Exportar Publicação

A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
Ferreira, J. C. & Esperança, M. (2025). Enhancing sustainable last-mile delivery: The impact of electric vehicles and AI optimization on urban logistics. World Electric Vehicle Journal. 16 (5)
Exportar Referência (IEEE) 
J. C. Ferreira and M. D. Esperança,  "Enhancing sustainable last-mile delivery: The impact of electric vehicles and AI optimization on urban logistics", in World Electric Vehicle Journal, vol. 16, no. 5, 2025
Exportar BibTeX 
@article{ferreira2025_1765958498850,
	author = "Ferreira, J. C. and Esperança, M.",
	title = "Enhancing sustainable last-mile delivery: The impact of electric vehicles and AI optimization on urban logistics",
	journal = "World Electric Vehicle Journal",
	year = "2025",
	volume = "16",
	number = "5",
	doi = "10.3390/wevj16050242",
	url = "https://www.mdpi.com/journal/wevj"
}
Exportar RIS 
TY  - JOUR
TI  - Enhancing sustainable last-mile delivery: The impact of electric vehicles and AI optimization on urban logistics
T2  - World Electric Vehicle Journal
VL  - 16
IS  - 5
AU  - Ferreira, J. C.
AU  - Esperança, M.
PY  - 2025
SN  - 2032-6653
DO  - 10.3390/wevj16050242
UR  - https://www.mdpi.com/journal/wevj
AB  - The rapid growth of e-commerce has intensified the need for efficient and sustainable last-mile delivery solutions in urban environments. This paper explores the integration of electric vehicles (EVs) and artificial intelligence (AI) into a combined framework to enhance the environmental, operational, and economic performance of urban logistics. Through a comprehensive literature review, we examine current trends, technological developments, and implementation challenges at the intersection of smart mobility, green logistics, and digital transformation. We propose an operational framework that leverages AI for route optimization, fleet coordination, and energy management in EV-based delivery networks. This framework is validated through a real-world case study conducted in Lisbon, Portugal, where a logistics provider implemented a city consolidation center model supported by AI-driven optimization tools. Using key performance indicators—including delivery time, energy consumption, fleet utilization, customer satisfaction, and CO₂ emissions—we measure the pre- and post-AI deployment impacts. The results demonstrate significant improvements across all metrics, including a 15–20% reduction in delivery time, a 10–25% gain in energy efficiency, and up to a 40% decrease in emissions. The findings confirm that the synergy between EVs and AI provides a robust and scalable model for achieving sustainable last-mile logistics, supporting broader urban mobility and climate objectives.
ER  -