 22. https://doi.org/10.3390/logistics9010022 com
