Robust supply chain network design with resilient supplier selection under disruption risks

Supply chain network design and resilient supplier selection are important in supply chain risk management to deal with various operational and disruption risks. In this paper, we develop a robust mathematical bi-objective, multi-product model to consider resilient suppliers and uncertainty in supply chain network design across a multi-period and multi-products simultaneously, and this study offers optimal solutions for resilient supplier selection and order allocation. First, we show a Mixed-Integer Linear Programming (MILP) model with two objective functions. The first objective function maximizes the total profit, while the second maximizes the total supplier resilience score. Fuzzy SECA was used to obtain the five resiliency criteria weights and the resilience scores for the objective function. We can rank the resilient suppliers using the fuzzy SECA method. We proposed an approach for coordinating production planning, supplier selection, and order allocation. Theε-constraint method was used to obtain optimum amounts of decision variables to maximize the profit for a real case study. Finally, a Pareto solution analysis was done to determine the tradeoff between robustness and resilience. The results show how uncertainty parameters in the supply chain can affect the objective function. Furthermore, this paper shows that with a supplier resilience score of 4000, the first objective function of the model presents the highest value. Therefore, at this point, we can have a resilient supplier with maximum profitability.