Abstract Disassembly Sequence Planning (DSP) refers to a disassembly sequence based on the disassembly properties and restrictions of the product parts that meets the benefit goal. This study aims to reduce the number of changes in disassembly direction and disassembly tools so as to reduce the disassembly time. This study proposes a novel Flatworm algorithm that evolves through the regenerative properties of the flatworm. It is similar to the evolutionary concept of genetic algorithms, with evolution as the main idea, but without crossover, mutation or replication mechanisms in the evolutionary processes. Instead, it is based upon the characteristics of the growth, fracture and regeneration mechanisms of the flatworm. The Flatworm algorithm features a variety of disassembly combinations and excellent mechanisms to avoid the local optimal solution. In particular, it has the advantage of keeping a good disassembly combination from being destroyed. In this study, it is compared with two genetic algorithms and two ant colony algorithms and tested in three examples of different complexity: a ceiling fan, a printer, and 150 simulated parts. The solution searching ability and execution time are compared upon the same evaluation standard. The test results demonstrate that the novel Flatworm algorithm proposed in this study is superior to the two genetic algorithms and ant colony algorithms in solution quality.

中文翻译：

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使用 Flatworm 算法进行反汇编序列规划

摘要 拆卸顺序计划(Disassembly Sequence Planning, DSP)是指根据满足效益目标的产品零件的拆卸特性和限制条件进行的拆卸顺序。本研究旨在减少拆卸方向和拆卸工具的变化次数，以减少拆卸时间。这项研究提出了一种新的扁虫算法，该算法通过扁虫的再生特性进化。它类似于遗传算法的进化概念，以进化为主要思想，但在进化过程中没有交叉、变异或复制机制。相反，它基于扁虫的生长、断裂和再生机制的特征。Flatworm 算法具有多种反汇编组合和出色的避免局部最优解的机制。尤其是具有保持良好的拆卸组合不被破坏的优点。在这项研究中，它与两种遗传算法和两种蚁群算法进行了比较，并在三个不同复杂度的示例中进行了测试：吊扇、打印机和 150 个模拟部件。解决方案搜索能力和执行时间以相同的评价标准进行比较。测试结果表明，本研究提出的新型扁虫算法在求解质量上优于两种遗传算法和蚁群算法。解决方案搜索能力和执行时间以相同的评价标准进行比较。测试结果表明，本研究提出的新型扁虫算法在求解质量上优于两种遗传算法和蚁群算法。解决方案搜索能力和执行时间以相同的评价标准进行比较。测试结果表明，本研究提出的新型扁虫算法在求解质量上优于两种遗传算法和蚁群算法。