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 $\phi$ 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.

机床选择一直是制造业中的重要问题，因为不正确的机床选择可能会对公司的生产率，准确性，灵活性和响应性制造能力产生负面影响。当前机床选择的多标准决策（MCDM）方法主要集中在主观角度。但是，由于客观评估代表了机床的实际性能，因此在选择合适的加工工具时既要考虑主观观点又要考虑客观观点。因此，本研究提出了一种基于新型混合MCDM模型的机床选择方法。首先，该方法采用了综合权重技术，该技术综合了使用模糊决策试验和评估实验室（FDEMATEL）获得的主观权重和使用熵加权（EW）获得的客观权重。其次，提出了以后的去模糊化VIKOR（LDVIKOR）来对可选方案进行排名。最后，通过案例验证了所提方法的有效性。评估结果表明，该方法选择的最佳和最差机床与实际工厂中的实际排名保持高度一致性。此外，参数影响的敏感性分析结果 案例应用验证了所提方法的有效性。评估结果表明，该方法选择的最佳和最差机床与实际工厂中的实际排名保持高度一致性。此外，参数影响的敏感性分析结果 案例应用验证了所提方法的有效性。评估结果表明，该方法选择的最佳和最差机床与实际工厂中的实际排名保持高度一致性。此外，参数影响的敏感性分析结果$\phi$决策结果上的结果表明，无论此参数如何变化，最佳决策结果都不会受到影响。这些表明，提出的混合模型在为决策者的偏好赋予灵活性方面具有优势。