Abstract: Currently, the recycling of End-of-Life Vehicles（ELVs） is driven not only by economic and technological factors, but also by global energy, environmental, and social concerns. This paper uses interpretative structural modeling to analyze ELV recycling systems in order to predict the dependence of the recycling ratio on various factors such as consumer behavior, enterprise preference, government policy, etc. In this methodology, a normal cloud model is proposed by analyzing each factor’s uncertainty using fuzzy language concepts. Then, a multi-dimensional reasoning system and multi-rule deductive systems are established to describe relationships among these factors. In addition, from the system simulation, the results of this paper offer a reasonable prediction of the recycling ratio via formal and illegal recycling channels under different base uncertainty factors such as national legal regulation, financial input, and consumer attitude towards remanufactured products.

Key words: recycling ratio, uncertainty, Interpretative Structural Modeling（ISM）, cloud model

摘要： 报废汽车回收与再制造是节约资源的重要途径，回收率是其中的关键指标之一。影响回收率的因素很多，尤其是回收系统中不确定性因素对回收率具有重要影响。因此，首先从系统工程角度出发，分析消费者行为、企业意愿、政府政策等影响回收率的不确定因素，建立报废汽车回收率系统递阶结构模型，运用模糊语言概念进行定性评价，并借助云模型工具，构建定性规则描述系统因素之间逻辑因果关系，得到报废汽车回收与再制造多维多规则不确定性推理系统，进而通过系统仿真，分析了国家法律法规、财政投入、消费者对再制造品购买态度等不确定性因素对回收率的影响。结果表明，模型直观地揭示了相关不确定性因素对回收率的影响机理，对政府和相关行业具有重要的参考价值。

关键词: 回收率, 不确定性, 解释结构模型, 云模型