Linguistic multi-criteria decision-making model with output variable expressive richness


In general, traditional decision-making models are based on methods that perform calculations on quantitative measures. These methods are usually applied to assess possible solutions to a problem, resulting in a ranking of alternatives. However, when it comes to making decisions about qualitative measures –such as service quality–, the quantitative assessment is a bit difficult to interpret. Therefore, taking into account the maturity of the linguistic assessment models, this paper puts forth a new solution proposal. It is a decision-making model that uses linguistic labels –represented with the 2-tuple notation– and a variable expressive richness when providing output results. This solution allows expressing results in a manner closer to the human cognitive system. To achieve this goal, a mechanism has been implemented for measuring the distance among the aggregate ratings, providing the decision-maker with a fast and intuitive answer. The proposal is illustrated with an application example based on the TOPSIS model, using linguistic labels throughout the entire process.


The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.


multi-criteria decision making, linguistic labels, variable expressive richness, 2-tuple representation, linguistic TOPSIS model


Cid-Lopez, A. et al. (2017) Linguistic multi-criteria decision-making model with output variable expressive richness. Expert Systems with Applications. 83, pp. 350-362


Research Institute

Institute of Artificial Intelligence (IAI)