Developing a WPF‐CCR model for selecting suppliers in the presence of stochastic data

In recent years, determining an appropriate supplier has become a crucial strategic consideration in a competitive market – with data envelopment analysis (DEA) methods increasingly important in this respect. DEA traditionally requires that the values for all inputs and outputs be known exactly. However, this assumption may not be true, because data in many real applications cannot be precisely measured. A successful approach for addressing uncertainty in data is to replace deterministic data with random variables, leading to chance‐constrained DEA. In this article, the concept of chance‐constrained programming is used to develop a Worst‐practice frontier‐Charnes‐Cooper‐Rhodes model and also its deterministic equivalent. Furthermore, it is shown that the latter can be formulated as a quadratic program. Finally, a numerical example demonstrates the application of the proposed model.