Dynamic AI Systems for Real-Time Fleet Reallocation: Minimizing Emissions and Operational Costs in Logistics
Year: 2025
Type: article
Abstract: The logistics and transportation industries are critical enablers of global commerce, but they also represent significant contributors to greenhouse gas emissions and operational inefficiencies. In response to these challenges, dynamic artificial intelligence (AI) systems have emerged as transformative tools for optimizing fleet allocation and minimizing environmental and financial impacts. This paper reviews literature on dynamic AI systems for real-time fleet reallocation in the logistics sector, focusing on their role in lowering emissions and operational costs. Findings indicate how dynamic reallocation improves delivery performance and directly supports broader corporate sustainability initiatives and compliance with evolving environmental regulations. In transportation sectors, including parcel delivery networks and freight logistics, quantifiable reductions in carbon footprint and cost savings can be achieved through AI deployment. However, technological barriers, implementation challenges, and ethical considerations exist in deploying autonomous decision-making systems for fleet management. Therefore, dynamic AI systems are essential enablers for future-ready, sustainable logistics operations in an increasingly carbon-conscious global economy.
Author: Abdulazeez Baruwa
Institution: Indiana University
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4411110916