Development of Methodological Foundations of Logistical Intellectual Control of Complex Systems Based on Hybrid Heuristic Algorithms

  • Authors

    • Elena Skakalina
    • . .
    • . .
    2018-10-13
    https://doi.org/10.14419/ijet.v7i4.8.27301
  • management of complex systems, heuristic algorithms, hybrid genetic algorithm, neuro-network group method of data handling, theory of fuzzy sets, information technologies
  • Systematized and generalized existing models of managing the production class of complex distributed systems. A formalized logical-plural model of a complex system is constructed, a description of all its elements is given. A two-level control system for this class of complex systems has been developed. The advantage of the developed system is the possibility of eliminating the existing contradictions between the resource constraints that exist at different structural levels of complex production systems. Elements of a set of control actions based on the principle of hybridization of heuristic algorithms are described. A hybrid genetic algorithm using a fuzzy set machine for a selection operator was used, which was used to optimize the overall logistic transportation plan. Verification of this algorithm was performed both on test functions and on objects of subject domains. The use of the apparatus of fuzzy sets in regulating the dimension of current populations makes it possible, within one formal apparatus, to implement the existing algorithms for selecting suitable individuals for further crossing. A neural-network modification of the method of group accounting of arguments is described as a short-term forecasting model. This model is verified on objects of subject areas, its adequacy and advantages are shown with short-term forecasting of production, financial and statistical indicators.

     

     

  • References

    1. [1] Zgurovsky M.Z., Pankratova N.D. (2007). Osnovy systemnoho analizu [Fundamentals of System Analysis]. -Kiev .: Publishing Group BHV.

      [2] Guide for the Business Process Management Common Body of Knowledge ABPMP BPM CBOK v.3.0, 2013. – ABPMP International. – 445 p.

      [3] Zadeh L.A. The concept of a linguistic variable and its application to appro1imate reasoning / L.A. Zadeh // Inform. Sci. O 1975. O Vol. 8. pp. 199-249.

      [4] Bronstein, E.M. and Zaiko T.A. (2010), “Deterministic optimization problems transport logisticsâ€, Automation and telemechanics. No. 10, pp. 133-147.

      [5] Dong, H., J. Ma, and M. H. Zhang, (2006), Calibration of Departure Time and Route Choice Parameters in Microsimulation with Macro Measurements and Genetic Algorithm. Presented at the 85th Annual Meeting of the Transportation Research Board, Washington, D.C.

      [6] J. E. Mendoza, B. Castanier, C. Gu´eret, A. L. Medaglia, and N. Velasco (2008): “Approximating the expected cost of recourse on a multi-compartment vehicle routing problem with stochastic demandsâ€. Tech. Rep. 08/03/AUTO, Ecole des Mines de Nantes, France.

      [7] A. El Fallahi, C. Prins, and R. Wolfler Calvo (2008): “A memetic algorithm and a tabu search for the multi-compartment vehicle routing problemâ€. In: Computers and Operations Research 35, pp.1725– 1741.

      [8] Friedrich H. and Gumpp J., (2014), “ Simplified Modeling and Solving of Logistics Optimization Problemsâ€, International Journal of Transportation Vol.2, No.1, pp.33-52 http://dx.doi.org/10.14257/ijt.2014.2.1.03.

      [9] Holland J. H. , (1975), Adaptation in Natural and Artificial Systems Ann Arbor: The University of Michigan Press.

      [10] Hallam N., Kendall G., and Blanchfield P. , (2006), “Solving Multi-objective Optimization Problems Using the Potential Pareto Regions Evolutionary Algorithmâ€, in T.P. Runarsson et al (Eds.): Parallel problem solving from nature (PPSN IX: 9th international conference), LNCS 4193, Springer-Verlag Berlin Heidelberg pp. 503-512.

      [11] De Jong K.A. (1989), “Evolutionary computation a unified approachâ€, A Bradford book. Cambridge: MA, USA .

      [12] Rosenbrock H.H., (1960) , An automatic method for finding the greatest or least value of a function. - The Computer Journal , no. 3, pp. 175–184.

      [13] Rastrigin L. A., (1974), Systems of Extremal Control - Nauka, Moscow.

      [14] Ivakhnenko A.G., Ivakhnenko G.A., (1995), The Review of Problems Solvable by Algorithms of the Group Method of Data Handling. Published in Pattern Recognition and Image Analysis, Vol. 5, No. 4, pp.527-535.

      [15] Loburets, A. T., Naumovets, A. G., Senenko, N. B., & Vedula, Y. S. (1997). Surface diffusion and phase transitions in strontium overlayers on W(112). Zeitschrift Fur Physikalische Chemie, 202(1-2), 75-85.

      [16] E. Skakalina , (2015), Applied aspects of using the method of group consideration of arguments in the short-term forecasting.- Scientific Bulletin of the National Mining University. - Issue No. 6 (150). - P. 80-88.

      [17] E. Skakalina, (2018), Investigation of intelligent technologies for formation forecasting models.- International Journal of Engineering & Technology.- 7(3.2). – pp.413-418.

      Cherniha, R., & Serov, M. (2006). Symmetries, ansätze and exact solutions of nonlinear second-order evolution equations with convection terms, II. European Journal of Applied Mathematics, 17(5), 597-605. https://doi.org/10.1017/S0956792506006681
  • Downloads

  • How to Cite

    Skakalina, E., ., ., & ., . (2018). Development of Methodological Foundations of Logistical Intellectual Control of Complex Systems Based on Hybrid Heuristic Algorithms. International Journal of Engineering & Technology, 7(4.8), 534-538. https://doi.org/10.14419/ijet.v7i4.8.27301