The Significance of Weighting in Multicriteria Decision-Making Methods: A Case Study on Robot Selection

Abstract

Multi-Criteria Decision-making (MCDM) is employed in many fields of engineering decision-making including robot selection challenges. Researchers are very interested in using the MCDM technique for robot selection difficulties. The current study employs four common MCDM methods: COCOSO, TOPSIS, VIKOR, and MOORA are utilized to determine the better robot choices. Furthermore, in MCDM, weight allocation is critical in selecting the optimal choices. Thus, five distinct objective weight allocation approaches are utilized to fix a real-time robot selection issue with five selection criteria and seven alternative robots: Entropy method (EM), Mean weight method (MW), Criteria Importance Through Inter Criteria Correlation (CRITIC), Standard deviation method (SD), and Analytic Hierarchy Process (AHP). The primary goal of this work is to compare the relative performance of the 20 most well-known combinations (four MCDM approaches and five alternative weight allocation methods) in terms of observed ranks. The ranks produced from the 20 permutations are not uniform, which must be considered. In this study, the Rank Average (Mean) approach is utilized to aggregate the 20 collected rankings into a single composite rank, which is then compared to the rankings produced by the other 20 permutations. The performance of the MOORA combination techniques (MOORA-MW, MOORA-CRITIC, and MOORA-AHP) is adequate for handling selection difficulties. It is impractical to expect a freshly designed MCDM, such as a COCOSO, to outperform well-tested -and-true approaches such as TOPSIS, VIKOR, and MOORA. Decision mistakes may occur if just one MCDM-TOOL (MCDM method - WEIGHT method) is used to pick the options.