Supply chain configuration based on dynamic pricing and robust optimization

The complexity of the production systems and increasing competition in the business environment has made uncertainty a crucial problem in supply chain design. In this regard, this study aims to develop an optimization model to configure multi-period, multi-product and multi-echelon supply chains, managing the demand and supply uncertain parameters simultaneously to maximize the profit of the entire chain. In this model, the uncertainty of the parameters was regulated by robust optimization and dynamic pricing approaches. First, the developed model was solved using the GAMS software, then the appropriate performance of the proposed model was compared with a certain base model, the validation of results were performed using sensitivity analysis. In the next step, through simulating supply and demand fluctuations, the model's performance under different conditions of uncertainty was evaluated. The results indicate that the model's optimal solution could resist this random uncertainty, even exposing a high level of supply and demand fluctuations.