Criterion for Ranking Interval Alternatives in a Decision-Making Task

Автор: Yuri Romanenkov, Vadym Mukhin, Viktor Kosenko, Daniil Revenko, Olena Lobach, Natalia Kosenko, Alla Yakovleva

Журнал: International Journal of Modern Education and Computer Science @ijmecs

Статья в выпуске: 2 vol.16, 2024 года.

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The study solves the problem of improving the methodological and algorithmic support of the decision-making process by developing a model of the preference criterion for interval evaluations of alternatives. The aim of the study is to improve the efficiency of decision-making based on interval expert data under conditions of uncertainty and risk by developing a criterion for the preferences of interval evaluations of overlapping alternatives. The object of the study is the decision-making process based on the classical efficiency matrix with interval elements, the subject is the model of decision maker's (DMP) preference criteria for interval evaluations of alternatives. The relevance of the task is stipulated by the urgency of the problem of adapting classical decision-making methods and models to practical problems of gray analysis, in particular, with interval uncertainty of primary expert data. A multifactorial model of the normalized preference criterion for interval evaluations of alternatives is proposed. Due to the additional consideration of the degree of preference of the DMP for the width of interval estimates, it allows ranking interval estimates of alternatives that overlap and are considered classically incomparable. A single analytical form of the normalized criterion model for ranking interval, weighted interval and point estimates makes it possible to increase the degree of automation of processing interval expert estimates in the decision-making process. Recommendations for the practical application of the proposed model are formulated. The developed model and corresponding algorithms can be used in automated expert decision support systems.

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Decision-making, preference criterion, interval analysis, expert evaluation, confidence probability

Короткий адрес: https://sciup.org/15019162

IDR: 15019162   |   DOI: 10.5815/ijmecs.2024.02.06

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