Developing a nondiscretionary slacks-based measure model for supplier selection in the presence of stochastic data

Supplier selection has a strategic importance for every company. Nondiscretionary Slacks-based Measure (SBM) model is one of the models in Data Envelopment Analysis (DEA). In many real world applications, data are often stochastic. A successful approach to the address uncertainty in data is to replace deterministic data via random variables, leading to Chance-con strained DEA (CCDEA). In this study, the concept of chance-constrained programming approach is used to develop nondiscretionary SBM model in the presence of stochastic data and also its deterministic equivalent which is a nonlinear program is derived. Furthermore, it is shown that the deterministic equivalent of the stochastic nondiscretionary SBM model can be converted into a quadratic program. Finally, a numerical example demonstrates the application of the proposed model.