Evaluating the performance of robust and stochastic programming approaches in a supply chain network design problem under uncertainty

Today, organisations have focused on improving their supply chain performance to achieve sustainable profit and proceed in volatile markets. The nature of today's volatile markets imposes parametric uncertainty to optimisation problems particularly in strategic decision making problems such as supply chain network design (SCND) problem. Two-stage stochastic programming (TSSP) and robust stochastic programming (RSP) approaches are widely used to deal with the uncertainty of optimisation problems. In this paper, the performance of these two approaches in a SCND problem is evaluated through conducting a case study in Iran and performing realisation process. The main objectives of this study are optimising three stage SCND problems under uncertainty and evaluating the performance of TSSP and RSP methods in optimising SCND problem under uncertainty. The results show that the RSP method leads to more robust solution than TSSP method. Also, the RSP method has more degree of flexibility to deal with the uncertainty according to DM preferences.