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.

中文翻译：

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两阶段最佳采购和再制造决策，更新需求和质量信息


逆向物流中的再制造活动因其明显的经济和环境效益而具有重要的理论和实践价值。然而，不断变化的市场需求和退货质量的不确定性使再制造生产的管理极具挑战性。以往的研究主要集中在基于静态市场需求和对型芯质量的充分了解来确定最优再制造决策，而忽略了需求信息的动态变化和质量分布案例的不完美估计。因此，本文提出了一种需求和质量信息更新的两阶段收购和再制造方法。在第一阶段，再制造商根据预测的需求和估计的质量分布制定采购和再制造决策。根据更新的市场需求和第一阶段的结果，再制造商调整第二阶段的最优决策，以实现利润最大化。有鉴于此，为收购和再制造问题建立了一个两阶段非线性数学模型。基于情景分析方法和多元优化理论，得到各阶段的最优策略。为了评估所提出的方法的有效性，进行了数值实验、参数敏感性分析以及与单级收购和再制造方法的比较分析。结果表明，考虑需求和质量信息更新的两阶段收购和再制造方法可以表现出对外部变化的更强适应性。 与单阶段法相比，采用两阶段法的再制造商可以实现 6 %∼11 % 的预期利润增长。此外，两阶段法的有效性受再加工点的显著影响，并且存在一个最佳再加工点，可以最大限度地提高再加工商的总利润。我们的研究为逆向物流的不确定性研究做出了贡献，为再制造企业的运营决策提供了新的见解。