Overcoming the Limitations of the RAPS Method by identifying Alternative Data Normalization Methods

Abstract

This study proposes a new approach to improve the performance of the Ranking Alternatives by Perimeter Similarity (RAPS) method in Multi-Criteria Decision-Making (MCDM). RAPS has attracted attention but encounters difficulties when handling zero values in the decision matrix. This study suggests using alternative data normalization methods and assesses their suitability when combined with RAPS in various scenarios. The results identified three additional normalization methods that are appropriate for integration with RAPS. These findings provide a theoretical basis and specific guidelines for selecting data normalization methods when applying RAPS in MCDM.