Expanding the scope of the LODECI method for criteria weight determination

ABSTRACT Determining the weights of criteria is an essential task when ranking alternative options, especially in contexts where multiple criteria are used to characterize each alternative. The LOgarithmic DEcomposition of Criteria Importance (LODECI) is a weighting methodology known for its advantage of analyzing criterion importance based on the intensity of contrast among all alternatives with respect to each criterion. Nevertheless, the existing data normalization technique utilized within the original LODECI method-specifically, the Linear normalization method, becomes infeasible in certain scenarios. This limitation effectively prevents the use of the LODECI method for criteria weight calculation in those specific cases. To broaden the applicability of this methodology, the present research was undertaken to identify suitable alternative data normalization techniques that can be successfully integrated with LODECI, replacing the standard Linear method. Three normalization methods were considered for combination with LODECI: the Vector normalization method, the Weitendorf method, and the Enhanced accuracy method. The investigation into which of these three normalization methods could replace the Linear method for integration with LODECI was carried out using four distinct examples. The results unequivocally verified that the Vector normalization method is a viable substitute for the Linear method when coupled with LODECI.