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Simplifying the Minimax Disparity Model for Determining OWA Weights in Large-Scale Problems

  • Thuy Hong Nguyen
Chapter
Part of the AIRO Springer Series book series (AIROSS, volume 1)

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. We use 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 the minimax disparity model encoded with a small set of these coefficients can be solved in less computation time than the original model including the full-dimensional set of OWA weights.

Keywords

Ordered weighted averaging OWA weights determination Binomial decomposition framework k-additive level Large-scale optimization problems 

Notes

Acknowledgements

The author would like to thank Ricardo Alberto Marques Pereira and Silvia Bortot for their helpful remarks on the manuscript. The author would like to thank to Andrea Mariello for his practical advice on the experiments. The author is grateful to the anonymous referees for their valuable suggestions.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Industrial EngineeringUniversity of TrentoPovoItaly

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