This work is devoted to the development of a new method for modeling information systems of multi-criteria design, based on expert ranking of criteria. The possibilities of the fuzzy-set theory and the possibility theory allow for a comprehensive analysis of criteria by reducing numerical indicators to a qualitative form, passing to the corresponding fuzzy relations of preference. Some of the information may be lost, therefore conclusions should be applied, both on the basis of “quantitative” processing of the results of measuring the criteria, and on the basis of “qualitative” processing. If these conclusions coincide, then their adequacy will be confirmed based on the initial data. Since the modeling of complex design systems is determined by several criteria defined on different base sets, and the problem of determining the multi-criteria efficiency in conditions of incompleteness of the initial data remains relevant, within the framework of this article, an optimization method is proposed simultaneously according to many criteria with incompleteness of the initial data. The difference between the described method lies in the fact that the expert ranking of criteria enables to formally set fuzzy preference graphs that make it possible to obtain comprehensive information about estimates in the rank scale during pairwise comparisons, with the aim of subsequent search for Pareto-optimal solutions in multi-criteria problems.

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This article briefly discusses methods for finding Pareto-optimal solutions for multi-criteria problems in the conditions of uncertainty. It is noted that the quality of management depends on the adequacy of the rule base for the selected fuzzy controller set by experts. The conditions for the final decision of the choice of the Pareto-optimal fuzzy solution are determined. An algorithm for the method of estimating the Pareto-optimal solution taking into account utility is developed and presented. Multicriteria optimization of the functioning of control systems is considered when setting local criteria in the form of fuzzy intervals. Scalarization methods that are used in ranking criteria are considered. Quantitative determination of the criterion is applicable if its numerical comparison is possible. The objective function is determined by local criteria. Optimization is associated with the search for control actions that provide optimal values of the multidimensional objective function. For problems with threshold optimization, a similar estimate can also be obtained. Given that, when analyzing solution options, complexity scales are constructed, an estimate of the time to build the complexity scale is also introduced. The application of the threshold optimization method to search for the Pareto optimal solution is considered. An approach to evaluating Pareto-optimal solutions using the criteria for evaluating utility is proposed. An algorithm has been developed to evaluate the Pareto-optimal solution taking into account utility.

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