Original scientific paper

https://doi.org/10.17559/TV-20260816003808

Comparative Study of SREM and RAM Methods for Machine Tool Selection

Do Duc Trung ; School of Mechanical and Automotive Engineering, Hanoi University of Industry, Hanoi 100000, Viet Nam
Branislav Dudic ; 1) Comenius University Bratislava, Faculty of Management, Odbojárov 10. P. O. BOX 95, 820 18 Bratislava 218, Slovak Republic E-mail: branislav.dudic@fm.uniba.sk 2) University Business Academy in Novi Sad, Faculty of Economics and Engineering Management in Novi Sad, Cvećarska 2, 21107 Novi Sad, Serbia *
Tran Van Dua ; School of Mechanical and Automotive Engineering, Hanoi University of Industry, Hanoi 100000, Viet Nam
Branko Strbac ; University of Novi Sad, Faculty of Technical Sciences, Department of Production Engineering, Trg Dositeja Obradovića 6, Novi Sad, Serbia
Sara Havrlisan orcid.org/0009-0009-4806-3346 ; University of Slavonski Brod, Mechanical Engineering Faculty in Slavonski Brod, 108. brigade ZNG 36, 35000 Slavonski Brod, Croatia *
Aleksandar Asonja ; University Business Academy in Novi Sad, Faculty of Economics and Engineering Management in Novi Sad, Cvećarska 2, 21107 Novi Sad, Serbia; The Academy of Applied Studies Polytechnic, Katarine Ambrozić 3, 11000 Belgrade, Serbia

* Corresponding author.

Abstract

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.

Keywords

Machine tool selection; MCDM; RAM method; sensitivity analysis; Spearman correlation coefficient; SREM method

Hrčak ID:

350410

URI

https://hrcak.srce.hr/350410

Publication date:

31.8.2026.

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