A resilient closed-loop supply chain network design through integrated sourcing and pricing strategies

Sourcing resilience has become a primary concern in most closed-loop supply chains (CLSC). Companies face the option of sourcing their raw materials from suppliers or recycling centers though latter can be disrupted sometimes. In this study, multi-stage, stochastic programming (MSSP) model is developed to analyze how company proactively employ strategies along with pricing policies enhance CLSC network design problem, where return end-of-life (used) products into and sensitive purchasing price. The modelled using scenario-tree-based approach. Since sample average approximation algorithm (SAA) scenario generation lead an increased number scenarios make hard solve, backward reduction employed efficiently reduce problem size. implemented automotive battery industry. findings indicate that effective policy help determine resilient strategy and, therefore, maximize total profit mitigate disruption risks. invest establishing only conditions high risk formulation suitable policy.