Mass customization enables the integration of traditional flow line production with product platforms to accommodate abundant product-process varieties. These platform-based flow lines explore common process routes while highlighting rebatching scheduling with selectivity banks (RBS) to handle large process varieties across production stages at minimum setup cost. Given the inherent coupling between decision making in job diverging and retrieval quality, an interactive optimization approach is necessary for the RBS problem. This study proposes a bilevel interactive optimization (BIO) model for RBS to accommodate high variety flow line production. The model addresses the conflicting goals of lane occupancy cost, process setup cost, and job divergence and retrieval efficiency. Regarding job divergence at the leader-level, a vehicle routeing problem with precedence constraints is formulated and solved by a constructed genetic algorithm (GA). Concerning job retrieval at the follower-level and the ongoing characteristic of selectivity banks, a dispatching problem with various batch size preference and dynamic time window is established and dealt with a restricted dynamic programming (RDP) algorithm after balancing search efficiency and accuracy. Thus, to solve the BIO, a hybrid GA-RDP is developed and implemented. A practical application to an automotive painting shop illustrates the operational benefits of the BIO model for the RBS problem.

大规模定制使传统流水线生产与产品平台集成，以适应丰富的产品工艺品种。这些基于平台的流水线探索了常见的工艺路线，同时强调了使用选择性库 (RBS) 的重新批处理调度，以以最低的设置成本处理跨生产阶段的大型工艺品种。鉴于工作分散决策和检索质量之间的内在耦合，RBS 问题需要一种交互式优化方法。本研究为 RBS 提出了一种双层交互优化 (BIO) 模型，以适应多品种流水线生产。该模型解决了车道占用成本、流程设置成本以及工作分歧和检索效率等相互冲突的目标。关于领导层的工作分歧，通过构造遗传算法（GA）制定和解决具有优先约束的车辆路线问题。针对追随者级别的作业检索和选择性银行的持续特性，在平衡搜索效率和准确性的基础上，建立了具有各种批量大小偏好和动态时间窗口的调度问题，并采用受限动态规划（RDP）算法进行处理。因此，为了解决 BIO，开发并实施了混合 GA-RDP。汽车涂装车间的实际应用说明了 BIO 模型对 RBS 问题的操作优势。在平衡搜索效率和准确性的基础上，建立了具有各种批量大小偏好和动态时间窗口的调度问题，并使用受限动态规划（RDP）算法进行处理。因此，为了解决 BIO，开发并实施了混合 GA-RDP。汽车涂装车间的实际应用说明了 BIO 模型对 RBS 问题的操作优势。在平衡搜索效率和准确性的基础上，建立了具有各种批量大小偏好和动态时间窗口的调度问题，并使用受限动态规划（RDP）算法进行处理。因此，为了解决 BIO，开发并实施了混合 GA-RDP。汽车涂装车间的实际应用说明了 BIO 模型对 RBS 问题的操作优势。