Anderson, J., & Patel, R. (2020). Challenges and opportunities in AI-driven logistics optimization. Journal of Supply Chain Management, 56(4), 223-239.
Brown, T., Li, X., & Zhao, H. (2018). Machine learning applications in predictive maintenance for logistics operations. International Journal of Logistics Research and Applications, 21(3), 157-176.
Chen, W., Zhang, L., & Xu, D. (2023). Multi-objective genetic algorithms for optimizing reverse logistics in closed-loop supply chains. Journal of Sustainable Transportation, 45(2), 289-310.
Chen, Y., Wang, Z., & Lee, K. (2020). AI-powered route optimization for last-mile delivery: A reinforcement learning approach. Transportation Research Part E: Logistics and Transportation Review, 142, 101923.
Choi, S., & Park, J. (2021). Blockchain and AI integration for transparency in supply chain management. Computers & Industrial Engineering, 154, 107134.
Gao, Y., & Zhang, F. (2019). The role of closed-loop supply chains in achieving sustainable logistics. Resources, Conservation and Recycling, 150, 104422.
Garcia, P., Sato, Y., & Kim, J. (2023). Predictive maintenance using IoT and AI: A framework for last-mile fleet reliability. Engineering Applications of Artificial Intelligence, 116, 105243.
Huang, L., & Lee, R. (2022). Hybrid AI models for decision-making in last-mile logistics. Expert Systems with Applications, 198, 116723.
Johnson, M., Nguyen, T., & Roberts, D. (2021). Federated learning for supply chain decision-making: Addressing data privacy concerns. Decision Support Systems, 149, 113732.
Kim, B., Wang, P., & Li, S. (2023). Autonomous AI in sustainable logistics: A case study on IoT-enabled last-mile delivery. Journal of Cleaner Production, 312, 127639.
Lee, S., & Kim, Y. (2020). Deep learning for predictive maintenance in logistics: Applications and challenges. Computers in Industry, 123, 103317.
Li, J., Torres, A., & Patel, S. (2022). IoT-enhanced supply chain visibility: Implications for AI-driven decision-making. Supply Chain Management Review, 58(1), 45-63.
Martinez, C., Zhou, L., & Gupta, R. (2019). AI-powered dynamic pricing models for resale of refurbished goods in CLSCs. International Journal of Production Economics, 208, 93-107.
Nguyen, H., Brown, P., & Garcia, Y. (2021). IoT and AI integration for predictive maintenance in autonomous logistics systems. Procedia CIRP, 99, 87-94.
Nozari, H., & Szmelter-Jarosz, A. (2022). IoT-based supply chain for smart business. ISNet.
Nozari, H., Tavakkoli-Moghaddam, R., & Dolgui, A. (2024, September). Analysis of Critical Success Factors of Sustainable and Resilient Aioe-based Supply Chain in Industry 5.0. In IFIP International Conference on Advances in Production Management Systems (pp. 76-90). Cham: Springer Nature Switzerland.
Nozari, H., Tavakkoli-Moghaddam, R., & Gharemani-Nahr, J. (2022). A neutrosophic fuzzy programming method to solve a multi-depot vehicle routing model under uncertainty during the covid-19 pandemic. International Journal of Engineering, 35(2), 360-371.
Patel, R., & Singh, V. (2020). Demand forecasting in last-mile logistics: A machine learning approach. Transportation Research Part A: Policy and Practice, 136, 132-149.
Rahman, M., Torres, L., & Zhang, F. (2021). Optimizing closed-loop logistics with AI and IoT: A case study on reusable packaging. Journal of Industrial Ecology, 25(4), 721-738.
Rodriguez, E., Anderson, J., & Patel, R. (2020). Cybersecurity challenges in IoT-enabled supply chains. Computers & Security, 93, 101820.
Sato, H., Garcia, M., & Lee, J. (2022). Real-time fleet monitoring using IoT: Enhancing last-mile delivery performance. IEEE Internet of Things Journal, 9(5), 4202-4216.
Sharma, P., & Gupta, K. (2020). AI-driven classification models for optimizing product returns in reverse logistics. Journal of Manufacturing Systems, 54, 230-245.
Singh, R., Choi, K., & Park, M. (2023). Integrating AI and blockchain for secure and transparent supply chain management. Journal of Business Logistics, 44(2), 378-400.
Torres, L., Zhou, C., & Zhang, W. (2022). RFID-enabled smart tracking in closed-loop supply chains: An AI-based approach. International Journal of Logistics Research and Applications, 25(1), 56-73.
Wang, J., Sato, Y., & Kim, B. (2023). AI-driven digital twin applications in last-mile logistics. Journal of Artificial Intelligence Research, 72, 112-127.
Wang, T., Patel, R., & Lee, D. (2019). The impact of AI on last-mile delivery performance: A case study in urban logistics. Journal of Transport Geography, 74, 284-298.
Zhang, H., Torres, M., & Anderson, P. (2022). Federated learning for AI-driven supply chains: A privacy-preserving approach. Decision Sciences, 53(3), 480-499.
Zhang, L., & Xu, H. (2021). Reinforcement learning for real-time route optimization in last-mile delivery. Expert Systems with Applications, 183, 115367.
Zhou, P., Martinez, J., & Gupta, R. (2020). AI-based segmentation of returned products for efficient reverse logistics processing. Computers & Industrial Engineering, 147, 106687.
