A Predictive AI Modeling Framework for Sustainable Logistics and Emissions
DOI:
https://doi.org/10.54536/ajfti.v3i1.6252Keywords:
Artificial Intelligence, Carbon Emissions, Fuel Optimization, Predictive Analytics, Route Planning, Supply Chain SustainabilityAbstract
The logistics sector faces immense pressure to decarbonize while maintaining efficiency. This research develops an integrated AI framework using machine learning and reinforcement learning to simultaneously optimize routing, fuel consumption, and emissions in supply chains. A simulation-based analysis demonstrates its significant potential, showing reductions of 15-20% in fuel use and greenhouse gas emissions, alongside a 12-15% decrease in total distance traveled. These operational improvements directly translate into strategic advantages, enhancing cost-effectiveness and supply chain resilience. For logistics managers, this framework provides a actionable tool for achieving sustainability targets without compromising service levels. Furthermore, the findings provide a tangible pathway for aligning corporate logistics with national and global decarbonization policies. The study concludes that the adoption of such AI-driven frameworks is not merely an operational upgrade but a critical step toward building sustainable, competitive, and environmentally responsible supply chains.
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Copyright (c) 2025 Abdullah Sheikh, Tajbiha Mehonaj Rinve2, Md. Shakil Sheikh
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