@revista_internacional{722,
  keywords = {"Minimum adjustment consensus, Large scale group decision making, Reliability degree, Two-dimension 2-tuple linguistic expression"},
  author = {Zelin Wang and Rosa María Rodríguez and Y.M. Wang and Luis Martínez},
  title = {A two-stage minimum adjustment consensus model for large scale decision making based on reliability modeled by two-dimension 2-tuple linguistic information},
  abstract = {"Consensus reaching processes (CRPs) have been required to assure the consensus in large scale group decision making (LSGDM). Opinion reliability detection has been demanded to ensure the trustworthiness of the original information and different information modeling approaches have facilitated it in which two dimensional linguistic (TDL) information has an outstanding place. The reliability degree of original opinions elicited by TDL expressions is often given in advance as subjective evaluation, and after adjustment during CRP, the reliability of the adjusted opinions is often neglected especially for automatic CRP, which may lead to unreliable decisions. In real decision making, considering the interest of decision makers (DMs) themselves, the self-assessment of the DMs on the reliability of the given opinions could be easily manipulated by DMs. To reduce the subjectivity of the decision making, we propose a method to obtain objectively the reliability of the adjusted opinions through a two-stage minimum cost consensus model based on 2-tuple linguistic additive preference relations. Firstly, a support degree (SD)-based clustering method will be developed for classifying DMs into several subgroups to make more manageable the large number of DMs. Subsequently, a two-stage minimum adjustment consensus model will be presented to improve the consensus level (CL) gradually. Eventually, the adjusted opinions will be presented as two-dimension 2-tuple linguistic (TD2L) information. A comparative performance analysis of this CRP based LSGDM approach will be provided to show its effectiveness."},
  year = {2021},
  journal = {Computers & Industrial Engineering},
  volume = {151},
  pages = {106973},
  publisher = {Elsevier},
  issn = {0360-8352},
  url = {http://www.sciencedirect.com/science/article/pii/S0360835220306458},
  doi = {https://doi.org/10.1016/j.cie.2020.106973},
}