Service supplier selection under fuzzy and stochastic uncertain environments

Abstract

Owing to the heterogeneity and inherent uncertainty of services, the selection of service suppliers is a complicated multi-attribute group decision-making (MAGDM) problem in which fuzzy criteria and stochastic criteria coexist. During the past few decades, many real-world supplier selection problems have been resolved using MAGDM methods. Nevertheless, extant research on supplier selection considers either fuzzy criteria or stochastic criteria, and hence most of these methods cannot address the complex and unstructured nature of contemporary service supplier selection problems. In this study, a novel technique for order preference by similarity to the ideal solution (TOPSIS) approach, integrating both fuzzy criteria and stochastic criteria, is developed; in this approach, the interval-valued intuitionistic fuzzy (IVIF) cross-entropy for fuzzy criteria and the Euclidean distance for stochastic criteria are used to acquire the rankings of alternatives. Moreover, a sensitivity analysis is conducted for a case study of hoisting service supplier selection, and a comparative analysis with other existing methods is performed to confirm the effectiveness and efficiency of the proposed approach.