Cite this article:

Sendzuk, M., Naumenko, I., Derzhuk, O. (2018). Evolutionary strategy as a method of improving the balanced scorecard of enterprise management. Ed.: M. Zveryakov (ed.-in-ch.) and others [Evoliutsiina stratehiia yak metod udoskonalennia zbalansovanoi systemy pokaznykiv upravlinnia pidpryiemstvom: za red.: M. I. Zveriakova (gol. red.) ta in.], Socio-economic research bulletin; Vìsnik socìal’no-ekonomìčnih doslìdžen’ (ISSN 2313-4569), Odessa National Economic University, Odessa, No. 1 (65), pp. 207‒216.

Abstract

The article is devoted to the application of the method of evolutionary strategy for improving the enterprise management as a type of evolutionary algorithms, namely, the completion of a balanced scorecard by the algorithm of the evolutionary strategy, which will significantly increase the efficiency of strategic and operational decision- making in the enterprise management. Theoretical and applied developments are based on research on improving enterprises balanced scorecard. The innovative computer technology of the implementation of evolutionary strategy algorithm is developed. The results of the application of the evolution strategy algorithm at the enterprise (wagon-renovating plant) are presented. Based on the results of conducted calculations the analysis is made and the propositions for improving the enterprise management system.

Keywords

evolutionary strategy algorithm; balanced scorecard; genetic algorithm; evolutionary approach; heuristic method.

JEL classification: С220; R490; DOI: https://doi.org/10.33987/vsed.1(65).2018.207-216

UD classification: 005.21-043.86:330.34:005.93

This work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

References

1. Grytsyshyn, J. М., Korpyliov, D. V., Kryvyi, R. Z., Sviridova, T. V., Tkachenko, S. P. (2009). Genetic algorithms for solving placement problems [Genetychni alhorytmy dlia rozviazannia zadach rozmishchennia], Visnyk Natsionalnoho Universytetu «Lvivska Politekhnika», No. 638, s. 271–276 [in Ukrainian]
2. Poli, R., Langdon, W. B., McPhee, N. F. (2008). A field guide to genetic programming, available at: http://digitalcommons.morris.umn.edu/cgi/viewcontent.cgi?article=1001&context=cs_facpubs.
3. Back, T. (1996). Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms, Oxford University Press, New York, 319 p.
4. Cordon, О., Herrera-Viedma, E., Luque, M. (2002). Evolutionary learning of boolean queries by multiobjective genetic programming, Parallel Problem Solving from Nature – PPSN VII, LNCS, Granada, Spain, No. 2439, pp. 710–719.
5. Paniagua, R. S., Flores Romero, J. J., Coello C. A. (2007). A genetic representation for dynamic system qualitative models on genetic programming: a gene expression programming approach, Proceedings of 6th Mexican international conference on artificial intelligence, MICAI, Aguascalientes, Mexico, No. 4827, pp. 30–40.
6. Al-Sakran, H., Koza, J. R., Jones, L. W. (2005). Automated re-invention of a previously patented optical lens system using genetic programming, Proceedings of 8th European conference on genetic programming, Lausanne, Switzerland, No. 3447, pp. 25–37.
7. Rutkovskaya, D., Pilinskiy, M., Rutkovskiy, L. (2006). Neural networks, genetic algorithms and fuzzy systems. Trans. from Pol. [Neyronnye seti, geneticheskie algoritmy i nechetkie sistemy: Translation by I. D. Rudinskyi], Goryachaya liniya-Telekom, Мoskva, 452 s. [in Russian]
8. Vitlinskii, V. V., Skitsko, V. I. (2013). Evolutionary modeling in decision-making processes. [Evoliutsiine modeliuvannia v protsesakh pryiniattia rishen], Aktualni problemy economiky, No. 1, s. 187–201 [in Ukrainian]