Supplier Selection by the New AR-IDEA Model

Abstract

Traditionally, supplier-selection models have been based on cardinal data with less emphasis on ordinal data. However, with the widespread use of manufacturing philosophies such as just-in-time (JIT), emphasis has shifted to the simultaneous consideration of cardinal and ordinal data in the supplier-selection process. The application of data envelopment analysis (DEA) for supplier-selection problems is based on total flexibility of the weights. However, the problem of allowing total flexibility of the weights is that the values of the weights obtained by solving the unrestricted DEA program are often in contradiction to prior views or additional available information. The objective of this paper is to propose a new pair of assurance region-imprecise data envelopment analysis (AR-IDEA) model for selecting the best suppliers in the presence of both weight restrictions and imprecise data. A numerical example demonstrates the application of the proposed method.