Skip to main content
Springer Nature Link
Log in

Methods and Tools for Developing Intelligent Systems for Solving Complex Real-Time Adaptive Resource Management Problems

  • Published:
Automation and Remote Control Aims and scope Submit manuscript

Abstract

We give the statement of the real-time adaptive enterprise resource management problem and provide examples of contemporary adaptive resource management problems in various fields of application, showing the dimension and other features of the problems being solved. Requirements for the solution of the problems under consideration are stated, and general principles improving the resource management adaptability are proposed. Existing approaches are briefly analyzed, and their limitations are highlighted. A new methodology for solving the problems under consideration is proposed, an overview of developments is given, and the first experience of applying this methodology to the creation of intelligent resource management systems is discussed. The possibility of solving extremely complex resource management problems is shown by means of a modified ontology-based concept of the network of needs and capacities. In the framework of this concept, the schedule is constructed as a “competitive equilibrium” in the virtual market of a unified multiagent system, tuned to a specific enterprise with the use of applied ontologies. An example of developing a prototype of an intelligent resource management system for a multisatellite Earth remote sensing constellation and the results of its experimental study are given. An approach to assessing the performance and efficiency of adaptive scheduling models and methods developed for real-time resource management is proposed. Prospects for further development of the approach to solving complex adaptive resource management problems are shown.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.
Fig. 8.

Similar content being viewed by others

REFERENCES

  1. Taha, H.A., Operations Research: an Introduction, Upper Saddle River, N.J.: Pearson, 2003. Translated under the title: Vvedenie v issledovanie operatsii, Kiev: Williams, 2005.

    MATH  Google Scholar 

  2. Novikov, D.A., Control system classifications, Probl. Upr., 2019, no. 4, pp. 27–42.

  3. Lazarev, A.A. and Gafarov, E.R., Teoriya raspisanii. Zadachi i algoritmy (Scheduling Theory. Problems and Algorithms), Moscow: Izd. Mosk. Gos. Univ., 2011.

    Google Scholar 

  4. Handbook of Scheduling: Algorithms, Models and Performance Analysis, Leung, J., Ed., Chapman & Hall, 2004.

  5. Vos, S., Meta-heuristics: the state of the art, in Local Search for Planning and Scheduling, Nareyek, А., Ed., Berlin: Springer-Verlag. 2001.

  6. Pinedo, M., Scheduling Theory, Algorithms, and Systems, Heidelberg: Springer, 2008.

    MATH  Google Scholar 

  7. Skobelev, P.O., Open multiagent systems for on-line data processing in the decision making processes, Avtometriya, 2002, no. 6, pp. 45–61.

  8. Vittikh, V.A. and Skobelev, P.O., Multiagent interaction models for constructing the needs-and-means networks in open systems, Autom. Remote Control, 2003, vol. 64, no. 1, pp. 162–169. https://doi.org/10.1023/A.1021836811441

  9. Vittikh, V.A. and Skobelev, P.O., Method of coupled interactions for real-time management of resource allocation, Avtometriya, 2009, no. 2, pp. 78–87.

  10. Skobelev, P.O., Multiagent technologies in industrial applications: to the 20th anniversary of the founding of the Samara scientific school of multiagent systems, Mekhatron. Avtom. Upr., 2010, no. 12, pp. 33–46.

  11. Skobelev, R., Bio-inspired multi-agent technology for industrial applications, in Multi-Agent Systems—Modeling, Control, Programming, Simulations and Applications, Faisal Alkhateeb, F. et al., Eds., Austria: InTech Publ., 2011, pp. 495–522.

  12. Rzevski, G. and Skobelev, P., Managing Complexity, London–Boston: WIT Press, 2014.

    Google Scholar 

  13. Skobelev, P., Towards autonomous AI systems for resource management: applications in industry and lessons learned, Demazeau, Y. et al., Eds., Proc. 16th Int. Conf. Pract. Appl. Agents Multiagent Syst. (PAAMS 2018), 2018, LNAI 10978, pp. 12–25. https://doi.org/10.1007/978-3-319-94580-4_2

  14. Mescon, M., Albert, M., and Khedouri, F., Management: Individual and Organizational Effectiveness, New York: Harper & Row, 1988. Translated under the title: Osnovy Menedzhmenta, Moscow: Delo, 1997.

