## 1 Introduction
In addition to optimization-based approaches, models based on machine tool selection criteria are also widely used. Effective machine tool selection must compromise, therefore, between conflicting tangible and intangible factors. To address this, multi-criteria decision making (MCDM) has proven useful. [PERSON] et al. developed a multi-criteria group decision procedure based on the fuzzy VIKOR method to solve a machine tool selection problem [18]. They used six main criteria (spindle speed, bar capacity, horsepower, capital cost, flexibility, and turning meter) that can affect the ability of CNC machines to perform the required production operations. The selection of the best machine tool from the increasing number of alternatives available on the market is a multi-criteria decision-making problem in the presence of many quantitative and qualitative characteristics. The selection of the best machine tool from the increasing number of alternatives existing in the market was explored by [PERSON] and [PERSON] [19]. They applied an intelligent approach by merging both techniques--fuzzy logic and AHP--to evaluate the procurement cost of each alternative. [PERSON] et al. proposed a machine tool selection method based on a hybrid multi-criteria decision model [20]. They combined two techniques, fuzzy logic and VIKOR method, and pointed out that the best and worst selected machine tools have high agreement with the actual ranking in the real factory. [PERSON] developed the VIKOR method based on interval target values of attributes and an interval decision matrix, and presented a novel normalization technique using the interval distance of interval number [21]. Then, they used interval distance as an improved formula compared to the interval distance equation available in the literature. [PERSON] et al. developed a model based on an analytical hierarchy process to support the selection of appropriate machine configurations for special purpose machines from the available alternatives [22]. They obtained different scenarios from the evaluation process of the developed model and showed the influence of changing the relevant importance of the elements in the hierarchy on the selection of special purpose machine configurations. [PERSON] et al. [23] proposed to combine decision making in machine tool selection with a combination of stepwise weight evaluation ratio analysis (SWARA) and proportional evaluation of alternatives with grey relationships (COPRAS -G). [PERSON] and [PERSON] combined Fuzzy Linear Regression and Quality Function Deployment Zero-One Goal Programming to obtain the decision results of machine tool selection [24]. The first part of their research refers to the determination of the weight of each criterion by SWARA and the evaluation of alternatives by COPRAS -G to obtain a ranked list of machines. Previous research has been based mainly on the use of MCDM techniques in selecting similar machine tools based on cost and machine performance. On the other hand, very little research has addressed the development of decision-making systems that incorporate different machine tools performing the same operation. Decision making in the analysis of different cutting methods requires a lot of information from the point of view of technological parameters. For example, [PERSON] [25] developed a fuzzy AHP method for choosing between plasma and laser cutting, in which the decision criteria are determined based on expert opinions. In his comparison, laser cutting technology was more important than plasma cutting. [PERSON] and [PERSON] used the TOPSIS method for simultaneous optimization of seven different non-conventional machining processes [26]. Similarly, [PERSON] and others conducted a study providing different system approaches in fuzzy environments for solving the problem of selecting the appropriate type of machining process using a fuzzy-based decision model [27].