@revista_internacional{1043,
  keywords = {group decision-making, incomplete probabilistic linguistic preference relation, consistent, Consensus reaching process, multiple scenario model},
  author = {Ran Dang and Peide Liu and Peng Wang and Luis Martínez},
  title = {Consistency- and Ordinal Consensus- based Multiple Scenario Model for Group Decision-making with Incomplete Probabilistic Linguistic Preference Relations},
  abstract = {Incomplete probabilistic linguistic preference relations (InPLPRs), as a practical expression to portray the uncertainty of things, can describe the information on decision makers’ (DMs’) evaluation of things in pairwise comparisons in group decision making with two dimensions simultaneously. This paper investigates a group decision-making (GDM) method with InPLPRs to express the preference information of DMs and establish a consensus reaching mechanism for multiple scenarios. First, to obtain incomplete information, we define the concept of opinion swing neighborhood based on referring to the opinions of others and analyzing the mental behavior of DMs. Furthermore, a missing information estimation model of InPLPRs considering the opinion swing neighborhood is developed to obtain consistent optimal estimates. Secondly, the ordinal consensus index is defined, facilitating a more accurate measure of consensus attainment. Then, we thoroughly explore the classification of decision situations using consistency, consensus, illogical rate, and distinguishing index. Based on this, the corresponding consensus optimization models are proposed for different decision scenarios. Subsequently, the DMs’ weights are presented for aggregating individual opinions with acceptable consistency and consensus into group opinions, and the collective opinions are ranked and selected. Finally, numerical examples, simulation experiments, and comparative analysis demonstrate the proposed method’s applicability, effectiveness, and advantages. The proposed method offers a comprehensive GDM approach based on InPLPRs, which can be applied to various real-world GDM scenarios, demonstrating broad applicability.},
  year = {0},
  journal = {Applied Soft Computing},
  pages = {113722},
  issn = {1568-4946},
  url = {https://www.sciencedirect.com/science/article/pii/S1568494625010336},
  doi = {https://doi.org/10.1016/j.asoc.2025.113722},
}