柔性车间双资源集成调度的小生境遗传算法优化

Integrated scheduling optimization of two resources in flexible job-shop based on niche genetic

  • 摘要: 针对柔性作业车间生产和物流双资源集成调度问题,提出了基于小生境自适应遗传算法的集成调度方法。首先,明确了柔性车间AGV物流流程、路径冲突消除方法和分配策略。其次,描述了车间生产和物流双资源集成调度问题,并建立了以完工时间最短为目标的优化模型。然后,将小生境技术和自适应策略引入到遗传算法中,使遗传策略随小生境特点自适应变化,提出了一种新的小生境自适应遗传(NAGA)算法。最后,制定了基于小生境自适应遗传算法的双资源集成调度流程。经实验验证,小生境自适应遗传算法调度的完工时间短于遗传(GA)算法和文献1改进分布估计(IEDA)算法,说明NAGA算法的集成调度性能好于GA算法和IEDA算法。经AGV数量影响分析,车间完工时间随AGV数量增加整体呈下降趋势,且当AGV数量饱和时完工时间不再下降。

     

    Abstract: Aiming at the integrated scheduling problem of production and logistics resources in flexible workshop, a niche adaptive genetic algorithm based integrated scheduling method is proposed. First of all, logistics process, path conflict elimination method and allocation strategy of the AGV in flexible workshop are defined. Secondly, the integrated scheduling problem of workshop production and logistics resources is described, and an optimization model aiming at the shortest completion time is established. Then, the niche technology and adaptive strategy are introduced into the genetic algorithm to make the genetic strategy change with niche characteristics adaptively. And a new niche adaptive genetic algorithm (NAGA) is proposed. Finally, a niche adaptive genetic algorithm based two-resource integrated scheduling process is developed. Through experimental verification, the completion time of niche adaptive genetic algorithm scheduling is shorter than that of genetic algorithm (GA) and improved distribution estimation (IEDA) algorithm in literature 1, which indicates that the integrated scheduling performance of NAGA algorithm is better than that of GA algorithm and IEDA algorithm. According to the impact analysis of the number of AGVs, the completion time of the workshop decreases with the increase of the number of AGVs, and the completion time does not decline when the number of AGVs is saturated.

     

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