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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM64398
Volume:
10 |
5 |
May 2026
Pages:
1-4
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
MASTERS OF COMPUTER APPLICATIONS
BAPUJI INSTITUTE OF ENGINEERING AND TECHNOLOGY DAVANAGERE
MASTERS OF COMPUTER APPLICATIONS
BAPUJI INSTITUTE OF ENGINEERING AND TECHNOLOGY DAVANAGERE
The explosive growth of e-commerce and the increasing complexity of transportation solutions. Outmoded often manual and reliant on static data are no longer sufficient to meet the dynamic needs of modern logistics. This project "Logistics & Route Optimization Analytics Using Large Datasets" addresses these challenges by proposing a comprehensive system that leverages the power of real-time data processing to revolutionize route optimization. The core of this project is the development of a sophisticated algorithmic engine capable of processing vast and varied datasets including historical delivery records real-time traffic information weather forecasts vehicle telematics and customer-specific constraints. By analyzing these large datasets travel time. The proposed system moves beyond simple shortest-path calculations to consider a multitude of real-world variables Through the application of advanced machine learning models the system will progressively efficient route suggestions. This adaptive learning capability will enable logistics companies to respond proactively to unforeseen events such as accidents or sudden changes in weather by rerouting vehicles in real-time. The project also emphasizes the importance of a user-centric design providing an intuitive interface for logistics managers to monitor fleet movements analyze performance metrics and make informed data-driven decisions.
KEYWORDS
The "Logistics & Route Optimization Analytics Using Large Datasets" project represents a significant leap forward in addressing the complex and dynamic challenges of modern logistics. The successful establishment and implementation of this system are a powerful data-driven solution that goes beyond the classical static route planning solution. It has the massive potential of big data machine learning and analytics and the project will enable the logistics firm to have an unprecedented level of efficiency cost-effectiveness and satisfaction. [7] The experience of working on this project from the conceptual stages to the design process has reinforced the importance of a more intelligent approach when it comes to the management of logistics. This proposed system with a comprehensive array of features addresses the gap with the provision of a holistic and comprehensive platform that extends throughout the life cycle of the delivery process. From the synergy of data integration to the sophisticated user interface design tools every aspect of it serves the dual purpose of maximizing value for its users.
1. Li J. & Wang Y. (2022). Big Data Analytics in Logistics and Supply Chain Management. IEEE Access 10 11245–11258. 2. Chen C. & Lee D. (2021). Machine Learning-Based Route Optimization for Smart Transportation Systems. Transportation Research Part C 130 103305. 3. Kumar S. & Raj P. (2020). Optimization of Vehicle .Routing Problem Using Genetic Algorithms. IJACSA 11(9) 450–456. 4. Ahmed N. & Kim D. (2023). IoT and Big Data Integration for Intelligent Fleet Management Systems. Journal of Big Data 10(1) 85–96. 5. Zhang L. & Zhao X. (2021). Predictive Analytics for Transportation Logistics Using AI Techniques. Procedia Computer Science 192 334–341. 6. Google Maps Platform. (2024). Route Optimization and Distance Matrix API Documentation. 7. Apache Software Foundation. (2024). Apache Spark Documentation.
Publication Date
6/1/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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