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In this paper, we use a technique for order preference called TOPSIS, to determine the ranking of importance of certain subfactors of causal factors of political stability. We introduce the use of intuitionistc fuzzy sets in the analysis.
We propose an approach for multi-attribute group decision-making (MAGDM) problems under neutrosophic information, where the preference values of alternatives over the attributes and the importance of attributes are expressed in terms of single-valued neutrosophic sets. Firstly, we develop a nonlinear programming approach based on Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method to determine relative closeness intervals of alternatives. Secondly, we aggregate closeness intervals to find out the ranking order of all alternatives by computing their optimal membership degrees based on the ranking method of interval numbers. Finally, we provide an illustrative example to show the effectiveness of the proposed approach.
Production in small and medium enterprises (SMEs) makes a substantial contribution to the Gross Domestic Product directly and indirectly in developing economies including India. In the present time, applying Industry 4.0 to the SMEs will build a smart manufacturing system that will prove to be economically feasible as well as socially sustainable. The purpose of this study is to identify and prioritize major barriers of implementing Industry 4.0 in Indian SMEs. A questionnaire with 12 barriers which were identified based on the literature survey and expert discussion was made to be filled by industry experts of production, information technology, business and members of the top management in SMEs. Further, Multi-Criteria Decision Making (MCDM) methods like TOPSIS, VIKOR and PROMETHEE are used to find the rank for each barrier. The study reveals that the major implementation barriers of Industry4.0 in Indian SMEs are fear of unemployment, lack of IT training, poor IT infrastructure, etc. The ranking for each barrier will not only help to assess risks in manufacturing, supply chain or business initiative, but also to help the managers in devising risk mitigation plans. This study may be used by firms working under the manufacturing sector.