ABSTRACT On the premise of satisfying the process priority relationship, there are many kinds of feasible sequencing schemes. How to obtain the optimal process sequencing meeting the process priority relationship has always been a popular research area in the field of CAPP (Computer-Aided Process Planning). Currently, some achievements have been made in the field of small and medium-size problems. For large-size problems, due to the explosion of solution space, the existing bionic algorithms are easy to fall into local optimum or even non-convergence. In this paper, a hybrid algorithm combining genetic algorithm and variable neighborhood search is proposed to solve the above problems. The basic idea is to decompose the complex and huge solution space into relatively simple multi-neighborhood spaces, and then search in each neighborhood space by genetic algorithm in turn. The global optimal solution is obtained when a solution is the best solution through all neighborhood spaces. Based on this idea, the hybrid algorithm framework and neighborhood construction rules are developed, and the implementation steps of the hybrid algorithm are detailed. Taking a real-world case as the case study, the feasibility and superiority of the proposed hybrid algorithm are demonstrated by algorithm comparison tests.

中文翻译：

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一种遗传算法与变邻域搜索相结合的混合算法，用于大问题的过程排序优化

摘要 在满足进程优先级关系的前提下，有多种可行的排序方案。如何获得满足工艺优先级关系的最优工艺排序一直是CAPP（计算机辅助工艺规划）领域的热门研究领域。目前，在中小问题领域已经取得了一些成果。对于大型问题，由于解空间爆炸，现有仿生算法容易陷入局部最优甚至不收敛。本文提出了一种结合遗传算法和变量邻域搜索的混合算法来解决上述问题。基本思想是将复杂庞大的解空间分解为相对简单的多邻域空间，然后依次通过遗传算法在每个邻域空间中进行搜索。当一个解是所有邻域空间的最佳解时，就得到了全局最优解。基于此思路，制定了混合算法框架和邻域构建规则，并详细阐述了混合算法的实现步骤。以一个真实案例为案例研究，通过算法对比测试证明了所提出的混合算法的可行性和优越性。