R-RAM: A Novel Hybrid Model for Option Ranking - Trung, Do Duc; Duc, Duong Van; Bao, Nguyen Chi; Son, Nguyen Hoai; Thuy, Duong Thi Thanh; Nga, Nong Thi Thanh (Yugoslav Journal of Operations Research; Online firstDO - 10.2298/YJOR240615009T, 2025)
Ranking alternatives considering multiple criteria is a complex task, requiring the selection of both a weight calculation method and a ranking method. This study proposes a hybrid method, R-RAM, combining the strengths of the R and RAM methods. R-RAM calculates criteria weights considering both subjective and objective factors and then ranks alternatives using the RAM method. The performance of R-RAM was evaluated in four case studies: ranking metal cutting experiments, electric bicycles, automotive protective materials, and 3D printers. Results showed that R-RAM consistently performed comparably to existing MCDM methods and outperformed the R method. The average Spearman correlation coefficients between R-RAM and other methods were significantly higher in all cases (0.8833, 0.9167, 1, and 0.9429), compared to those of R (0.6214, 0.8839, 0.7, and 0.7714). Sensitivity analysis demonstrated the stability of rankings produced by R-RAM under various weight scenarios. Using R-RAM eliminates the need for users to spend time and effort thinking about which weighting method or solution ranking method to choose, as R-RAM operates by using R to calculate weights for criteria and RAM to rank the alternatives. The introduction of RRAM is a noteworthy contribution to the field of option ranking.