Novel Decision Modeling for Manufacturing Sustainability under Single-Valued Neutrosophic Hesitant Fuzzy Rough Aggregation Information

We developed a multicriteria decision-making method based on the list of novel single-valued neutrosophic hesitant fuzzy rough (SV-NHFR) weighted averaging and geometric aggregation operators to address uncertainty achieve sustainability manufacturing business. In addition, case study choosing optimum elements for sustainable sector was carried out. The proposed decision support is then compared other relevant methodologies, validity test performed show reliability new methodology. Sustainability one most important issues world economy facing today. Several industrial businesses have incurred large financial losses as result their ignorance issues. Manufacturers in industrialized countries done decent job making sure that are over long run. Modern companies use lot modern technologies. These include blockchain, artificial intelligence (AI), Internet Things (IoT), big data analytics (BDA), logic (fuzziness). technologies continuation life, either directly or indirectly. Therefore, it utmost importance concentrate those encourage adoption sustainability. goal this provide framework using cutting-edge technology increase firms. Under guidance aggregate information, advised place strong emphasis addressing sustainability, waste management, environmental protection, cost savings, chemicals resources. results suggest technique can solve inadequacy existing by SV-NHFR terms adaptability.