Comparative Study of SREM and RAM Methods for Machine Tool Selection

This study performs a comparative evaluation of two multi-criteria decision-making (MCDM) approaches, the Square-Root based Evaluation Method (SREM) and the Root Assessment Method (RAM) for machine tool selection, specifically wood planers and grinding machines. The performance effectiveness and ranking stability of both methods were evaluated using Spearman's rank correlation coefficient (S) against ten widely used contemporary MCDM techniques: TOPSIS, SAW, MARCOS, ROV, FUCA, PIV, VIKOR, COCOSO, CRADIS, and EDAS. Experimental results and sensitivity analyses under different weight distributions indicate that SREM's capacity to maintain ranking stability relies on the adjustment coefficient k, which balances square-root and squared transformations. When k equals 0 (considering only the square-root transformation), SREM achieves very high ranking consensus, fully comparable to RAM and the other MCDM techniques (with mean S ranging from 0.9143 to 0.9580). However, increasing coefficient k (which raises the weight of the squared transformation) lowers SREM's ranking agreement, as the squared transformation exaggerates disparities among criteria containing extreme or outlier values, resulting in severe over-penalization or over-rewarding of specific alternatives. Although both SREM and RAM were verified as suitable for machine tool selection in the tested scenarios, a detailed assessment reveals that RAM proves superior to SREM in ensuring overall ranking consensus and stability for machine tool ranking.