DESIGN AND SIMULATION PLANNING OF AN AI AND WEB-GIS INTEGRATED SYSTEM FOR REAL-TIME LOGISTICS ROUTE OPTIMIZATION

Mualliflar

  • Kucharova Shaxlo Sobir qizi Doctorate (PhD) student Institute for advanced training of personnel and statistical research, Uzbekistan Muallif

;

https://doi.org/10.5281/zenodo.20726363

;

logistics, digital transformation, real-time monitoring, transportation, IoT, GPS, supply chain management, logistics analytics.

Abstrak

The rapid development of digital technologies has significantly transformed logistics and transportation systems worldwide. Real-time monitoring technologies enable organizations to track, analyze, and optimize logistics operations continuously. In Uzbekistan, the modernization of transport and logistics infrastructure requires the implementation of advanced monitoring systems capable of providing accurate and timely information for decision-making. This study proposes a conceptual framework for a real-time monitoring system for transport and logistics operations. The framework integrates GPS technologies, Internet of Things (IoT) sensors, cloud computing, and data analytics tools to improve operational efficiency, reduce transportation costs, and enhance supply chain visibility. The proposed model offers a comprehensive approach for monitoring transportation processes and supporting strategic logistics management in the context of digital transformation.

Iqtiboslar

Badrinarayanan, A. (2024). AI-driven optimization of last-mile delivery. International Journal for Multidisciplinary Research. https://doi.org/10.36948/ijfmr.2024.v06i06.32057

Banu, D. (2025). A scalable AI-driven framework for sustainable ride-sharing and intelligent logistics using advanced route optimization. International Journal of Scientific Research in Engineering and Management. https://doi.org/10.55041/ijsrem46131

Chen, W., Men, Y., Fuster, N., Osorio, C., & Juan, A. (2024). Artificial intelligence in logistics optimization with sustainable criteria: A review. Sustainability. https://doi.org/10.3390/su16219145

Danchuk, V., & Hutarevych, O. (2024). Adaptable dynamic routing system in urban transport logistics problems using GIS data. Scientific Journal of Silesian University of Technology. Series Transport. https://doi.org/10.20858/sjsutst.2024.125.2

Dikshit, S., Atiq, A., Shahid, M., Dwivedi, V., & Thusu, A. (2023). The use of artificial intelligence to optimize the routing of vehicles and reduce traffic congestion in urban areas. EAI Endorsed Transactions on Energy Web, 10. https://doi.org/10.4108/ew.4613

Egbuhuzor, N. S., Ajayi, A., Akhigbe, E. E., Ewim, C. P.-M., Ajiga, D. I., & Agbede, O. O. (2023). Artificial intelligence in predictive flow management: Transforming logistics and supply chain operations. International Journal of Management and Organizational Research. https://doi.org/10.54660/ijmor.2023.2.1.48-63

Eze, C. C. (2025). Integration of AI-driven multimodal transport systems for optimizing real-time urban and intercity mobility solutions. International Journal of Research Publication and Reviews. https://doi.org/10.55248/gengpi.6.0425.1574

Fatorachian, H., Kazemi, H., & Pawar, K. (2025). Enhancing smart city logistics through IoT-enabled predictive analytics: A digital twin and cybernetic feedback approach. Smart Cities. https://doi.org/10.3390/smartcities8020056

Hussain, K. M. (2025). Revolutionizing route optimization systems with artificial intelligence for a smarter, sustainable logistics ecosystem. International Journal of Computer Science and Mobile Computing. https://doi.org/10.47760/ijcsmc.2025.v14i02.008

Mandal, J., & Mohammed, I. (2024). Implementation of AI transportation routing in reverse logistics to reduce CO₂ footprint. International Journal of Supply Chain Management. https://doi.org/10.47604/ijscm.3079

Mohsen, B. (2024). AI-driven optimization of urban logistics in smart cities: Integrating autonomous vehicles and IoT for efficient delivery systems. Sustainability. https://doi.org/10.3390/su162411265

Munawar, S. (2023). Bridging gaps in integrated transportation systems for sustainable logistics. Sinergi International Journal of Logistics. https://doi.org/10.61194/sijl.v1i2.618

Ojadi, J. O., Odionu, C. S., Onukwulu, E. C., & Owulade, O. A. (2024). Big data analytics and AI for optimizing supply chain sustainability and reducing greenhouse gas emissions in logistics and transportation. International Journal of Multidisciplinary Research and Growth Evaluation. https://doi.org/10.54660/ijmrge.2024.5.1.1536-1548

Pathuri, N. (2024). The convergence of AI and human expertise in modern logistics operations. International Journal for Multidisciplinary Research. https://doi.org/10.36948/ijfmr.2024.v06i06.33707

Paul, P. O., Aderoju, A. V., Shitu, K., Ononiwu, M. I., Igwe, A. N., Ofodile, O. C., Paul-Mikki, C., & Ewim, P.-M. (2024). Predictive analytics and AI in sustainable logistics: A review of applications and impact on SMEs. Magna Scientia Advanced Research and Reviews. https://doi.org/10.30574/msarr.2024.12.1.0176

Prof, R. M., & Pradhan, T. (2025). Smart logistics: The AI revolution in supply chain optimization and its challenges. International Journal of Advanced Research in Science, Communication and Technology. https://doi.org/10.48175/ijarsct-24915

Rismanto, H., & Judijanto, L. (2025). Dynamic routing in urban logistics: A comprehensive review of AI, real-time data, and sustainability impacts. Sinergi International Journal of Logistics. https://doi.org/10.61194/sijl.v3i2.741

Sihotang, H. T., Sihotang, J., Simbolon, A. P. H., Panjaitan, F., & Simbolon, R. S. (2024). Advancing decision-making: AI-driven optimization models for complex systems. International Journal of Basic and Applied Science. https://doi.org/10.35335/ijobas.v13i3.581

Vemuri, N., Tatikonda, V. M., & Thaneeru, N. (2024). Enhancing public transit system through AI and IoT. International Journal of Scientific Research and Management (IJSRM). https://doi.org/10.18535/ijsrm/v12i02.ec07

Vinnakota, S. (2022). AI-Driven Route Optimization for Logistics. International Scientific Journal of Engineering and Management. https://doi.org/10.55041/isjem00112

Yerra, S. (2024). The Role of Cloud-Based Analytics in Transforming Logistics Data Management and Reporting. The Eastasouth Journal of Information System and Computer Science. https://doi.org/10.58812/esiscs.v2i02.515

Zhang, D. (2024). AI integration in supply chain and operations management: Enhancing efficiency and resilience. Applied and Computational Engineering. https://doi.org/10.54254/2755-2721/90/2024melb0060

##submission.downloads##

Nashr qilingan

2026-06-17

Nashr

Bo'lim

Maqolalar

Iqtibos keltirish tartibi

Kucharova, S. (2026). DESIGN AND SIMULATION PLANNING OF AN AI AND WEB-GIS INTEGRATED SYSTEM FOR REAL-TIME LOGISTICS ROUTE OPTIMIZATION. Yosh Olimlar, 4(58), 120-127. https://doi.org/10.5281/zenodo.20726363
Innovative Academy RSC
Article metrics Views and PDF downloads
2 Views
0 Downloads