A novel fuzzy data envelopment analysis model with double frontiers for supplier selection

Abstract

Selecting the best suppliers is a significant yet complex decision which requires careful consideration of various performance criteria. Hence, supplier selection is an important issue and has a strategic significance for every company. One of the techniques that can be used for selecting suppliers is data envelopment analysis (DEA). In this paper, to handle ambiguity and fuzziness in supplier selection problems, a novel fuzzy DEA approach with double frontiers for selecting suppliers is developed. Compared with the traditional DEA, the DEA approach with double frontiers can identify the best supplier appropriately and easily without the need to impose any weight restriction or the need to calculate the cross-efficiency matrix, which requires a large number of computations and may also result in inconsistent conclusions. A numerical example demonstrates the application of the proposed model.

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