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A multi-attribute group decision model based on unbalanced and multi-granular linguistic information: An application to assess entrepreneurial competencies in secondary schools

  • Olga Porro*
  • , N. Agell
  • , Mónica Sánchez
  • , Francisco Javier Ruiz
  • *Corresponding author for this work

Research output: Indexed journal article Articlepeer-review

18 Citations (Scopus)

Abstract

Advances in multi-attribute group decision making require the development of structures flexible enough to deal with unbalanced and multi-granular linguistic information. New distances between linguistic terms are needed to aggregate opinions and measure consensus among decision makers with different profiles. In this paper, firstly, based on the lattice structure of hesitant fuzzy linguistic terms sets, a perceptual-based distance able to capture differences between unbalanced linguistic assessments is developed. Secondly, a projected algebraic structure is defined to deal with multi-perceptual group decision-making contexts where each decision maker has its own qualitative reasoning approach. To this end, a methodology to aggregate unbalanced linguistic information based on different perceptual maps is developed. This methodology can also deal with different multi-granularity linguistic environments. Finally, through an illustrative example based on real data provided by the Andorra Government in a pilot test, the proposed framework is applied to classify and rank a set of secondary students according to their degree of entrepreneurial competency.

Original languageEnglish
Article number107662
Pages (from-to)1-17
Number of pages17
JournalApplied Soft Computing Journal
Volume111
DOIs
Publication statusPublished - Nov 2021

Keywords

  • Consensus measures
  • Hesitant fuzzy linguistic term sets
  • Linguistic modelling
  • Multiple-attribute group decision-making
  • Unbalanced linguistic term sets

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