智能RGV的动态调度策略研究
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摘要
针对自动化立体仓库中智能Rail Guided Vehicle小车(以下简称RGV)的动态调度问题,对于单工序加工动态调度建立了单目标优化、最短路模型,求得最小支撑树,即耗时最短的工艺路线,由此得到单工序加工的最优调度。针对多工序加工动态调度问题,考虑存在设备柔性和工艺路线的柔性,首先要解决设备的分配问题,其次是调度过程中RGV对信号的响应原则,建立多目标优化模型,利用0-1整数规划求解使得物料加工完所有工序的时间最短,由此得到多工序加工的优化调度。最后得出了不同工序的加工时长对RGV的动态调度和生产车间的最大产量起到了绝对性的影响,RGV的作业参数变化对模型影响极小。
Aiming at the dynamic scheduling problem of the intelligent Rail Guided Vehicle(RGV) in an automated stereoscopic warehouse, a single-objective optimization and shortest path model is established for the dynamic scheduling of a single-job process to obtain the minimal spanning tree, i.e. the shortest time-consuming route of the process, thus arriving at its optimal scheduling. Then aiming at the dynamic scheduling problem of the multi-job process, and considering the flexibility of the equipment and the process route, the equipment allocation problem is firstly solved. Next the signal response principle of the RGV in the scheduling process is established, for which, a multi-objective optimization model is set up, and the 0-1 integer programming used to minimize the time needed for finishing all the jobs of the process, thus optimizing its scheduling. At the end, it is concluded that the processing time of the different jobs has absolute influence on the dynamic scheduling of the RGV and the maximum output of the workshop, and the change of the RGV operation parameters has little influence on the model.
Aiming at the dynamic scheduling problem of the intelligent Rail Guided Vehicle(RGV) in an automated stereoscopic warehouse, a single-objective optimization and shortest path model is established for the dynamic scheduling of a single-job process to obtain the minimal spanning tree, i.e. the shortest time-consuming route of the process, thus arriving at its optimal scheduling. Then aiming at the dynamic scheduling problem of the multi-job process, and considering the flexibility of the equipment and the process route, the equipment allocation problem is firstly solved. Next the signal response principle of the RGV in the scheduling process is established, for which, a multi-objective optimization model is set up, and the 0-1 integer programming used to minimize the time needed for finishing all the jobs of the process, thus optimizing its scheduling. At the end, it is concluded that the processing time of the different jobs has absolute influence on the dynamic scheduling of the RGV and the maximum output of the workshop, and the change of the RGV operation parameters has little influence on the model.
引文
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[15]《运筹学》教材编写组.运筹学[M].北京:清华大学出版社2012.
[2]郑雯,周炳海.基于事件驱动的高架提升传输小车派工与路径调度算法[J].计算机集成制造系统,2013,19(1):75-82.
[3]赵芳.考虑配合工序约束的生产调度问题研究[D].广州:广东工业大学,2010.
[4]李蓓智,杨建国,丁惠敏.基于生物免疫机理的智能调度系统建模与仿真[J].计算机集成制造系统,2002,(6):446-450.
[5]何海洋.考虑机器故障特征的作业车间调度问题研究[D]宜昌:三峡大学,2015.
[6]陈华.基于分区法的2-RGV调度问题的模型和算法[J].工业工程与管理,2014,19(6):70-77.
[7]杨少华,张嘉毅,赵立.基于排队论的环轨多车数量与能力分析[J].计算机科学,2009,36(4):250-253.
[8]Dotoli M,Fanti M P.A colored Petri net model for automated storage and retrieval systems serviced by rail-guided vehicles a control perspective[J].International Journal of Computer Integrated Manufacturing,2005,18(2):122-136.
[9]宴鹏宇,杨乃定,车阿大.自动化制造单元最小完工时间调度问题的混合启发式算法[J].计算机集成制造系统,2010,16(4)847-854.
[10]Liu Y,Li S,Li J,et al.Operation Policy Research of Double Rail-Guided Vehicle Based on Simulation[A].E-Product EService and E-Entertainment(ICEEE),2010 International conference on IEEE[C].2010.
[11]吴焱明,刘永强,张栋,等.基于遗传算法的RGV动态调度研究[J].制造业自动化,2011,33(16):102-104.
[12]徐伟华,熊坚,王文才,等.双工位穿梭车系统能力优化[J].烟草科技,2013,(9):21-26.
[13]刘晓平,徐本柱,彭军,等.工件工序可并行的作业车间调度模型与求解[J].计算机辅助设计与图形学报,2012,24(1)120-127.
[14]吴正佳,何海洋,黄灿超,等.带机器故障的柔性作业车间动态调度[J].机械设计与研究,2015,31(3):94-98.
[15]《运筹学》教材编写组.运筹学[M].北京:清华大学出版社2012.