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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)
Elvas, L. B., Tokkozhina, U., Martins, A. & Ferreira, J. (2023). Implementation of disruptive technologies for the last mile delivery efficiency achievement. In Luís de Picado Santos, Jorge Pinho de Sousa, Elisabete Arsenio (Ed.), 2022 Conference Proceedings Transport Research Arena, TRA Lisbon 2022. (pp. 32-39).: Elsevier.
Exportar Referência (IEEE)
L. M. Elvas et al.,  "Implementation of disruptive technologies for the last mile delivery efficiency achievement", in 2022 Conf. Proc. Transport Research Arena, TRA Lisbon 2022, Luís de Picado Santos, Jorge Pinho de Sousa, Elisabete Arsenio, Ed., Elsevier, 2023, vol. 72, pp. 32-39
Exportar BibTeX
@inproceedings{elvas2023_1734882048539,
	author = "Elvas, L. B. and Tokkozhina, U. and Martins, A. and Ferreira, J.",
	title = "Implementation of disruptive technologies for the last mile delivery efficiency achievement",
	booktitle = "2022 Conference Proceedings Transport Research Arena, TRA Lisbon 2022",
	year = "2023",
	editor = "Luís de Picado Santos, Jorge Pinho de Sousa, Elisabete Arsenio",
	volume = "72",
	number = "",
	series = "",
	doi = "10.1016/j.trpro.2023.11.319",
	pages = "32-39",
	publisher = "Elsevier",
	address = "",
	organization = "",
	url = "https://www.sciencedirect.com/science/article/pii/S2352146523006166?via%3Dihub"
}
Exportar RIS
TY  - CPAPER
TI  - Implementation of disruptive technologies for the last mile delivery efficiency achievement
T2  - 2022 Conference Proceedings Transport Research Arena, TRA Lisbon 2022
VL  - 72
AU  - Elvas, L. B.
AU  - Tokkozhina, U.
AU  - Martins, A.
AU  - Ferreira, J.
PY  - 2023
SP  - 32-39
SN  - 2352-1457
DO  - 10.1016/j.trpro.2023.11.319
UR  - https://www.sciencedirect.com/science/article/pii/S2352146523006166?via%3Dihub
AB  - he last mile delivery (LMD) is one of the most tangled procedures in logistics. The reason is that it involves various uncertainties, including weather and road conditions, traffic hours and route selection, car accidents, delivery vehicle anomalies, at the same time needing to avoid parcel damages and delivery errors, while communicating with the recipient of the parcel. Above-mentioned factors cause the difficulties of successful parcels delivery to customers' doorsteps. Therefore, businesses need to search for technology solutions that will enable increase of last mile delivery efficiency. All intelligent solutions are built upon big data, as huge volumes of data allow the prediction of future behavior based on historical knowledge. In this study we propose a last-mile solution where the combination of disruptive technologies allow a better distribution without costs increasing.


ER  -