Selecting Normalization Techniques for the Analytical Hierarchy Process

One of the matters which has influence on Multi-Criteria Decision Making (MCDM) methods is the normalizing procedure. Most MCDM methods implement normalization techniques to produce dimensionless data in order to aggregate/rank alternatives. Using different normalization techniques may lead to different rankings. So, selecting a more suitable normalization technique is a requirement in the decision process. Specially, by the advent of big data and its role in developing life’s quality, finding the best normalization technique in MCDM models are more challenging. Collecting data from sensors causes more complex decision problems, thus, providing accurate normalized values (in the same unit) is more critical in these types of contexts. In this research, we analyze and evaluate the effect of different normalization techniques on the ranking of alternatives in one of the Multi-Criteria Decision Making (MCDM) methods called Analytical Hierarchy Process (AHP) using our developed assessment framework. An illustrative example (smart car parking) is used to discuss the suitability of the framework and recommend more proper normalization technique for AHP. Furthermore, the developing of technological innovation is expected by using the evaluation framework which can raise the accuracy of the normalized values in decision problems.