分层混合遗传算法求解柔性作业车间调度问题

发布时间：2018-03-29 23:26

本文选题：柔性作业车间调度　切入点：遗传算法　出处：《工业工程与管理》2017年05期

【摘要】：针对柔性作业车间调度问题,以总拖期最短为目标,提出了一种分层混合遗传算法。其中,根据总拖期的大小,将种群划分为精英层和普通层,精英层包含全局最优的数个不同质个体,其余个体划分为普通层;针对遗传算法局部搜索不足的问题,对精英层提出了一种邻域搜索策略,使代表机器选择和工序顺序的染色体可以根据自身的不足进行调节;针对遗传算法多样性容易丢失的问题,对精英层提出了一种灾变策略,不仅保留了种群的进化优势而且可以向优秀的个体学习。最后通过一系列标准测试函数以及一个生产中的实际案例验证了该算法的有效性。
[Abstract]:Aiming at the problem of flexible job shop scheduling, a hierarchical hybrid genetic algorithm is proposed, in which the population is divided into elite layer and ordinary layer according to the size of total trailing period. The elite layer contains several globally optimal individuals with different qualities, and the others are divided into ordinary layers. In order to solve the problem of insufficient local search in genetic algorithm, a neighborhood search strategy is proposed for elite layer. The chromosomes, which represent machine selection and process order, can be adjusted according to their own shortcomings. Aiming at the problem that genetic algorithm diversity is easily lost, a catastrophic strategy for elite layer is proposed. It not only preserves the evolutionary advantage of the population but also can learn from excellent individuals. Finally, the effectiveness of the algorithm is verified by a series of standard test functions and a practical case in production.
【作者单位】：西安交通大学管理学院;
【基金】：国家社科基金资助项目(15BGL082,15XGL001)
【分类号】：TB497;TP18

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