    Google Scholar 

  15. Deming, E., The New Economics for Industry, Government, Education, Cambridge, MA: MIT Press, 2000. Translated under the title: Menedzhment novogo vremeni: Prostye mekhanizmy, vedushchie k rostu, innovatsiyam i dominirovaniyu na rynke, Moscow: Alpina, 2019.

    Google Scholar 

  16. Merdan, M., Moser, T., Sunindyo, W., Biffl, S., and Vrba, P., Workflow scheduling using multi-agent systems in a dynamically changing environment, J. Simul., 2012, vol. 7(3), pp. 144–158.

    Google Scholar 

  17. Deng, L., Lin, Y., and Chen, M., Hybrid ant colony optimization for the resource-constrained project scheduling problem, J. Syst. Eng. Electron., 2010, vol. 21, no. 1, pp. 67–71.

    Article  Google Scholar 

  18. Jaberi, M., Resource constrained project scheduling using mean field annealing neural networks, Int. J. Multidiscip. Sci. Eng., 2011, vol. 2, no. 7, pp. 6–12.

    Google Scholar 

  19. Zhang, H., Xu, H., and Peng, W., A genetic algorithm for solving RCPSP, Int. Symp. Comput. Sci. Comput. Technol. (ISCSCT ’08), 2008, vol. 2, pp. 246–249.

  20. Burkov, V.N. and Novikov, D.A., Teoriya aktivnykh sistem: sostoyanie i perspektiva (Active Systems Theory: State and Perspective), Moscow: Sinteg, 1999.

    Google Scholar 

  21. Ivanyuk, V., Abdikeyev, N., Patshenko, F., and Grineva, N., Network-centric methods management, in Management Science, Moscow: Financ. Univ. Gov. Russ. Fed., 2017, vol. 7(1), pp. 26–34.

  22. Chigani, A., Jamed, D., and Bohner, S., Architecting network-centric software systems: a style-based beginning, 1st IEEE Software Eng. Workshop (SEW ’07), pp. 290–299. https://doi.org/10.1109/SEW.2007.95

  23. Wooldridge, M. and Jennings, N., Intelligent agents: theory and practice, Knowl. Eng. Rev., 1995, vol. 10(2), pp. 115–152.

    Article  Google Scholar 

  24. Gorodetskii, V.I, Grushinskii, M.S., and Khabalov, A.V., Multiagent systems, Nov. Iskusstv. Intell., 1998, no. 2, pp. 64–116.

  25. Panteleev, M.G. and Puzankov, D.V., Intellektual’nye agenty i mnogoagentnye sistemy (Intelligent Agents and Multiagent Systems), St. Petersburg.: Izd. LETI, 2016.

    Google Scholar 

  26. Pearce, J., Tambe, M., and Maheswaran, R., Solving multiagent networks using distributed constraint optimization, AI Mag., 2008, vol. 29(3). http://teamcore.usc.edu/papers/2008/K-optimality%20AI%20mag%202.pdf .

  27. Modi, P.J., Shen, W., Tambe, M., and Yokoo, M., ADOPT: Asynchronous distributed constraint optimization with quality guarantees, Artif. Intell. J., 2005, vol. 161 (1–2), pp. 149–180.

    Article  MathSciNet  Google Scholar 

  28. Petcu, A., A class of algorithms for distributed constraint optimizations, Frontiers in Artificial Intelligence and Applications, IOS Press, 2009, vol. 194(1). https://doi.org/10.5075/epfl-thesis-3942

  29. Evolutionary Multiobjective Optimization Theoretical Advances and Applications, Abraham, A., Jain, L.C., and Goldberg, R., Eds., Heidelberg, Springer, 2005. https://doi.org/10.1007/1-84628-137-7

  30. Brussel, H., Wyns, J., Valckenaers, P., et al., Reference architecture for holonic manufacturing systems: PROSA, Comput. Ind., 1998, no. 37, pp. 255–274.

