Scientific Journal

Applied Aspects of Information Technology

The article deals with the theoretical aspects of effective allocation of subsets of the valid options sets in technology making design decisions. As a result of analysis of the current state of the problem revealed that due to the combinatorial nature of most tasks synthesis number of alternative solutions dramatically increases with the dimension of design problems. The vast majority of options is ineffective. They can be improved at the same time on all the quality parameters. This leads to the need to develop methods for the isolation procedures subsets of effective design solutions tailored to the features of the original sets, as the complexity of the requirements and the accuracy of the solution. To meet the challenges of various dimensions on convex and nonconvex set of feasible options to choose the exact and approximate methods based on pair-wise analysis of the options, theorems Karlin and Germeyer. To reduce the time complexity problem solutions proposed methods of pre-allocate a plurality of approximate methods effective solutions “sector” and “segment”. According to the analysis method estimates the computational complexity as a function of the dimension of the original set of alternatives and the amount of local optimization criteria established that the selection of sets of effective solutions of approximate the original set of alternatives at high power always is appropriate. This can significantly reduce the complexity of solving the decision-making tasks without loss of effective alternatives. The analysis time complexity methods revealed that the most efficient for large-scale problems is to use a scheme based on a modified method “segment”. The results are recommended to be used in the procedures for multifactor solutions in the design and management systems. Their use will improve the degree of automation of processes.
1. Bezruk, V. M., Chebotareva, D. V. & Skorik, Yu. V. (2017). “Mnogokriterialnyiy analiz i vyibor sredstv telekommunikatsiy”. [Multi criteria analysis and choice of telecommunication facilities]. Kharkiv, Ukraine, FOP Koryak S.F., 268 p. (in Russian). 
2. Gubarenko, E. V., Ovezgeldyiev, A. O, & Petrov, E. G. (2013). “Modeli i metodyi upravleniya ustoychivyim razvitiem sotsialno-ekonomicheskih sistem”. [Models and methods for managing the sustainable development of socio-economic systems]. Herson, Ukraine, Grin D. S., 252 p. (in Russian). 
3. Chebotareva, D. V., & Bezruk, V. M. (2013). “Mnogokriterialnaya optimizatsiya proektnyih resheniy pri planirovanii sotovyih setey mobilnoy svyazi”. [Multi criteria optimization of design decisions when planning cellular mobile networks]. Harkov, Ukraine, Kompaniya SMIT, 148 p. (in Russian). 
4. Malyar, M. M. (2016). “Modeli i metody bagatokriterialnogo obmezheno-ratsionalnogo vyboru”. [Models and methods of multicriteria limited rational choice]. Uzhgorod, Ukraine, RA “AUTDOR-ShARK”, 222 p. (in Ukrainian). 
5. Petrov, E. G., Bryinza, N. A., Kolesnik, L. V. & Pisklakova O. A. (2014). “Metody i modeli prinyatiya resheniy v usloviyah mnogokriterialnosti i neopredelennosti”. [Methods and models of decision making under conditions of multicriteria and uncertainty]. Herson, Ukraine, Grin D. S., 192 p. (in Russian). 
6. Ataei, M., Shahsavany, H. & Mikaeil, R. (2013). “Monte Carlo Analytic Hierarchy Process (MAHP) approach to selection of optimum mining method”. International Journal of Mining Science and Technology, Vol. 23, pp. 573-578. 
7. Bagočius, V., Zavadskas, E. K., & Turskis, Z. (2014). “Multi-person selection of the best wind tur-bine based on the multi-criteria integrated additive-multiplicative utility function”. Journal of Civil Engineering and Management, Vol. 20, pp. 590-599. 
8. Baky, I. A. (2014). “Interactive TOPSIS algorithms for solving multi-level non-linear multiobjective decision-making problems”. Applied Mathematical Modelling, Vol. 38, pp. 1417-1433. 
9. Baky, I. & Abo-Sinna, M. A. (2013). “TOPSIS for bi-level MODM problems”. Applied Mathematical Modelling, No. 37, pp. 1004-1015. 
10. Bernasconi, M., Choirat, C., & Seri, R. (2014). “Empirical properties of group preference aggrega-tion methods employed in AHP: Theory and evidence”. European Journal of Operational Research, No. 232, pp. 584-592. 
11. Greco S., Ehrgott M. & Figueira J. R. (2016). “Multiple Criteria Decision Analysis – State of the Art Surveys”. New York, USA, Springer, 1346 p. 
12. Kaliszewski I., Kiczkowiak T. & Miroforidis J. (2016). “Mechanical design, Multiple Criteria Decision Making and Pareto optimality gap”. Engineering Computations, Vol. 33(3). pp. 876-895. 
