Uncertainties in the quality and price of returned items add to the management complexity of remanufacturing system. To achieve the optimal procurement strategy for improved economic performance, this paper develops a mathematical model to quantify the impact of quality-based categorization of returned products under different return scenarios. Through the stochastic analysis of quality uncertainty with returns, it derives the optimal procurement strategy for cost minimization. The results show that an optimal procurement decision leads to over 80% cost savings in multi-echelon remanufacturing systems. Further, to bridge the gap between optimality theory and practical applications, this paper employs maximum likelihood estimation to speculate the key parameters of quality distribution in an empirical case whereby the optimal procurement strategies are formulated. Thus, the study provides a mathematical foundation for remanufacturers to make optimal decisions for the minimization of total cost. Importantly, the paper innovatively employs a quality coefficient to signal the overall condition in a batch of returned cores before accepting or dismantling, and then establishes the explicit relationship among the quality coefficient and production indexes in remanufacturing.

退回物品的质量和价格的不确定性增加了再制造系统的管理复杂性。为了实现最佳采购策略以提高经济绩效，本文开发了一个数学模型来量化不同退货场景下基于质量的退货产品分类的影响。通过对质量不确定性和收益的随机分析，推导出成本最小化的最优采购策略。结果表明，最佳采购决策可在多级再制造系统中节省 80% 以上的成本。此外，为了弥合最优理论与实际应用之间的差距，本文采用最大似然估计，在制定最优采购策略的实证案例中推测质量分布的关键参数。因此，该研究为再制造商做出最小化总成本的最佳决策提供了数学基础。重要的是，本文创新性地用质量系数来表示一批退回芯在接收或拆解前的整体状况，进而建立了质量系数与再制造生产指标之间的显式关系。