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

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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.

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Authors and Affiliations

  1. Matrosov Institute for System Dynamics and Control Theory, Siberian Branch of the Russian Academy of Sciences, Irkutsk, Russia

    A. A. Lempert

  2. Melentiev Energy Systems Institute, Siberian Branch of the Russian Academy of Sciences, Irkutsk, Russia

    D. N. Sidorov

  3. Irkutsk National Research Technical University, Irkutsk, Russia

    D. N. Sidorov & G. L. Nguyen

  4. Irkutsk State University, Irkutsk, Russia

    A. V. Zhukov

Authors
  1. A. A. Lempert
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  2. D. N. Sidorov
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  3. A. V. Zhukov
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  4. G. L. Nguyen
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Corresponding author

Correspondence to A. A. Lempert.

Additional information

Original Russian Text © A.A. Lempert, D.N. Sidorov, A.V. Zhukov, G.L. Nguyen, 2016, published in Avtomatika i Telemekhanika, 2016, No. 11, pp. 4–17.

This paper was recommended for publication by A.A. Lazarev, a member of the Editorial Board

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Lempert, A.A., Sidorov, D.N., Zhukov, A.V. et al. A combined work optimization technology under resource constraints with an application to road repair. Autom Remote Control 77, 1883–1893 (2016). https://doi.org/10.1134/S0005117916110011

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  • DOI: https://doi.org/10.1134/S0005117916110011

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