A Robustness Study of State-of-the-Art Surrogate Weights for MCDM

A vast number of methods for solving multi-criteria decision problems have been suggested for assessing criteria weights requiring more exact input data than users normally are able to provide. In particular, the selection of adequate criteria weights is difficult and in order to be realistic, other methods must be introduced. One class of such methods is to introduce so called surrogate weights, where numerical weights are assigned to each criterion based on a cardinal or ordinal rank ordering, assumed to represent the information extracted from the user. One essential problem is the robustness of such methods. In this article, we compare state-of-the-art methods based on surrogate weights from the literature and, utilising a simulation approach, discuss underlying assumptions and robustness properties. This results in a quantitative measurement of these weighting methods and a methodology applicable also to forthcoming methods.