Robust-fuzzy model for supplier selection under uncertainty: An application to the automobile industry

This paper proposes an innovative robust-fuzzy method for multiobjective, multiperiod supplier selection problem under multiple uncertainties. This approach integrates robust optimization and fuzzy programming. Uncertain parameters are modeled as random variables that take value within a symmetrical interval. However, due to the complexity or ambiguity of some real world problems and specially the nature of some of the available input data, the length of interval is also highly uncertain. This ambiguity motivated us to present a new approach, which can be applicable to multiple uncertainties conditions. Thus, in our approach the halflength of these intervals is also represented by fuzzy membership function. We develop a model and a solution approach to select suppliers by considering risk. The proposed method is applied to a real case of supplier selection in automobile industry under uncertainty and ambiguity conditions. To verify the proposed model, we evaluated the results by simulation technique and compared values of objective function under different scenarios.