| Summary: | The presence of negative data 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 adopt the Proximity Indexed Value (PIV) method. In this study, which deals with real life application, two different solutions are presented to find a solution to this problem. Firstly, negative decision matrix elements are converted to positive using a z-score standardization method. Secondly, different normalization techniques are used instead of vector normalization in the algorithm of the PIV method. According to the results obtained, the most appropriate technique to reach a result with the PIV method in the presence of negative data is the min-max technique. The model proposed in this study supports the usage the PIV method in the presence of negative data. In addition, this study is the first to test the suitability of different techniques for the PIV method.
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