多重故障驱动的再制造并行拆卸序列规划方法

为了克服由故障导致的连接失效、不可拆卸对拆卸序列的影响，提出多重故障驱动的再制造并行拆卸序列规划（RPDSP）方法. 通过分析零件的不可拆卸性以及故障特征，构建层次式多重故障传递链模型，描述产品零部间的故障类型、故障程度、报废程度、回收决策、拆卸模式等信息. 为了方便计算机编程，应用多色集合理论对模型进行数学描述，映射为多重故障信息传递多色模型，通过多色推理识别出零部件的拆卸模式. 将零件故障信息融入拆卸模型，建立可破坏性拆卸信息模型，并推导出可拆卸性条件. 为了表示零件可破坏信息，提出包含拆卸节点层和破坏约束层的多层染色体编码方法. 设计考虑零部件回收决策的初始种群获取方法以剔除不合理序列，通过选择、交叉、变异等进化机制实现多重故障驱动的可破坏再制造并行拆卸序列寻优. 以波轮式洗衣机为案例验证所提方法的可行性和有效性.

Remanufacturing parallel disassembly sequence planning method driven by multiple failures

A remanufacturing parallel disassembly sequence planning (RPDSP) method driven by multiple failures was proposed in order to overcome the influence of connection failure and component non-disassembly caused by failures on the disassembly sequence. A hierarchical multiple failures transfer chain model was constructed through analyzing the non-detachability and failure characteristics of the parts to describe the relationship among the failure type, failure degree, scrap level, recycling decision, and disassembly mode of parts. Furthermore, the model was formally described in mathematically language and mapped into a polychromatic graph model of multiple failures information transfer chain based on the polychromatic sets theory, which is convenient for computer programming and manipulating. On this basis, the disassembly mode of parts can be identified by polychromatic reasoning. The information of part failure was integrated into the disassembly information model, and then a destructible disassembly information model was established, and the disassemblability formula was derived. A multi-layer chromosome coding method including disassembled node layer and destruction constraint layer was proposed to represent the partial destructive information of the product. An initial population acquisition method considering the part recycling decision was designed to eliminate unreasonable sequences. The RPDSP was realized by chromosome evolution rules such as selection, crossover and mutation operator. Finally, a case study of washing machine was used to demonstrate the feasibility and effectiveness of the proposed method.