13. Timchenko, A. A. (2000). “Osnovy systemnogo proektuvannya ta analizu skladnyh objektiv”. [Fundamentals of system design and analysis of complex objects]. U 2-h kn. Kn. 1. “Osnovy SAPR ta systemnogo proektuvannya skladnyh objektiv”. [Basics of CAD and system design of complex objects]: za red. V. I. Bykova. Kiev, Ukraine, Lybid, 272 p. (in Ukrainian). 
14. Beskorovaynyiy, V. V., Imangulova, Z. A., Petrov, S. V., Koshel, A. V. & Voskalenko, A. S. (2016). “Sintez logicheskoy shemyi sistemnogo proektiro-vaniya sistem kontrolya krupnomasshtabnyih ob'ek-tov”. [Synthesis of the logical scheme of system design of control systems for large-scale objects], Scientific works of Kharkiv National Air Force University, No. 4 (49), pp. 70-74 (in Russian). 
15. Vilkas, E. Y. & Mayminas, E. Z. (1981). “Reshenie: teoriya, informatsiya, modelirovanie”. [Solution: theory, information, modeling]. Moscow, Russian Federation, Radio i svyaz, 328 p. (in Russian). 
16. Makarov, I. M., Vinogradskaya, T. M., Rubinskiy, A. A. & Sokolov, V. B. (1982). “Teoriya vyibora i prinyatiya resheniy”. [Theory of choice and decision making]. Moscow, Russian Federation, Nauka, 328 p. (in Russian). 
17. Ovezgeldyiev, A. O., Petrov, E. G. & Petrov, K. E. (2002). “Sintez i identifikatsiya modeley mnogofaktornogo otsenivaniya i optimizatsii”. [Synthesis and identification of multivariate assessment and optimization models]. Kyiv, Ukraine, Nauk. dumka, 164 p. (in Russian). Bezruk, V. M., Buhanko, O. M. & Chebotarova, D. V. (2014). “Optimizatsiya ta matematichne modelyuvannya merezh zv’yazku”. [Optimization is a mathematical model that’s just a little bit]. Harkiv, Ukraine, Kompaniya SMIT, 194 p. (in Ukrainian). 
18. Beskorovaynyiy, V. V. & Podolyaka K. E. (2016). “Vybor mnogokriterialnyh resheniy pri reinzhiniringe topologicheskih struktur sistem krupnomasshtabnogo monitoring’. [The choice of multicriteria solutions for the reengineering of topological structures of large-scale monitoring systems]. Sistemi obrobki Informatsiyi, No. 5(142), pp. 80-86. 
19. Beskorovaynyiy, V. V. & Krasko, A. F. (2007). “Avtomatizatsiya protsessov vybora effektivnyh resheniy pri avtomatizirovannom proektirovanii sistem upravleniya i avtomatiki”. [Automation of the processes of choosing effective solutions for the automated design of control systems and automation]. Vestnik Hersonskogo natsionalnogo tehnicheskogo universiteta, No. 4 (27), pp. 208-212 (in Russian). 
20. Kalyanmoy Deb & Debayan Deb. (2014). “Analysing mutation schemes for real-parameter genetic algorithms”. International Journal of Artificial Intelligence and Soft Computing, No. 4(1), pp. 1-28. 
21. Kalyanmoy Deb & Himanshu Jain (2014). “An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach”. Part I: Solving problems with box constraints. IEEE Trans. Evolutionary Computation, No. 18(4), pp. 577-601. 22. Deb, K. (2011) “Multi-objective Optimisation Using Evolutionary Algorithms: An Introduction”. In book: Multi-objective Evolutionary Optimisation for Product Design and Manufacturing, Springer London, pp.3-34. DOI: 10.1007/978-0- 85729-652-8_1. 
22. Deb, K., Pratap, A., Agarwal, S. & Meyarivan, T. (2002). “A fast and elitist multiobjective genetic algorithm: NSGA-II”. IEEE transactions on evolutionary computation, Vol. 6(2), pp. 182-197. 
23. Shadura, О. (2019). “Modyfikazija genetychnych algorytmiv na osnovi metodu nezentrovanych golovnych komponent ta standartni testy”. [Modification of Genetic Algorithms Based on the Uncited Principal Component Method and Standard Tests]. World Science, No. 4(44), pp. 4-11 (in Ukrainian). 
24. Mark Velasquez1 & Patrick T. Hester (2013). “An Analysis of Multi-Criteria Decision Making Methods”. International Journal of Operations Research, Vol. 10, No. 2, pp. 56-66. 
25. Abbas Mardani, Ahmad Jusoh, Khalil MD Nor, Zainab Khalifah, Norhayati Zakwan & Alireza Valipour. (2015). “Multiple criteria decision-making techniques and their applications – a review of the literature from 2000 to 2014”. Economic Research, Vol. 28, No. 1, pp. 516-571, doi:10.1080/1331677X.2015.1075139. 
26. Mikhalevich, V. S. & Volkovich, V. L. (1982). “Vichislitelnye metody issledovanija I proektiorovanija sloshnych system”. [Computational methods of research and design of complex systems]. Moscow, Russian Federation, Наukа, 288 p. 
27. Beskorovainyi, V. & Berezovskyi, G. (2017). “Estimating the properties of technological systems based on fuzzy sets”. Innovative technologies and scientific solutions for industries, No.1 (1), pp. 14-20. DOI: 10.30837/2522- 9818.2017.1.014. 
28. Beskorovainyi, V. & Berezovskyi, H. (2017). “Іdentification of preferences in decision support systems”. ECONTECHMOD, Vol. 06, No.4. pp. 15-20.

Received 05.01.2020
Received after revision 30.01.2020
Accepted 11.02.2020
Last download:
22 Oct 2021


[ © KarelWintersky ] [ All articles ] [ All authors ]
[ © Odessa National Polytechnic University, 2018.]