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In the context of multicriteria decision making, the ordered weightedaveraging (OWA) functions play a crucial role in aggregating multiple criteriaevaluations into an overall assessment supporting the decision makers' choice.Determining OWA weights, therefore, is an essential part of this process.Available methods for determining OWA weights, however, often require heavycomputational loads in real-life large-scale optimization problems. In thispaper, we propose a new approach to simplify the well-known minimax disparitymodel for determining OWA weights. For this purpose, we use to the binomialdecomposition framework in which natural constraints can be imposed on thelevel of complexity of the weight distribution. The original problem ofdetermining OWA weights is thereby transformed into a smaller scaleoptimization problem, formulated in terms of the coefficients in the binomialdecomposition. Our preliminary results show that a small set of thesecoefficients can encode for an appropriate full-dimensional set of OWA weights.

Simplifying the minimax disparity model for determining OWA weights in large-scale problems