An approach of TOPSIS technique for developing supplier selection with group decision making under type-2 neutrosophic number

Faculty Computer Science Year: 2019
Type of Publication: ZU Hosted Pages: Pages 438-452
Authors:
Mohammed Abdel Basset Metwally Attia
Journal: Applied Soft Computing Elsevier B.V. Volume: Volume 77
Keywords : , approach , TOPSIS technique , developing supplier selection    
Abstract:
This paper proposes an advanced type of neutrosophic technique, called type 2 neutrosophic numbers, and defines some of its operational rules. The type 2 neutrosophic number weighted averaging operator is determined in order to collective t
    
     
 
       

Author Related Publications

  • Mohammed Abdel Basset Metwally Attia, "Discrete greedy flower pollination algorithm for spherical traveling salesman problem", Springer, 2019 More
  • Mohammed Abdel Basset Metwally Attia, "A New Hybrid Flower Pollination Algorithm for Solving Constrained Global Optimization Problems", Natural Sciences Publishing Cor., 2014 More
  • Mohammed Abdel Basset Metwally Attia, "A novel equilibrium optimization algorithm for multi-thresholding image segmentation problems", Springer London, 2021 More
  • Mohammed Abdel Basset Metwally Attia, "An efficient binary slime mould algorithm integrated with a novel attacking-feeding strategy for feature selection", Pergamon, 2021 More
  • Mohammed Abdel Basset Metwally Attia, "An efficient teaching-learning-based optimization algorithm for parameters identification of photovoltaic models: Analysis and validations", Pergamon, 2021 More

Department Related Publications

  • Ibrahiem Mahmoud Mohamed Elhenawy, "BERT-CNN: A Deep Learning Model for Detecting Emotions from Text", Tech Science Press, 2021 More
  • Ahmed Raafat Abass Mohamed Saliem, "BERT-CNN: A Deep Learning Model for Detecting Emotions from Text", Tech Science Press, 2021 More
  • Ahmed Raafat Abass Mohamed Saliem, "Using General Regression with Local Tuning for Learning Mixture Models from Incomplete Data Sets", ScienceDirect, 2010 More
  • Ahmed Raafat Abass Mohamed Saliem, "On determining efficient finite mixture models with compact and essential components for clustering data", ScienceDirect, 2013 More
  • Ahmed Raafat Abass Mohamed Saliem, "Unsupervised learning of mixture models based on swarm intelligence and neural networks with optimal completion using incomplete data", ScienceDirect, 2012 More
Tweet