基于模糊推理的不确定环境下拆卸工艺规划

回收产品因磨损等原因其结构和质量会发生改变，拆卸序列规划存在不确定性，拆卸前很难确定产品最优拆卸序列。首先建立一个模糊拆卸Petri网模型表示产品可行拆卸序列和拆卸过程存在的不确定信息，为降低产品质量和拆卸能力的不确定性对拆卸序列优化的影响，建立一个自适应的模糊推理系统，利用模糊推理和反馈学习的方法对产品各拆卸步骤的成本进行预测，然后通过计算不同拆卸序列下拆卸的收益来得到产品最优拆卸序列，最后通过算例证明了方法的有效性。

Fuzzy-Reasoning-Based Disassembly Sequence Planning under Uncertainty

Before the remanufacturing of a type of products,the buy-back products should be disassembled.During the life cycle,the structure of a buy-back product and the quality of the components are changed.Thus,the disassembly sequence is uncertain,which complicates the disassembly process.To solve this problem,a fuzzy Petri net is built to model such a disassembly process such that the effect of uncertainty on the disassembly quality can be described.With this model,the cost resulting from different disassembly sequences can be estimated by using fuzzy reasoning.Then,an algorithm is presented to find the optimal disassembly sequence by minimizing the disassembly cost.An illustrative example is given to verify the effectiveness of the proposed method.