摘要
考虑批量分割的多目标柔性作业车间调度问题是经典作业车间调度问题的扩展,研究该问题对于缩短生产周期、提高设备利用率和均衡负荷具有积极作用。它不仅需要确定工件的加工顺序和加工工件的机器,还要确定合理的分批方案。以最大完工时间(makespan)最小和运输距离最短为优化目标,建立柔性作业车间批量分割和调度集成优化模型,并用遗传算法进行求解;针对该问题的特点,提出了基于批量分割、工序排序和机器选择的三段编码方式,利用遗传算子以及基于精英策略的锦标赛选择方法,结合重新启动机制,有效避免算法陷入局部最优。通过数值算例验证了方法的有效性,并分析了不同分割策略对调度结果的影响。研究结果对于提升柔性作业车间的作业管理水平具有一定指导意义。
The multi-objective flexible job shop scheduling problem considering lot-splitting is an extension to the classical job shop scheduling problem.It is of great significance to shorten the makespan,to improve equipment utility and to balance the load.It not only determines jobs’sequence and the corresponding machines but also appropriate splitting of orders.An integrated lot-splitting and scheduling optimization model,aimed at minimizing the makespan and the shortest job transportation distance was established.Genetic algorithm was used to solve it.A three-stage coding scheme based on lot splitting,job sequencing and machine selection was proposed.A composite genetic operator and elites-based tournament selection were designed.Combined with a simple restarting mechanism,local optimal solutions can be avoided effectively.Numerical experimental results were provided to test the validity of the model.The performance of different lot-splitting strategies was analyzed.The results may be useful to improve management of flexible job shops.
作者
谭宬
张纪会
郭藤
Tan Cheng;Zhang Jihui;Guo Teng(Institute of Complexity Science,Qingdao University,Qingdao 266071,Shandong,China;School of Statistics&Mathematics,Central University of Finance&Economics,Beijing 100081,China)
出处
《现代制造工程》
CSCD
北大核心
2020年第12期25-35,共11页
Modern Manufacturing Engineering
基金
国家自然科学基金项目(61673228,61402216)。
关键词
批量分割
柔性作业车间调度
遗传算法
组合遗传算子
lot-splitting
flexible job shop scheduling
genetic algorithm
composite genetic operators
