Enhanced assessment of a supplier selection problem by integration of soft sets and hesitant fuzzy linguistic term set

Abstract

Selecting the most appropriate supplier is a key issue in supply chain management and is linked to the success of the entire supply chain. Supplier selection is a multiple-criteria decision-making problem that has qualitative and quantitative factors. The traditional method by which this is calculated adopts a precise value or single linguistic terms to represent attribute data. However, real-life situations have many uncertainties and imprecise or missing data with regard to supplier selection. Moreover, experts equivocate between several values in assessing attribute data in real-world situations. These factors increase the difficulty selecting suppliers, causing decision-makers to make incorrect choices. To solve this issue, we present an integrated approach, using a soft set and hesitant fuzzy linguistic term set, for selecting the appropriate supplier in the supply chain. A practical example of liquid crystal display module supplier selection is presented to illustrate the proposed approach, the results of which are compared with those of the arithmetic average method and hesitant fuzzy linguistic term set method. The proposed approach effectively solves the problems of incomplete attribute data and expert hesitation in assessing attribute data.