Integrated process planning and scheduling (IPPS) is of great significance for modern manufacturing enterprises to achieve high efficiency in manufacturing and maximize resource utilization. In this paper, the integration strategy and solution method of IPPS problem are deeply studied, and an improved genetic algorithm based on multi-layer encoding (IGA-ML) is proposed to solve the IPPS problem. Firstly, considering the interaction ability between the two subsystems and the multi-flexibility characteristics of the IPPS problem, a new multi-layer integrated encoding method is designed. The encoding method includes feature layer, operation layer, machine layer and scheduling layer, which respectively correspond to the four sub-problems of IPPS problem, which provides a premise for a more flexible and deeper exploration in the solution space. Then, based on the coupling characteristics of process planning and shop scheduling, six evolutionary operators are designed to change the four-layer coding interdependently and independently. Two crossover operators change the population coding in the unit of jobs, and search the solution space globally. The four mutation operators change the population coding in the unit of gene and search the solution space locally. The six operators are used in series and iteratively optimized to ensure a fine balance between the global exploration ability and the local exploitation ability of the algorithm. Finally, performance of IGA-ML is verified by testing on 44 examples of 14 benchmarks. The experimental results show that the proposed algorithm can find better solutions (better than the optimal solutions found so far) on some problems, and it is an effective method to solve the IPPS problem with the maximum completion time as the optimization goal.

集成过程计划与调度（Integrated Process Planning and Scheduling，IPPS）对于现代制造企业实现制造高效率和资源利用最大化具有重要意义。本文对IPPS问题的集成策略和求解方法进行了深入研究，提出了一种基于多层编码的改进遗传算法（IGA-ML）来求解IPPS问题。首先，考虑到两个子系统之间的交互能力和IPPS问题的多灵活性特点，设计了一种新的多层集成编码方法。编码方法包括特征层、操作层、机器层和调度层，分别对应IPPS问题的四个子问题，为在解空间中更灵活、更深入的探索提供了前提。然后，基于工艺计划和车间调度的耦合特性，设计了6个进化算子相互依赖和独立地改变四层编码。两个交叉算子以作业为单位改变种群编码，全局搜索解空间。四个变异算子以基因为单位改变种群编码，局部搜索解空间。六个算子串联使用并迭代优化，以确保算法的全局探索能力和局部开发能力之间的良好平衡。最后，通过对 14 个基准的 44 个示例进行测试，验证了 IGA-ML 的性能。实验结果表明，所提出的算法可以在某些问题上找到更好的解决方案（优于目前找到的最优解），