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CSOC 2017: Artificial Intelligence Trends in Intelligent Systems pp 456–465Cite as

Synthesis Production Schedules Based on Ant Colony Optimization Method

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 573)

Abstract

It is proposed to use ant algorithms together with the object-oriented simulation models. To optimize the functioning of the automated technological complex machining together with a modified ant algorithm it is designed object model, which allows to calculate the fitness function and evaluate potential solutions. The transition and calculation of the concentration for synthetic pheromone rules are determined for supposed directed ant algorithms.

Keywords

  • Natural computing
  • Ant colony algorithm
  • Production schedules component
  • Flexible manufacturing systems

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References

  1. Skobtsov, Y., Sekirin, A., Zemlyanskaya, S., Chengar, O., Skobtsov, V., Potryasaev, S.: Application of object-oriented simulation in evolutionary algorithms. In: Silhavy, R., Senkerik, R., Oplatkova, Z.K., Silhavy, P., Prokopova, Z. (eds.) Automation Control Theory Perspectives in Intelligent Systems. AISC, vol. 466, pp. 453–462. Springer, Cham (2016). doi:10.1007/978-3-319-33389-2_43

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Acknowledgments

The research described in this paper is partially supported by the Russian Foundation for Basic Research (grants 15-07-08391, 15-08-08459, 16-07-00779, 16-08-00510, 16-08-01277, 16-29-09482-ofi-i, 17-08-00797, 17-06-00108, 17-01-00139, 17-20-01214), grant 074-U01 (ITMO University), project 6.1.1 (Peter the Great St.Petersburg Polytechnic University) supported by Government of Russian Federation, Program STC of Union State “Monitoring-SG” (project 1.4.1-1, project 6MCГ/13-224-2), state order of the Ministry of Education and Science of the Russian Federation № 2.3135.2017/K, state research 0073–2014–0009, 0073–2015–0007, International project ERASMUS +, Capacity building in higher education, № 73751-EPP-1-2016-1-DE-EPPKA2-CBHE-JP, Innovative teaching and learning strategies in open modelling and simulation environment for student-centered engineering education.

Author information

Authors and Affiliations

  1. St. Petersburg State National Research Polytechnic University, St. Petersburg, Russia

    Yuriy Skobtsov

  2. Federal Public Autonomous Educational Institution of the Higher Education Sevastopol State University, Sevastopol, Russian Federation

    Olga Chengar

  3. United Institute of Informatics Problems of National Academy of Sciences of Belarus, Minsk, Belarus

    Vadim Skobtsov

  4. Saint Petersburg National Research University of Information Technologies, Mechanics and Optics (ITMO), St. Petersburg, Russia

    Alexander N. Pavlov

  5. Mozhaisky Military Space Academy, St. Petersburg, Russia

    Alexander N. Pavlov

Authors
  1. Yuriy Skobtsov
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  2. Olga Chengar
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  3. Vadim Skobtsov
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  4. Alexander N. Pavlov
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Corresponding author

Correspondence to Vadim Skobtsov .

Editor information

Editors and Affiliations

  1. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlin, Czech Republic

    Radek Silhavy

  2. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlin, Czech Republic

    Roman Senkerik

  3. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlin, Czech Republic

    Zuzana Kominkova Oplatkova

  4. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlin, Czech Republic

    Zdenka Prokopova

  5. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlin, Czech Republic

    Petr Silhavy

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Skobtsov, Y., Chengar, O., Skobtsov, V., Pavlov, A.N. (2017). Synthesis Production Schedules Based on Ant Colony Optimization Method. In: Silhavy, R., Senkerik, R., Kominkova Oplatkova, Z., Prokopova, Z., Silhavy, P. (eds) Artificial Intelligence Trends in Intelligent Systems. CSOC 2017. Advances in Intelligent Systems and Computing, vol 573. Springer, Cham. https://doi.org/10.1007/978-3-319-57261-1_45

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  • DOI: https://doi.org/10.1007/978-3-319-57261-1_45

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-57260-4

  • Online ISBN: 978-3-319-57261-1

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