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Modelling and Data Analysis
2020. Vol. 10, no. 1, 86–95
https://doi.org/10.17759/mda.2020100105
ISSN: 2219-3758 / 2311-9454 (online)

Application of Criteria Aggregation Techniques for the Selection of Innovative Products

 
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Abstract

The peculiarity of innovative products requires taking into account a large number of criteria that should be aggregated into generalized ones and building a convolution tree with qualitative, quantitative and fuzzy rules. The paper proposes a methodology for the optimal partitioning of criteria scales into generalized gradations for using combined methods of multicriteria analysis of alternatives. The transition to fewer criteria leads to a signifi cant increase in the dimension of the scales of generalized criteria. Scales have to be converted to new scales with fewer gradations. To solve the problem of minimizing the information loss occurring when converting the scales picked Bellman function and applied method of dynamic programming. Computational experiments have shown the effectiveness of the proposed approach.

General Information

Keywords: innovative products, the aggregation criteria, the Bellman function, multicriteria analysis of alternatives, scale, ranging, integrated assessment, minimization of information loss

Journal rubric: Data Analysis

OpenAlex citations: 0

OpenAlex trends: Statistical and Computational Modeling, Advanced Research in Systems and Signal Processing, Multi-Criteria Decision Making

Information about the work in OpenAlex

Number of citations: 0

Topics

Statistical and Computational Modeling

This cluster of papers focuses on the application of inductive modeling techniques, particularly GMDH-type neural networks and interval models, in various scientific research domains. The papers cover topics such as environmental monitoring, modeling and prediction of complex processes, application of machine learning algorithms, and the use of self-organization techniques. The overarching theme revolves around the utilization of advanced computational methods for sustainable development and scientific analysis.

Number of works: 23498  |  Total number of citations: 170936

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Advanced Research in Systems and Signal Processing

This cluster of papers focuses on the integration of cyber, physical, and social systems, with an emphasis on decision making, urban computing, autodyne sensors, machine learning, data mining, transportation systems, information management, parallel computing, and infrastructure development.

Number of works: 31317  |  Total number of citations: 164598

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Multi-Criteria Decision Making

This cluster of papers focuses on the application and development of Multi-Criteria Decision Making (MCDM) methods, including Analytic Hierarchy Process (AHP), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and various fuzzy set theories. The papers cover topics such as group decision making, supplier selection, environmental decision making, and the use of Geographic Information Systems (GIS) in decision analysis.

Number of works: 95217  |  Total number of citations: 2310123

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Work details in OpenAlex

Article type: scientific article

DOI: https://doi.org/10.17759/mda.2020100105

Published

For citation: Sivakova, T.V., Sudakov, V.A. (2020). Application of Criteria Aggregation Techniques for the Selection of Innovative Products. Modelling and Data Analysis, 10(1), 86–95. (In Russ.). https://doi.org/10.17759/mda.2020100105

© Sivakova T.V., Sudakov V.A., 2020

License: CC BY-NC 4.0

References

  1. Michaelides P.G.Joseph Schumpeter and the German Historical School. Cambridge Journal of Economics. 33(3).2009, pp. 495–516.
  2. Corrente, S., Greco, S. and Slowinski R. Multiple Criteria Hierarchy Process in Robust Ordinal Regression. Decision Support Systems. 53(3). 2012, pp. 660–674. (In Russ., abstr. In Engl.)
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  4. Posadskij A.I., Sivakova T.V., Sudakov V.A. Agregirovanie nechetkih suzhdenijj ekspertov [Aggregation of fuzzy expert judgments]. Preprinty IPM im. M.V. Keldysha. 2019. № 101. 12 s. doi:10.20948/prepr-2019–101 URL: http://library.keldysh.ru/preprint.asp? id=2019–101.
  5. Saaty T.L. and Vargas L.G.Models Methods, Concepts and Applications of the Analytic Hierarchy Process. Boston: Kluwer Academic Publishers. 2000.
  6. Batkovskiy, A. M., Nesterov, V. A., Semenova, E. G., Sudakov, V. A. and Fomina A.V. Developing intelligent decision support systems in multi-criteria problems of administrative-territorial formations infrastructure projects assessment. Journal of Applied Economic Sciences. 5(51). 2017, pp. 1301– 1311.
  7. Arndt C. Information Measures: Information and its Description in Science and Engineering. Berlin: Springer. 2004.
  8. Eddy S.R. What is Dynamic Programming? Nature Biotechnology. 22(7). 2004, pp. 909–910.
  9. Sudakov V.A., Nesterov, V. A. and Kurennykh A.E. Integration of decision support systems ‘kosmos’ and WS-DSS with computer models. Proceedings of 2017 10th International Conference Management of Large-Scale System Development (MLSD). Moscow, Russia: IEEE.
  10. Sivakova T.V., Sudakov V.A. Metod nechetkih oblastej predpochtenii dlja ocenkij effektivnosti innovacij [Fuzzy areas preference method for evaluating innovation performance]. XXVIII Mezhdunarodnaja nauchno-tehnicheskaja konferencija “Sovremennye tehnologii v zadachah upravlenija, avtomatiki i obrabotki informacii”. Alushta, 14–20 sentjabrja 2019 g. Sbornik trudov. [Proceedings of the XXVIII International Scientifi c and Technical Conference “Modern Technologies in the Problems of Control, Automation and Information Processing”], Alushta. M.: Izd.-vo Nacional’nyj issledovatel’skij jadernyj universitet “MIFI”, 2019. S. 81–82.

Information About the Authors

Tatyana V. Sivakova, Researcher, Keldysh Institute of Applied Mathematics (Russian Academy of Sciences), researcher, Plekhanov Russian University of Economics, Moscow, Russian Federation, ORCID: https://orcid.org/0000-0001-8026-2198, e-mail: sivakova15@mail.ru

Vladimir A. Sudakov, Doctor of Engineering, Professor of Department 805, Moscow Aviation Institute (MAI), Leading Researcher, Keldysh Institute of Applied Mathematics (Russian Academy of Sciences), Moscow, Russian Federation, ORCID: https://orcid.org/0000-0002-1658-1941, e-mail: sudakov@ws-dss.com

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