  31. Sandholm, T. and Lesser, V., Issues in automated negotiation and electronic commerce: extending the contract net framework, Proc. First Int. Conf. Multiagent Syst. (ICMAS-95) (1995), pp. 328–335.

  32. Sandholm, T. and Lesser, V., Leveled-commitment contracting: a backtracking instrument for multiagent system, AI Mag., 2002, vol. 23(3), p. 89.

    Google Scholar 

  33. Shoham, Y. and Leyton-Brown, K., Multi-Agent Systems: Algorithmic, Game Theoretic and Logical Foundations, Cambridge: Cambridge Univ. Press, 2009. http://www.masfoundations.org .

  34. Easley, D. and Kleinberg, J., Networks, Crowds, and Markets: Reasoning about a Highly Connected World, Cambridge: Cambridge Univ. Press, 2010. http://www.cs.cornell.edu/home/kleinber/networks-book .

  35. Amelina, N., Fradkov, A., Jiang, Y., and Vergados, D., Approximate consensus in stochastic networks with application to load balancing, IEEE Trans. Inf. Theory, 2015, vol. 61, no. 4, pp. 1739–1752.

    Article  MathSciNet  Google Scholar 

  36. Ivanskiy, Y., Amelina, N., Granichin, O., et al., Optimal step-size of a local voting protocol for differentiated consensuses achievement in a stochastic network with cost constraints, IEEE Conf. Control Appl. (CCA) (2015), pp. 1367–1372.

  37. Zhilyaev, A.A., Ontologies as a tool for creating open multiagent resource management systems, Ontologii Proekt., 2019, vol. 9, no. 2(32), pp. 261–281.

    Google Scholar 

  38. Minhas, S., Ontology based environmental knowledge management – a system to support decisions in manufacturing planning, 6th Int. Conf. Knowl. Eng. Ontology Dev. (2014), pp. 397–404.

  39. Jarvenpaa, E., Siltala, N., Hylli, O., and Lanz, M., The development of an ontology for describing the capabilities of manufacturing resources, J. Intell. Manuf., 2019, vol. 30, pp. 959–978.

    Article  Google Scholar 

  40. Khoroshevskii, V.F., Designing software systems controlled by ontologies: models, methods, implementations, Ontologiya Proekt., 2019, vol. 9, no. 4, pp. 429–448.

    Google Scholar 

  41. Rojko, A., Industry 4.0 concept: background and overview, Int. J. Interact. Mobile Technol., 2017, vol. 11(5), pp. 77–90.

    Article  Google Scholar 

  42. Leitao, P., Karnouskos, S., Ribeiro, L., et al., Smart agents in industrial cyber-physical systems, Proc. IEEE, 2016, vol. 104(5), pp. 1086–1101.

    Article  Google Scholar 

Download references

Funding

This work was supported by the Russian Foundation for Basic Research, project no. 20-37-90052.

Author information

Authors and Affiliations

  1. Samara State Technical University, Samara, 443100, Russia

    S. P. Grachev, A. A. Zhilyaev, V. B. Laryukhin, D. E. Novichkov & V. A. Galuzin

  2. Samara University, Samara, 443086, Russia

    E. V. Simonova

  3. Institute for Complex Systems Control Problems, Samara Federal Research Center, Russian Academy of Sciences, Samara, 443001, Russia

    I. V. Maiyorov & P. O. Skobelev

Authors
  1. S. P. Grachev
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. A. Zhilyaev
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. V. B. Laryukhin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. D. E. Novichkov
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. V. A. Galuzin
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. E. V. Simonova
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. I. V. Maiyorov
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. P. O. Skobelev
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to S. P. Grachev, A. A. Zhilyaev, V. B. Laryukhin, D. E. Novichkov, V. A. Galuzin, E. V. Simonova, I. V. Maiyorov or P. O. Skobelev.

Additional information

Translated by V. Potapchouck

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Grachev, S.P., Zhilyaev, A.A., Laryukhin, V.B. et al. Methods and Tools for Developing Intelligent Systems for Solving Complex Real-Time Adaptive Resource Management Problems. Autom Remote Control 82, 1857–1885 (2021). https://doi.org/10.1134/S0005117921110035

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S0005117921110035

Keywords