Standard

Modelling of traffic flows and supply chains based on geospatial knowledge. / Kolesnikov, A.; Kikin, P.; Panidi, E.

в: Journal of Physics: Conference Series, Том 2068, № 1, 012042, 01.10.2021.

Результаты исследований: Научные публикации в периодических изданиях › статья в журнале по материалам конференции › Рецензирование

Harvard

Kolesnikov, A, Kikin, P & Panidi, E 2021, 'Modelling of traffic flows and supply chains based on geospatial knowledge', Journal of Physics: Conference Series, Том. 2068, № 1, 012042. https://doi.org/10.1088/1742-6596/2068/1/012042

APA

Kolesnikov, A., Kikin, P., & Panidi, E. (2021). Modelling of traffic flows and supply chains based on geospatial knowledge. Journal of Physics: Conference Series, 2068(1), [012042]. https://doi.org/10.1088/1742-6596/2068/1/012042

Vancouver

Kolesnikov A, Kikin P, Panidi E. Modelling of traffic flows and supply chains based on geospatial knowledge. Journal of Physics: Conference Series. 2021 Окт. 1;2068(1). 012042. https://doi.org/10.1088/1742-6596/2068/1/012042

Author

Kolesnikov, A. ; Kikin, P. ; Panidi, E. / Modelling of traffic flows and supply chains based on geospatial knowledge. в: Journal of Physics: Conference Series. 2021 ; Том 2068, № 1.

BibTeX

@article{0e5abb09cbf74e1287657ceacce36fb3,
title = "Modelling of traffic flows and supply chains based on geospatial knowledge",
abstract = "The field of logistics and transport operates with large amounts of data. The transformation of such arrays into knowledge and processing using machine learning methods will help to find additional reserves for optimizing transport and logistics processes and supply chains. This article analyses the possibilities and prospects for the application of machine learning and geospatial knowledge in the field of logistics and transport using specific examples. The long-term impact of geospatial-based artificial intelligence systems on such processes as procurement, delivery, inventory management, maintenance, customer interaction is considered.",
keywords = "Geospatial knowledge, Logistics, Machine learning, Spatial modelling",
author = "A. Kolesnikov and P. Kikin and E. Panidi",
note = "Publisher Copyright: {\textcopyright} Journal of Physics: Conference Series 2021.; 2021 4th International Conference on Applied Mathematics, Modeling and Simulation, AMMS 2021 ; Conference date: 17-09-2021 Through 18-09-2021",
year = "2021",
month = oct,
day = "1",
doi = "10.1088/1742-6596/2068/1/012042",
language = "English",
volume = "2068",
journal = "Journal of Physics: Conference Series",
issn = "1742-6588",
publisher = "IOP Publishing Ltd.",
number = "1",

}

RIS

TY - JOUR

T1 - Modelling of traffic flows and supply chains based on geospatial knowledge

AU - Kolesnikov, A.

AU - Kikin, P.

AU - Panidi, E.

N1 - Publisher Copyright: © Journal of Physics: Conference Series 2021.

PY - 2021/10/1

Y1 - 2021/10/1

N2 - The field of logistics and transport operates with large amounts of data. The transformation of such arrays into knowledge and processing using machine learning methods will help to find additional reserves for optimizing transport and logistics processes and supply chains. This article analyses the possibilities and prospects for the application of machine learning and geospatial knowledge in the field of logistics and transport using specific examples. The long-term impact of geospatial-based artificial intelligence systems on such processes as procurement, delivery, inventory management, maintenance, customer interaction is considered.

AB - The field of logistics and transport operates with large amounts of data. The transformation of such arrays into knowledge and processing using machine learning methods will help to find additional reserves for optimizing transport and logistics processes and supply chains. This article analyses the possibilities and prospects for the application of machine learning and geospatial knowledge in the field of logistics and transport using specific examples. The long-term impact of geospatial-based artificial intelligence systems on such processes as procurement, delivery, inventory management, maintenance, customer interaction is considered.

KW - Geospatial knowledge

KW - Logistics

KW - Machine learning

KW - Spatial modelling

UR - http://www.scopus.com/inward/record.url?scp=85120500844&partnerID=8YFLogxK

U2 - 10.1088/1742-6596/2068/1/012042

DO - 10.1088/1742-6596/2068/1/012042

M3 - Conference article

AN - SCOPUS:85120500844

VL - 2068

JO - Journal of Physics: Conference Series

JF - Journal of Physics: Conference Series

SN - 1742-6588

IS - 1

M1 - 012042

T2 - 2021 4th International Conference on Applied Mathematics, Modeling and Simulation, AMMS 2021

Y2 - 17 September 2021 through 18 September 2021

ER -

ID: 89520050