| Peer-Reviewed

Two-Level Multi-criteria Model for Calculating Multinomenclature Spare Parts of an Auto Service Enterprise Based on the Rougher Algorithm for Optimizing the Behavior of Their Particles

Received: 29 August 2017     Accepted: 18 September 2017     Published: 8 November 2017
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Abstract

On the example of the two-criterion problem with the objective functions of the maximum, the confidence probabilities of the demand and the minimum of the total costs show the applicability of the method of Vector Optimization of Particle Swarm Optimization (VEPSO). Compared with genetic algorithms and other methods of evolutionary modeling, this method is easy to implement and has high efficiency, as well as the accelerated cost of an approximate solution of the problem from the external archive of the no dominant best solutions to the Pareto front, which is the boundary of the Pareto-optimal Compromise) solutions.

Published in Science Research (Volume 5, Issue 4)
DOI 10.11648/j.sr.20170504.12
Page(s) 57-64
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

Rougher Algorithm, Swarm Optimization, Vector Optimization of Particle Swarm Optimization (VEPSO)

References
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Cite This Article
  • APA Style

    Karimov Nijat Ashraf, Dyshin Oleq Aleksandr, Gozalov Sulhaddin Kamal. (2017). Two-Level Multi-criteria Model for Calculating Multinomenclature Spare Parts of an Auto Service Enterprise Based on the Rougher Algorithm for Optimizing the Behavior of Their Particles. Science Research, 5(4), 57-64. https://doi.org/10.11648/j.sr.20170504.12

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    ACS Style

    Karimov Nijat Ashraf; Dyshin Oleq Aleksandr; Gozalov Sulhaddin Kamal. Two-Level Multi-criteria Model for Calculating Multinomenclature Spare Parts of an Auto Service Enterprise Based on the Rougher Algorithm for Optimizing the Behavior of Their Particles. Sci. Res. 2017, 5(4), 57-64. doi: 10.11648/j.sr.20170504.12

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    AMA Style

    Karimov Nijat Ashraf, Dyshin Oleq Aleksandr, Gozalov Sulhaddin Kamal. Two-Level Multi-criteria Model for Calculating Multinomenclature Spare Parts of an Auto Service Enterprise Based on the Rougher Algorithm for Optimizing the Behavior of Their Particles. Sci Res. 2017;5(4):57-64. doi: 10.11648/j.sr.20170504.12

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  • @article{10.11648/j.sr.20170504.12,
      author = {Karimov Nijat Ashraf and Dyshin Oleq Aleksandr and Gozalov Sulhaddin Kamal},
      title = {Two-Level Multi-criteria Model for Calculating Multinomenclature Spare Parts of an Auto Service Enterprise Based on the Rougher Algorithm for Optimizing the Behavior of Their Particles},
      journal = {Science Research},
      volume = {5},
      number = {4},
      pages = {57-64},
      doi = {10.11648/j.sr.20170504.12},
      url = {https://doi.org/10.11648/j.sr.20170504.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sr.20170504.12},
      abstract = {On the example of the two-criterion problem with the objective functions of the maximum, the confidence probabilities of the demand and the minimum of the total costs show the applicability of the method of Vector Optimization of Particle Swarm Optimization (VEPSO). Compared with genetic algorithms and other methods of evolutionary modeling, this method is easy to implement and has high efficiency, as well as the accelerated cost of an approximate solution of the problem from the external archive of the no dominant best solutions to the Pareto front, which is the boundary of the Pareto-optimal Compromise) solutions.},
     year = {2017}
    }
    

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    T1  - Two-Level Multi-criteria Model for Calculating Multinomenclature Spare Parts of an Auto Service Enterprise Based on the Rougher Algorithm for Optimizing the Behavior of Their Particles
    AU  - Karimov Nijat Ashraf
    AU  - Dyshin Oleq Aleksandr
    AU  - Gozalov Sulhaddin Kamal
    Y1  - 2017/11/08
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    N1  - https://doi.org/10.11648/j.sr.20170504.12
    DO  - 10.11648/j.sr.20170504.12
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    PB  - Science Publishing Group
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    UR  - https://doi.org/10.11648/j.sr.20170504.12
    AB  - On the example of the two-criterion problem with the objective functions of the maximum, the confidence probabilities of the demand and the minimum of the total costs show the applicability of the method of Vector Optimization of Particle Swarm Optimization (VEPSO). Compared with genetic algorithms and other methods of evolutionary modeling, this method is easy to implement and has high efficiency, as well as the accelerated cost of an approximate solution of the problem from the external archive of the no dominant best solutions to the Pareto front, which is the boundary of the Pareto-optimal Compromise) solutions.
    VL  - 5
    IS  - 4
    ER  - 

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Author Information
  • Department of Automotive Engineering, Azerbaijan Technical University, Baku, Azerbaijan

  • Department of Applied Mechanics, Azerbaijan State University of Oil and Industry, Baku, Azerbaijan

  • Department of Automotive Engineering, Azerbaijan Technical University, Baku, Azerbaijan

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