摘要
为快速应对柔性作业车间生产过程中出现的突发状况,构建了一种以全局任务最大生产完成时间以及紧急订单生产完成时间为优化目标的柔性作业车间动态调度模型。针对上述模型,提出一种更加适用于动态排产的动态交互层DIL (Dynamic Interaction Layer)来代替滚动窗口。设计了粒子群遗传混合算法PSGA (Particle Swarm Genetic hybrid Algorithm),将粒子群算法中位置更新策略与遗传算法基因突变融合,加强算法局部搜索能力。针对柔性作业车间订单加急的意外状况,采用DIL与PSGA相结合的方法求解动态调度问题。通过仿真实验,验证了DIL处理紧急订单的能力和PSGA算法的有效性。
In order to quickly response to the unforeseen circumstances in flexible job shop, a dynamic flexible job shop scheduling model is constructed, which takes the overall production time and the completion time of emergency orders as the optimization objectives. For the model, a dynamic interaction layer(DIL) model, which has a better performance on DFJSP, is proposed to replace the scroll window. Particle swarm genetic hybrid algorithm(PSGA) is designed to combine the particle swarm optimization algorithm with the genetic algorithm to enhance the ability of local search. Aiming at the unexpected urgent orders in flexible job shop, DIL and PSGA are combined to solve the dynamic scheduling problem. The simulation experiments verify DIL’s ability to handle urgent orders and the effectiveness of PSGA.
作者
张祥
王艳
纪志成
Zhang Xiang;Wang Yan;Ji Zhicheng(Engineering Research Center of Internet of Things Technology Applications Ministry of Education,Wuxi 214122,China)
出处
《系统仿真学报》
CAS
CSCD
北大核心
2020年第11期2129-2137,共9页
Journal of System Simulation
基金
国家自然科学基金(61973138)
国家重点研发计划(2018YFB1701903)。
关键词
柔性作业车间
粒子群遗传算法
动态交互层
动态排产
flexible job shop scheduling problem
particle swarm genetic hybrid algorithm
dynamic interaction layer
dynamic scheduling
