Aiming at the distributed integrated scheduling of complex products with tree structure, a memetic algorithm-based distributed integrated scheduling algorithm is proposed. Based on the framework of the memetic algorithm, the algorithm uses a distributed estimation algorithm for global search and performs a local search strategy based on the critical operation set for the current optimal solution obtained in each evolutionary generation. A bi-chain-based individual representation method is presented and a simple greedy insertion-based decoding method is given; two position-based probability models are built, which are used to describe the distribution of the operation priority and factory assignment, respectively. Based on the designed probability models, two learning-based updating mechanisms and an improved sampling method are given, which ensures that the population evolves towards a promising region. In order to enhance the searchability for the superior solutions, nine disturbance operators based on the critical operation set are presented. The parameters are determined by the design-of-experiment (DOE) test, and the effectiveness of the proposed algorithm is verified by comparative experiments.

针对树结构复杂产品的分布式集成调度，提出了一种基于模因算法的分布式集成调度算法。该算法基于模因算法的框架，采用分布式估计算法进行全局搜索，并根据每个进化代得到的当前最优解的关键操作集执行局部搜索策略。提出了一种基于双链的个体表示方法，并给出了一种简单的基于贪心插入的解码方法；建立了两个基于位置的概率模型，分别用于描述操作优先级和工厂分配的分布。基于所设计的概率模型，给出了两种基于学习的更新机制和改进的抽样方法，这确保了人口向有希望的地区发展。为了增强对优解的可搜索性，提出了九个基于关键操作集的扰动算子。参数通过实验设计（DOE）测试确定，并通过对比实验验证了所提算法的有效性。