Consideration of disruptive technologies and supply chain sustainability through α-discounting AHP–VIKOR: calibration, validation, analysis, and methods

Faculty Computer Science Year: 2023
Type of Publication: ZU Hosted Pages:
Authors:
Journal: Soft Computing Springer Nature Volume:
Keywords : Consideration , disruptive technologies , supply chain sustainability    
Abstract:
Supply chain (SC) networks now need to submit structural adjustments to respond to events like natural and unanticipated disasters that tend to cause interruptions and damage the continuity of business operations. Artificial intelligence, robotics, blockchain, 3D printing, 5G, and the Internet of Things are just a few examples of disruptive technologies that are being widely used and will drastically alter how SCs sustainability is conducted in the real world. The sustainability of SC operations can be greatly enhanced by disruptive technology. Organizational competency is needed to strike a balance between sustainability and SC disruption. Sustainable SCs may not have enough resilience to survive disruptive events since they prioritize efficiency and economic aspects. That is why sustainable SC management studies trends toward disruption utilizing new technologies. To adapt SCs to the needs of contemporary manufacturing processes, innovations in environmental conservation and social development have been put into place. In contrast, even though the fact that sustainability and disruptive technologies have each been extensively examined separately, few suggestions combine them in the measuring of SC performance. Since there are not any studies in the literature that identify the influence of disruptive technologies on SC sustainability generally, in this research we concentrate on several disruptive technologies that are used to provide SC sustainability. The objective of this work's suggestion is to provide a framework that integrates multi-criteria analysis methods with neutrosophic theory to evaluate the influence of disruptive technologies on the sustainability of SCs. This framework is an integration of the α-discounting technique for multi-criteria decision-making (α-D MCDM) and the Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) method. The new α-D MCDM approach overcomes the analytical hierarchy process's (AHP) method limitations of pairwise comparison to n-wise comparisons and also deals with inconsistent problems. SC sustainability has three main criteria which are economic, environmental, and social, their weights are measured using α-D MCDM with n-wise comparison. Also, the twenty sub-criteria weights were evaluated using the standard AHP method with consistent comparison. To rank the disruptive technologies that influence SC sustainability, we utilize the VIKOR method. The proposed framework is applied under a neutrosophic environment to ensure more accurate results and deal with uncertainty and vague conditions. Finally, sensitivity and comparative analyses were performed to confirm the verity, strength, and stability of the enhanced methodology.
   
     
 
       

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