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.

在满足过程优先级关系的前提下，有许多种可行的排序方案。如何获得满足过程优先级关系的最佳过程顺序一直是CAPP（计算机辅助过程计划）领域的热门研究领域。当前，在中小型问题领域已经取得了一些成就。对于大型问题，由于解空间的爆炸式增长，现有的仿生算法很容易陷入局部最优甚至不收敛。针对上述问题，提出了一种结合遗传算法和变量邻域搜索的混合算法。基本思想是将复杂而庞大的解决方案空间分解为相对简单的多邻域空间，然后通过遗传算法依次搜索每个邻域空间。当一个解决方案是所有邻域空间中的最佳解决方案时，便获得了全局最优解。基于此思想，开发了混合算法框架和邻域构造规则，并详细说明了混合算法的实现步骤。以实际案例为例，通过算法比较测试证明了所提混合算法的可行性和优越性。