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Comparative Analysis of Subcontracting Scheduling Methods

  • Konstantin Aksyonov
  • Anna Antonova
  • Eugene Sysoletin
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 710)

Abstract

This paper considers the following methods of the work scheduling: network planning techniques (critical path method, program evaluation and review technique, and graphical evaluation and review technique), method of agents cooperation in the needs-and-means networks proposed by Skobelev P.O., method of simulation and genetic algorithms integration proposed by Kureichik V.V., and method of multiagent genetic optimization developed by the authors based on the Kureichik method. As a result of the comparative analysis, the advantages of the method of multiagent genetic optimization in terms of solving the problem of subcontracting scheduling have been revealed. The multiagent genetic optimization method takes into account the nonrenewable resources, allows implementing different resource allocation strategies using simulation and multiagent modeling, and allows optimizing subcontract resources via analysis of alternative work schedules using genetic algorithms and simulation.

Keywords

Subcontracting scheduling Network planning techniques Genetic algorithms Simulation Multiagent modeling 

Notes

Acknowledgements

This work is supported by Act 211 Government of the Russian Federation, contract No 02.A03.21.0006.

References

  1. 1.
    Aksyonov, K. and Antonova, A.: Multiagent genetic optimisation to solve the project scheduling problem under uncertainty. International Journal on Advances in Software, 7(1&2) (2014) 1–19Google Scholar
  2. 2.
    Aksyonov, K., Bykov, E., Aksyonova, O., and Antonova, A.: Development of real-time simulation models: integration with enterprise information systems. In: Proceedings of the Ninth International Multi-Conference on Computing in the Global Information Technology (2014) 45–50Google Scholar
  3. 3.
    Borodin, A., Kiselev, Y., Mirvoda, S., and Porshnev, S.: On design of domain-specific query language for the metallurgical industry. In: Proceedings of 11th International Conference BDAS: Beyond Databases, Architectures and Structures: Communications in Computer and Information Science (2015) 505–515Google Scholar
  4. 4.
    Goffe, V., Ferrier, G., and Rogers, J.: Global optimization of statistical functions with simulated annealing. Journal of Econometrics 60 (1994) 65–99Google Scholar
  5. 5.
    Goldberg, D.: Genetic algorithms. Addison Wesley, (1989)Google Scholar
  6. 6.
    Kureichik, V.M., Malioukov, S., Kureichik, V.V., and Malioukov, A.: Genetic Algorithms for Applied CAD Problems. Springer (2009)Google Scholar
  7. 7.
    Lehman, J. and Stanley, K.: Exploiting open-endedness to solve problems through the search for novelty. In: Proceedings of the Eleventh International Conference Artificial Life (ALIFE XI) (2008) 329–336Google Scholar
  8. 8.
    Moder, J. and Elmaghraby, S. (Eds.): Handbook of operations research: foundations and fundamentals, Vol. 1. New York: Van Nostrand-Reinhold, 2nd. ed., (1978)Google Scholar
  9. 9.
    Moder, J. and Elmaghraby, S. (Eds.): Handbook of operations research: models and applications, Vol. 2. New York: Van Nostrand-Reinhold, 2nd. ed., (1978)Google Scholar
  10. 10.
    Pritsker, A. and Happ, W.: GERT: graphical evaluation and review technique: Part I, Fundamentals. Journal of Industrial Engineering, 17(6), (1966) 267–274Google Scholar
  11. 11.
    Rzevski, G., Himoff, J., and Skobelev, P.: MAGENTA technology: a family of multi-agent intelligent schedulers. In: Proceedings of International conference on multi-agent systems: Workshop on Software Agents in Information Systems and Industrial Applications 2 (SAISIA), Germany: Fraunhofer IITB (2006)Google Scholar
  12. 12.
    Vittikh, V. and Skobelev, P.: Multiagent interaction models for constructing the needs-and-means networks in open systems. Automation and Remote Control, 64, (2003) 162–169Google Scholar
  13. 13.
    Wooldridge, M.: Intelligent Agent: Theory and Practice. Knowledge Engineering Review, Vol. 10 (2), (1995)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Konstantin Aksyonov
    • 1
  • Anna Antonova
    • 1
  • Eugene Sysoletin
    • 1
  1. 1.Department of Information Technology and AutomationUral Federal UniversityYekaterinburgRussian Federation

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