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Previous issue (2019. Vol. 09, no. 4)

Modelling and Data Analysis

Publisher: Moscow State University of Psychology and Education

ISSN (printed version): 2219-3758

ISSN (online): 2311-9454

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

License: CC BY-NC 4.0

Started in 2011

Published 4 times a year

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Application of Criteria Aggregation Techniques for the Selection of Innovative Products 16

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

Sudakov V.A.
Doctor of Engineering, Professor, Moscow Aviation Institute (National Research University), Moscow, Russia
ORCID: https://orcid.org/0000-0002-1658-1941
e-mail: sudakov@ws-dss.com

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.

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

Column: Data Analysis

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

For Reference

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