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

TL;DR

This paper introduces a simplified approach to compute OWA weights in large-scale multicriteria decision making by using binomial decomposition, reducing computational complexity while maintaining effectiveness.

Contribution

It proposes a novel simplification of the minimax disparity model for OWA weights using binomial decomposition, enabling easier computation in large problems.

Findings

Small set of coefficients can encode a full set of OWA weights

Simplified model reduces computational load

Preliminary results show effective weight approximation

Abstract

In the context of multicriteria decision making, the ordered weighted averaging (OWA) functions play a crucial role in aggregating multiple criteria evaluations 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 heavy computational loads in real-life large-scale optimization problems. In this paper, we propose a new approach to simplify the well-known minimax disparity model for determining OWA weights. For this purpose, we use to the binomial decomposition framework in which natural constraints can be imposed on the level of complexity of the weight distribution. The original problem of determining OWA weights is thereby transformed into a smaller scale optimization problem, formulated in terms of the coefficients in the binomial decomposition. Our preliminary results show that a small set of these coefficients can encode for an appropriate full-dimensional set of OWA weights.