带准备时间的柔性流水车间多序列有限缓冲区排产优化问题

针对带准备时间的柔性流水车间多序列有限缓冲区排产优化问题，提出一种改进的紧致遗传算法（Improved compactgenetic algorithm，ICGA）与局部指派规则结合的方法来解决该问题。全局优化过程采用改进的紧致遗传算法，为了克服紧致遗传算法（Compact genetic algorithm，CGA）易早熟收敛的问题，提出一种基于高斯映射的概率模型更新方式，在保持紧致遗传算法快速收敛特性的前提下，扩展了种群中个体的多样性，增强了算法进化活力。为减少生产阻塞和降低准备时间对排产过程的影响，设计了多种局部启发式规则来指导工件进出多序列有限缓冲区的分配和选择过程。采用某客车制造企业中的实例数据进行测试，测试结果表明，改进的紧致遗传算法与局部指派规则配合使用，能够有效解决带准备时间的柔性流水车间多序列有限缓冲区排产优化问题。

Multi-queue Limited Buffer Scheduling Problems in Flexible Flow Shop with Setup Times

Aiming at solving the multi-queue limited buffer scheduling problems in flexible flow shop with setup times, an improved compact genetic algorithm(ICGA) with local dispatching rules is proposed. Specifically, the global optimization process adopts ICGA algorithm. In order to overcome the issue of premature convergence of compact genetic algorithm (CGA), a Gaussian-mapping-based probabilistic updating method is proposed. Under the premise of maintaining the fast search feature of CGA, ICGA algorithm expand the diversity of population individuals and enhance the evolutionary vigor of the algorithm. In order to reduce the impact of production blocking and setup times on the scheduling process, multiple local heuristic rules are designed to guide the distribution and selection process of the jobs into and out of the multi-queue limited buffer. The instance data in a bus manufacturing enterprise is used for testing. The test results show that the ICGA algorithm combined with local dispatching rules can effectively solve the problem of multi-queue limited buffer scheduling problems in flexible flow shop with setup times.