Two-stage optimal acquisition and remanufacturing decisions with demand and quality information updating

Remanufacturing activities in reverse logistics hold significant theoretical and practical value for their obvious economic and environmental benefits. However, the ever-changing market demand and the uncertain quality of returned items make the management of remanufacturing production highly challenging. Previous studies have mainly focused on determining the optimal remanufacturing decisions based on static market demand and full knowledge of cores quality, while overlooking the dynamic changes in demand information and the imperfect estimation of quality distribution case. Therefore, this paper proposes a two-stage acquisition and remanufacturing method with demand and quality information updating. In the first stage, the remanufacturer formulates acquisition and remanufacturing decisions based on predicted demand and estimated quality distribution. According to updated market demand and the results of the first-stage, the remanufacturer adjusts the optimal decisions for the second stage to maximize profit. In light of this, a two-stage nonlinear mathematical model is established for the acquisition and remanufacturing problem. Based on the scenario analysis method and multivariate optimization theory, optimal strategies for each stage are obtained. To assess the effectiveness of the method put forward, numerical experiments, sensitivity analysis of parameters, and comparative analysis with single-stage acquisition and remanufacturing method are conducted. The results show that the two-stage acquisition and remanufacturing method that accounts for demand and quality information updating can demonstrate greater adaptability to external changes. Compared to the single-stage method, the remanufacturer adopting the two-stage method can achieve an expected profit growth of 6 %∼11 %. Additionally, the effectiveness of the two-stage method is significantly influenced by the reorder point, and there exists an optimal reorder point to maximize the total profit of the remanufacturer. Our research contributes to the uncertainty research in reverse logistics, providing new insights for operational decision-making in remanufacturing enterprises.