Analysis of a Robot Selection Problem Using Two Newly Developed Hybrid MCDM Models of TOPSIS-ARAS and COPRAS-ARAS

Traditional Multi-Criteria Decision Making (MCDM) methods have now become outdated; therefore, most researchers are focusing on more robust hybrid MCDM models that combine two or techniques to address decision-making problems. The authors attempted create novel systems in this paper by integrating Additive Ratio ASsessment (ARAS) with Technique for Order of Preference Similarity Ideal Solution (TOPSIS) and Complex PRoportional (COPRAS). To demonstrate the ability effectiveness these i.e., TOPSIS-ARAS COPRAS-ARAS were applied solve a real-time robot selection problem 12 alternative robots five criteria, while evaluating parametric importance using CRiteria Importance Through Inter criteria Correlation (CRITIC) objective weighting estimation tool. rankings alternatives gained from also compared obtained results eight other solo tools. Although slightly differ each other, final outcomes all adopted consistent enough suggest is best choice followed 11, 4 worst one among alternatives. Spearman Coefficient (SCC) reveals proposed derived various strong ranking relationship another. Finally, sensitivity analysis was performed investigate effects weight variation validate robustness implemented approaches.