MACHINE LEARNING-DRIVEN LAST-MILE DELIVERY TIME PREDICTION USING MGRS BLOCK ANALYSIS

Abstract

This article explores the implementation of machine learning-driven last-mile delivery time prediction systems utilizing Military Grid Reference System (MGRS) block analysis. The article presents a comprehensive framework integrating AI models with precise geospatial referencing to enhance delivery prediction accuracy. By leveraging deep learning architectures, including recurrent neural networks and gradient boosting algorithms, combined with sophisticated time-series analysis, the system demonstrates significant improvements in prediction capabilities. The article addresses critical challenges in data sparsity, computational optimization, and system scalability through innovative approaches to data management and performance optimization. A multi-layered security framework ensures regulatory compliance while maintaining system flexibility. The implementation incorporates edge computing capabilities and real-time processing systems, enabling efficient handling of continuous data flows. Articles reveal substantial improvements in delivery accuracy, operational efficiency, and customer satisfaction across various deployment scales. The article contributes to the evolving landscape of logistics optimization by providing a scalable, secure, and efficient framework for precise delivery time predictions.