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Recently, the manufacturing industry has been striving for sustainability because of the environmental degradation and resource depletion caused by it. Remanufacturing considerably saves material and is energy efficient, and thus, it can represent an important solution to environmental issues. However, the uncertainty of remanufacturing makes the practical management of closed-loop supply chains (CLSCs) difficult. To unlock the value potential of end-of-life (EOL) products, we studied a reuse, remanufacture, and recycle (3R) processing system under quality uncertainty for returned EOL engines. In the system, the returned cores were distributed into different processing routes, depending on the results of quality grading. The proposed matrix operations could efficiently assess the environmental benefits; moreover, we designed an algorithm to calculate the quality coefficient that reflects the overall quality condition of returned EOL cores. The impacts of quality uncertainty on the environment could be efficiently quantified via our proposed method. Furthermore, using Monte Carlo simulation and the law of large numbers, we devised a model to establish direct and definite quantitative relationships between the quality coefficient and production indexes. This model provides a basis for the formulation of optimal acquisition strategies under different returning scenarios. [ABSTRACT FROM AUTHOR]