Abstract
NormNormalization is an essential step in data analysis and for MCDM methods. This study aims to outline the positive and negative features of the normalization techniques that can be used in MCDM problems. In order to compare the different normalization techniques, fourteen sets representing different scenarios of decision problems were used. According to the results, if the decision-maker chooses to take the alternative with the highest value in the criteria and avoid the one with the lowest value, or vice versa, optimization-based normalization techniques should be preferred, whereas the reference-based normalization techniques are considered appropriate for situations where there are ideal values determined by the decision-maker for each criterion. However, if the decision-maker believes that the values in the criteria do not represent the monotonous increasing or decreasing benefit/cost, then non-linear normalization techniques should be used. Also, in the event of a change in the conditions mentioned above, the decision maker may opt for mixed normalization techniques. However, some data structures, such as the presence of zero, and negative values in the decision matrix, can prevent the use of some normalization techniques. The choice of the normalization technique may also be affected by the problem of rank reversal, the range of normalized values, obtaining the same optimization aspect for all criteria, and the validity of results.
Highlights
In quantitative research, researchers often try to use methods appropriate for the data structures
Some normalization techniques provide a mixed/integrated normalization process with the idea that the optimization orientation and reference value are vital for different criteria that can be found at the same time in the decision problem
The following section will examine the normalization techniques which depend on the optimization orientation, those that are independent of the optimization orientation, and those that have a mixed structure
Summary
Researchers often try to use methods appropriate for the data structures. Studies considering the selection of the normalization technique, and the criteria to be used in this selection process are limited Another important issue is that every normalization technique cannot be suitable for all decision problems. It is, necessary to investigate the extent to which the normalization techniques have achieved their purposes of development, their roles in the problem, and the MCDM method, their dimensionlessness, and comparability. Necessary to investigate the extent to which the normalization techniques have achieved their purposes of development, their roles in the problem, and the MCDM method, their dimensionlessness, and comparability In this context, this study will examine the practical comparisons of the normalization techniques, determine the positive and negative features, and outline the selection process of normalization techniques suitable for different data structures. The purpose of the study is to provide different perspectives on normalization techniques and a holistic framework for researchers and decision makers
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