Simplifying the minimax disparity model for determining OWA weights in large-scale problems
This work addresses computational efficiency for decision-makers in large-scale optimization, but it appears incremental as it builds on an existing model with a simplification approach.
The paper tackles the computational burden of determining ordered weighted averaging (OWA) weights in large-scale multicriteria decision-making problems by simplifying the minimax disparity model, resulting in a transformed smaller-scale optimization problem that reduces computational load.
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.