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Regular version of the site

Book chapter

On the Development of an Expert Decision Support System Based on the ELECTRE Methods

P. 552-561.
Kravchenko T., Shevgunov T., Petrakov A.

Analytical justification of decision options using decision support
systems (DSS) significantly improves the quality of decisions. The use of the
currently existing DSS, which usually includes one or two decision-making
methods, does not always lead to the desired results, since each method is based
on certain assumptions and is not universal. The noticeable effect is achieved
when many decision-making methods are included in one DSS knowledge base.
The systems that meet these requirements belong to the class of Expert Decision
Support Systems (EDSS), which currently includes more than 50 decisionmaking
methods. Expanding the EDSS knowledge base, made by including new
methods in it, allows choosing the most suitable solution method for each
decision-making task. Addition of the decision table model, which is the basis of
the system knowledge base, allows developing EDSS without completely processing
the system program code. The ELECTRE methods were adopted for
expanding the EDSS knowledge base. The basis for the selection was their key
feature, which consists in the fact that they do not use the alternative valuation
convolution operation given in different scales according to individual criteria.
The article shows the adapted algorithms of the ELECTRE family methods
ready for inclusion in EDSS. The algorithm is proposed for obtaining a criterion
matrix being based on alternatives that serve as input information for the
ELECTRE methods in cases where there is no objective information to fill it.
The results of the study can be used to develop EDSS, that open the way to
analytically substantiate solutions using methods that were not previously used
in the system.























In book

Edited by: R. Silhavy Т. 1226: Proceedings of the 9th Computer Science On-line Conference 2020. Кн. 3: Applied Informatics and Cybernetics in Intelligent Systems. Springer, 2020.