Designing and Deploying AI Models for Sustainable Logistics Optimization: A Case Study on Eco-Efficient Supply Chains in the USA

• URL: http://arxiv.org/abs/2503.14556v1
• Date: Tue, 18 Mar 2025 00:46:35 GMT
• Title: Designing and Deploying AI Models for Sustainable Logistics Optimization: A Case Study on Eco-Efficient Supply Chains in the USA
• Authors: Reza E Rabbi Shawon, MD Rokibul Hasan, Md Anisur Rahman, Mohamed Ghandri, Iman Ahmed Lamari, Mohammed Kawsar, Rubi Akter,
• Abstract summary: This study explores AI-based methodologies for optimizing logistics operations in the USA.<n>Key AI applications include predictive analytics for demand forecasting, route optimization through machine learning, and AI-powered fuel efficiency strategies.<n>Various models, such as Linear Regression, XGBoost, Support Vector Machine, and Neural Networks, are applied to real-world logistics datasets.
• Score: 0.13551232282678033
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid evolution of Artificial Intelligence (AI) and Machine Learning (ML) has significantly transformed logistics and supply chain management, particularly in the pursuit of sustainability and eco-efficiency. This study explores AI-based methodologies for optimizing logistics operations in the USA, focusing on reducing environmental impact, improving fuel efficiency, and minimizing costs. Key AI applications include predictive analytics for demand forecasting, route optimization through machine learning, and AI-powered fuel efficiency strategies. Various models, such as Linear Regression, XGBoost, Support Vector Machine, and Neural Networks, are applied to real-world logistics datasets to reduce carbon emissions based on logistics operations, optimize travel routes to minimize distance and travel time, and predict future deliveries to plan optimal routes. Other models such as K-Means and DBSCAN are also used to optimize travel routes to minimize distance and travel time for logistics operations. This study utilizes datasets from logistics companies' databases. The study also assesses model performance using metrics such as mean absolute error (MAE), mean squared error (MSE), and R2 score. This study also explores how these models can be deployed to various platforms for real-time logistics and supply chain use. The models are also examined through a thorough case study, highlighting best practices and regulatory frameworks that promote sustainability. The findings demonstrate AI's potential to enhance logistics efficiency, reduce carbon footprints, and contribute to a more resilient and adaptive supply chain ecosystem.

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