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The high-speed train (HST) system is one of the most critical components of national and international passenger transportation networks. Selecting the appropriate train sets is a critical task for railway operators to build an efficient, productive, safe, inexpensive and environmentally friendly passenger transport network system. On the other hand, selecting the proper HST set is a highly complex process since many conflicting criteria, and decision alternatives make it difficult for decision-makers. This paper suggests the fuzzy Grey Relational Analysis technique. In addition, the fuzzy technique proposed in the current paper has been implemented in two ways: using both the experts' linguistic evaluations and crisp numbers to compare real numerical values and fuzzy evaluations. A comprehensive sensitivity analysis was then conducted to assess the validation of the proposed fuzzy technique and its results in applying this method. The decision alternative of A8 Siemens is the best option for all scenarios, and it has been observed that there are slight differences, which cannot change the overall result in the ranking positions of the options. The analysis results prove that the fuzzy method can be applied to solve these complicated decision-making problems and that the obtained results are robust, accurate, applicable, and realistic. [ABSTRACT FROM AUTHOR]

In recent years, there have been dramatic changes in manufacturing systems in many industries depending on technological developments. Robotics is one of the essential components of these changes. Today, the usage of robotics in manufacturing processes has become widespread in almost all industries. Also, it has become a very strong desire ever-increasing for even small and medium-sized enterprises at present. Almost all the previous studies emphasized that industrial robot selection is a highly complex decision-making problem as there are many conflicting factors and criteria. Besides, different and advanced specifications of these robotics added by robotic manufacturers have caused to increase the complexities much more. Hence, decision-makers encounter more complicated decision-making problems affected by many uncertainties. Because of that, an integrated fuzzy group MCDM framework can help overcome many ambiguities proposed in the current paper. The proposed fuzzy integrated model consists of the fuzzy SWARA (F-SWARA'B) and the fuzzy CoCoSo (F-CoCoSo'B), which are extended with the help of the Bonferroni function. The model selected the appropriate industrial robotics used in the automotive industry by considering 15 criteria and ten alternatives. According to the result of the study, the three most significant criteria have been determined: Working Accuracy, Reaching Distance, and Performance; and the most suitable option is the A8. The obtained results were validated with the help of a comprehensive sensitivity analysis consisting of different 150 scenarios. The results are also compared with some existing techniques. The sensitivity analysis results approve the validity and applicability of the proposed model. [ABSTRACT FROM AUTHOR]