International Journal For Multidisciplinary Research

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AI-Driven Optimization of Last-Mile Delivery

Author(s) Arvindan Badrinarayanan
Country United States
Abstract The optimization of last-mile delivery represents a critical challenge in modern e-commerce logistics,
consuming a substantial portion of total shipping costs. This comprehensive technical article examines
how artificial intelligence and machine learning technologies are revolutionizing last-mile delivery
operations through advanced route optimization, demand forecasting, and resource allocation. This article
synthesizes findings from recent implementations across major logistics providers, demonstrating that
AI-driven route optimization systems significantly reduce delivery times and decrease fuel consumption
across diverse operational environments. Analysis of machine learning models deployed by leading
e-commerce platforms shows marked improvements in delivery time prediction accuracy, achieving
unprecedented precision in estimated delivery windows. This article examines key technical components,
including dynamic routing algorithms, predictive demand modeling, and real-time fleet management
systems, while also addressing implementation challenges such as data quality and system integration.
Case studies from major logistics providers demonstrate substantial ROI improvements following AI
implementation, with particular emphasis on neural network architectures optimized for urban delivery
scenarios. This article provides a technical framework for understanding how AI technologies transform
last-mile logistics while offering insights into future developments, including autonomous delivery
systems and smart city integration.
Keywords Last-Mile Logistics, Artificial Intelligence Optimization, Machine Learning Route Planning, Predictive Delivery Analytics, Supply Chain Automation.
Field Computer
Published In Volume 6, Issue 6, November-December 2024
Published On 2024-12-05
Cite This AI-Driven Optimization of Last-Mile Delivery - Arvindan Badrinarayanan - IJFMR Volume 6, Issue 6, November-December 2024. DOI 10.36948/ijfmr.2024.v06i06.32057
DOI https://doi.org/10.36948/ijfmr.2024.v06i06.32057
Short DOI https://doi.org/g8t3kn
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