Decision making with extended fuzzy linguistic computing, with applications to new product development and survey analysis

Fuzzy set theory, with its ability to capture and process uncertainties and vagueness inherent in subjective human reasoning, has been under continuous development since its introduction in the 1960s. Recently, the 2-tuple fuzzy linguistic computing has been proposed as a methodology to aggregate fuzzy opinions (Herrera and Martinez, 2000a and Herrera and Martinez, 2000b), for example, in the evaluation of new product development performance (Wang, 2009) and in customer satisfactory level survey analysis (Lin & Lee, 2009). The 2-tuple fuzzy linguistic approach has the advantage of avoiding information loss that can potentially occur when combining opinions of experts. Given the fuzzy ratings of the evaluators, the computation procedure used in both Wang, 2009 and Lin and Lee, 2009 returned a single crisp value as an output, representing the average judgment of those evaluators. In this article, we take an alternative view that the result of aggregating fuzzy ratings should be fuzzy itself, and therefore we further develop the 2-tuple fuzzy linguistic methodology so that its output is a fuzzy number describing the aggregation of opinions. We demonstrate the utility of the extended fuzzy linguistic computing methodology by applying it to two data sets: (i) the evaluation of a new product idea in a Taiwanese electronics manufacturing firm and (ii) the evaluation of the investment benefit of a proposed facility site.

► The 2-tuple fuzzy linguistic computing was previously proposed for aggregating fuzzy opinions. ► In the original method, the result of aggregation is a crisp number. ► We extend the 2-tuple fuzzy linguistic method such that the result of aggregation is a fuzzy number. ► We describe a computationally efficient process to implement the extended method. ► Finally, we demonstrate the extended method by applying it to two previously published data sets.