Document Type : Article
Operations Management and Information Technology Department, Faculty of Management, Kharazmi University, Tehran, Iran
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 the latter can be disrupted sometimes. In this study, a multi-stage, stochastic programming (MSSP) model is developed to analyze how a company can proactively employ sourcing strategies along with pricing policies to enhance sourcing resilience in a CLSC network design problem, where the return of end-of-life (used) products into recycling centers is stochastic and sensitive to the purchasing price. The stochastic return is modelled using a scenario-tree-based approach. Since the sample average approximation algorithm (SAA) in scenario generation can lead to an increased number of scenarios and make the model hard to solve, a backward scenario reduction algorithm is employed to efficiently reduce the problem size. The developed model is implemented in the automotive battery industry. The findings indicate that an effective pricing policy can help determine the resilient sourcing strategy in the CLSC network design problem and, therefore, maximize the total profit and mitigate the disruption risks. Companies can invest in establishing recycling centers only in conditions of high return risk and formulation of a suitable pricing policy.