Application of the PIV Method in the Presence of Negative Data: An Empirical Example from a Real-World Case
Application of the PIV Method in the Presence of Negative Data: An Empirical Example from a Real-World Case

Summary/Abstract: The presence of negative values in the decision matrix is a rare situation in Multiple Criteria Decision Making (MCDM) methods. In such a case, normalized matrix elements must be between 0 and 1 to use the Proximity Indexed Value (PIV) method. In this study, in which a real-life application is presented, two different solutions are generated for this problem. Firstly, negative decision matrix elements are converted to positive using the Z-score standardization method. Secondly, different normalization techniques are used instead of vector normalization in the algorithm of the PIV method. According to the obtained results, the most appropriate technique to use the PIV method in the presence of negative values in the decision matrix is the min-max normalization technique. The proposed model in this study supports the use the PIV method in the presence of negative values. In addition, this study is the first to test the suitability of different techniques for the PIV method.

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