Disassembly is one of the essential operations in manufacturing to recover the useful parts of the product after End of Life (EOL). Moreover, by generating an optimal disassembly sequence, the time to dismantle the product will be reduced, and in turn, cost also reduces. However, achieving an optimal disassembly sequence is not an easy task as it is an NP-hard combinatorial problem. Many researchers followed different approaches like mathematical, knowledge-based and artificial intelligence (AI)-based methods to generate optimal disassembly sequences. Most of the researchers concentrate on generating the optimal disassembly sequence, but only a few of them discuss the disposal of the parts after EOL. It is very much essential to consider the type of disposal that has to follow the individual components after dismantle to reduce the effect on the environment due to parts of the EOL product. In this research work, a stability graph cut-set method is applied to generate optimal disassembly sequences by considering the minimum number of directional changes as a fitness equation. In the proposed methodology, a stability graph is formulated for the considered assembly to apply cut-set rules for generating optimal assembly sequences. Later, the reverse of the obtained optimal assembly sequences is followed to generate the optimal disassembly sequences. In this strategy, along with the generation of optimal disassembly sequences, the type of disposal (like landfill, incineration and recycling) that has to follow for the individual parts is also discussed using a SOLIDWORKS sustainable tool. The proposed stability graph cut-set method is validated using an eleven-part punching machine assembly to generate the optimal disassembly sequences; also the type of disposal that has to follow for each part after dismantle is discussed. Moreover, the proposed methodology is compared to the well-known algorithms [genetic algorithm (GA) and ant colony optimization (ACO) algorithm] in terms of the number of iterations, the number of optimal disassembly sequences generated and fitness value to check the performance of the algorithm.

拆卸是在生产过程中要恢复寿命终止（EOL）后产品的有用零件的基本操作之一。而且，通过产生最佳的拆卸顺序，将减少拆卸产品的时间，进而降低了成本。但是，实现最佳的拆卸顺序并非易事，因为这是NP难题。许多研究人员遵循不同的方法，例如数学，基于知识和基于人工智能（AI）的方法来生成最佳拆卸序列。大多数研究人员专注于生成最佳的拆卸顺序，但只有少数人讨论了停产后的零件处置。非常重要的一点是，考虑在拆卸后必须遵循各个组件的处置方式，以减少由于EOL产品的部件而对环境造成的影响。在这项研究工作中，采用稳定性图割集方法，通过将方向变化的最小数量作为适应性方程，来生成最佳拆卸序列。在所提出的方法中，为所考虑的装配制定了稳定性图，以应用割集规则来生成最佳装配顺序。随后，按照获得的最佳组装顺序的相反顺序生成最佳拆卸顺序。在此策略中，除了生成最佳拆卸顺序外，还需要处理类型（例如垃圾填埋场，还使用SOLIDWORKS可持续性工具讨论了各个零件必须遵循的``焚烧和回收利用''）。所提出的稳定性图割集方法是使用由11个零件组成的冲压机组装件进行验证，以生成最佳的拆卸顺序。还讨论了拆卸后每个零件必须遵循的处置类型。此外，在迭代次数，生成的最佳拆卸序列数和适应性值方面，将所提出的方法与众所周知的算法[遗传算法（GA）和蚁群优化（ACO）算法]进行比较，以检查性能算法的 还讨论了拆卸后每个零件必须遵循的处置类型。此外，在迭代次数，生成的最佳拆卸序列数和适应性值方面，将所提出的方法与众所周知的算法[遗传算法（GA）和蚁群优化（ACO）算法]进行比较，以检查性能算法的 还讨论了拆卸后每个零件必须遵循的处置类型。此外，在迭代次数，生成的最佳拆卸序列数和适应性值方面，将所提出的方法与众所周知的算法[遗传算法（GA）和蚁群优化（ACO）算法]进行比较，以检查性能算法的