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Avtomat. i Telemekh., 2016, Issue 11, Pages 4–17 (Mi at14594)  

This article is cited in 2 scientific papers (total in 2 papers)

Topical issue

A combined work optimization technology under resource constraints with an application to road repair

A. A. Lemperta, D. N. Sidorovbc, A. V. Zhukovd, G. L. Nguenc

a Matrosov Institute for System Dynamics and Control Theory, Siberian Branch of the Russian Academy of Sciences, Irkutsk, Russia
b Melentiev Energy Systems Institute, Siberian Branch of the Russian Academy of Sciences, Irkutsk, Russia
c Irkutsk National Research Technical University, Irkutsk, Russia
d Irkutsk State University, Irkutsk, Russia

Abstract: We propose an approach for solving the task prioritization problem in road surface repair under bounded resources; the idea is to use a combination of defect recognition and classification methods based on statistical analysis and machine learning (random forests) with original methods for solving infinite-dimensional optimization problems (optical-geometric analogy). We show the results of a computational experiment that indicate high performance of the developed algorithms, and the resulting solutions were evaluated highly by experts in road facilities management. Our results may encourage more efficient use of resources to improve the quality of motorways.

Funding Agency Grant Number
Russian Foundation for Basic Research 14-07-00222
16-06-00464
This work was supported in part by the Russian Foundation for Basic Research, projects nos. 14-07-00222 and 16-06-00464.


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English version:
Automation and Remote Control, 2016, 77:11, 1883–1893

Bibliographic databases:

Presented by the member of Editorial Board: А. А. Лазарев

Received: 04.02.2016

Citation: A. A. Lempert, D. N. Sidorov, A. V. Zhukov, G. L. Nguen, “A combined work optimization technology under resource constraints with an application to road repair”, Avtomat. i Telemekh., 2016, no. 11, 4–17; Autom. Remote Control, 77:11 (2016), 1883–1893

Citation in format AMSBIB
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\paper A combined work optimization technology under resource constraints with an application to road repair
\jour Avtomat. i Telemekh.
\yr 2016
\issue 11
\pages 4--17
\mathnet{http://mi.mathnet.ru/at14594}
\elib{https://elibrary.ru/item.asp?id=28367184}
\transl
\jour Autom. Remote Control
\yr 2016
\vol 77
\issue 11
\pages 1883--1893
\crossref{https://doi.org/10.1134/S0005117916110011}
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\elib{https://elibrary.ru/item.asp?id=27591122}
\scopus{https://www.scopus.com/record/display.url?origin=inward&eid=2-s2.0-84994762968}


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    Citing articles on Google Scholar: Russian citations, English citations
    Related articles on Google Scholar: Russian articles, English articles

    This publication is cited in the following articles:
    1. T. H. Nguyen, T. L. Nguyen, D. N. Sidorov, A. I. Dreglea, “Machine learning algorithms application to road defects classification”, Intell. Decis. Technol.-Neth., 12:1 (2018), 59–66  crossref  isi  scopus
    2. Huong Thu Nguyen, Long The Nguyen, “Roc curve analysis for classification of road defects”, BRAIN-Broad Res. Artif. Intellect. Neurosci., 10:2 (2019), 65–73  isi
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