A novel hybrid MCDM model for machine tool selection using fuzzy DEMATEL, entropy weighting and later defuzzification VIKOR

Abstract

Machine tool selection has been an important issue in the manufacturing industry because improper machine tool selection can have a negative effect on productivity, accuracy, flexibility, and the responsive manufacturing capabilities of a company. The current multi-criteria decision making (MCDM) approach of machine tool selection mostly focuses on the subjective perspective. However, as the objective evaluation represents the actual performance of machine tools, both subjective and objective perspectives need to be considered when choosing an appropriate machining tool. Therefore, this study proposes a machine tool selection method based on a novel hybrid MCDM model. Firstly, the presented method employs a comprehensive weight technique integrating subjective weights obtained using fuzzy decision-making trial and evaluation laboratory (FDEMATEL) with objective weights obtained using entropy weighting (EW). Secondly, later defuzzification VIKOR (LDVIKOR) is put forward to rank the optional alternatives. Finally, a case application verifies the effectiveness of the proposed method. The evaluation results indicate that the best and worst selected machine tool of the proposed method keeps high conformance with the actual ranking in real factory. Additionally, sensitivity analysis results of the effect of parameters φ on the decision outcome show that irrespective of the variations in this parameter, the best decision outcome will be not influenced. These indicate that the presented hybrid model has advantages in granting flexibility to the preferences of decision makers.