Under the current volatile business environment, the requirement of flexible production is becoming increasingly urgent. As an innovative production mode, seru-system with reconfigurability can overcome the lack of flexibility in traditional flow lines. Compared with pure seru-system, the hybrid seru-system composed of both serus and production lines is more practical for adapting to many production processes. This paper addresses a specific hybrid seru-system scheduling optimization problem (HSSOP), which includes three strongly coupled sub-problems, i.e., hybrid seru formation, seru scheduling and flow line scheduling. To minimize the makespan of the whole hybrid seru-system, we propose an efficient cooperative coevolution algorithm (CCA). To tackle three sub-problems, specific sub-algorithms are designed based on the characteristic of each sub-problem, i.e., a sub-space exploitation algorithm for hybrid seru formation, an estimation of distribution algorithm for seru scheduling, and a first-arrive-first-process heuristic for flow line scheduling. Since three sub-problems are coupled, a cooperation coevolution mechanism is proposed for the integrated algorithm by information sharing. Moreover, a batch reassign rule is designed to overcome the mismatch of partial solutions during cooperative coevolution. To enhance the exploitation ability, problem-specific local search methods are designed and embedded in the CCA. In addition to the investigation about the effect of parameter setting, extensive computational tests and comparisons are carried out which demonstrate the effectiveness and efficiency of the CCA in solving the HSSOP.

在当前多变的商业环境下，柔性生产的需求日益迫切。作为一种创新的生产模式，具有可重构性的血清系统可以克服传统流线缺乏灵活性的问题。用纯水相比SERU -System，混合SERU的两个组成-System SERU S和生产线是适应许多生产过程更为实用。本文解决了一个特定的混合血清系统调度优化问题（HSSOP），它包括三个强耦合子问题，即混合血清形成、血清调度和流线调度。为了最小化整个混合血清系统的完工时间，我们提出了一种有效的合作协同进化算法（CCA）。为了解决三个子问题，特定的子算法被设计基于每个子问题的特点，即，用于混合的子空间算法开发SERU形成，用于分配算法的估计SERU调度，以及用于流线调度的先到先过程启发式。由于三个子问题是耦合的，因此通过信息共享为集成算法提出了一种协作协同进化机制。此外，还设计了批量重新分配规则来克服合作协同进化过程中部分解的不匹配问题。为了增强开发能力，特定问题的局部搜索方法被设计并嵌入到 CCA 中。除了对参数设置的影响进行调查外，还进行了大量的计算测试和比较，以证明 CCA 在解决 HSSOP 方面的有效性和效率。