Dynamic multi-product delivery environments demand rapid coordination of part completion and product-level kitting within hybrid processing and assembly systems to satisfy strict hierarchical supply constraints. The flexible assembly flow shop scheduling problem formally defines dependencies for multi-stage kitting, yet dynamic variants make designing integrated scheduling rules under multi-level time coupling highly challenging. Existing automated heuristic design methods, particularly genetic programming constrained to fixed terminal symbol sets, struggle to capture and leverage dynamic uncertainties and hierarchical dependency information under transient decision states. This study develops an LLM-assisted Dynamic Rule Design framework (LLM4DRD) that automatically evolves integrated online scheduling rules adapted to scheduling features. Firstly, multi-stage processing and assembly supply decisions are transformed into feasible directed edge orderings based on heterogeneous graph. Then, an elite knowledge guided initialization embeds advanced design expertise into initial rules to enhance initial quality. Additionally, a dual-expert mechanism is introduced in which LLM-A evolutionary code to generate candidate rules and LLM-S conducts scheduling evaluation, while dynamic feature-fitting rule evolution combined with hybrid evaluation enables continuous improvement and extracts adaptive rules with strong generalization capability. A series of experiments are conducted to validate the effectiveness of the method. The average tardiness of LLM4DRD is 3.17-12.39% higher than state-of-the-art methods in 20 practical instances used for training and testing, respectively. In 24 scenarios with different resource configurations, order loads, and disturbance levels totaling 480 instances, it achieves 11.10% higher performance than the second best competitor, exhibiting excellent robustness.

翻译：动态多产品交付环境要求快速协调零件完工与产品级配套，以满足混合加工装配系统中严格的层级供应约束。柔性装配流水车间调度问题形式化定义了多阶段配套的依赖关系，然而动态变体使得在多层级时间耦合下设计集成调度规则极具挑战。现有自动化启发式设计方法，特别是受限于固定终端符号集的遗传规划，难以在瞬态决策状态下捕捉和利用动态不确定性及层级依赖信息。本研究开发了一种LLM辅助的动态规则设计框架（LLM4DRD），能够自动演化适应调度特征的集成在线调度规则。首先，基于异构图将多阶段加工与装配供应决策转化为可行的有向边排序。其次，通过精英知识引导的初始化将先进设计专业知识嵌入初始规则以提升初始质量。此外，引入双专家机制：LLM-A演化代码生成候选规则，LLM-S进行调度评估，同时结合动态特征拟合的规则演化与混合评估，实现持续改进并提取具有强泛化能力的自适应规则。通过一系列实验验证了方法的有效性。在用于训练和测试的20个实际算例中，LLM4DRD的平均延迟分别比最先进方法高出3.17-12.39%。在包含不同资源配置、订单负载和扰动水平的24种场景总计480个算例中，其性能比第二名竞争者高出11.10%，展现出优异的鲁棒性。