基于制造执行系统的静态调度算法研究

发布时间：2018-02-26 04:03

本文关键词： 制造执行系统作业车间调度遗传算法混合遗传算法　出处：《长春工业大学》2012年硕士论文　论文类型：学位论文

【摘要】：当今时代,自动化控制和计算机技术发展飞速,制造执行系统由于其能较大程度的优化利用企业资源,以及大大提高企业生产效率方面的优越性,使其被越来越多的企业所认可。对于柔性作业车间这样一个资源使用情况复杂、事件离散且动态变化的系统,调度问题就显得非常重要。优秀的调度计划在提高生产效率和降低产品成本两个方面起着非常重要的作用,正是因为如此,也使得车间调度问题越来越受到人们的关注,这也是本文研究意义之所在。本文首先对遗传算法的理论知识进行了研究,并在遗传算法的思想基础上设计了基于vc++的遗传算法程序平台；并基于所设计的遗传算法平台求解了FT06(6x6)和FT10(10x10)两个Benchmark问题,并与基于启发式规则的启发式算法进行比较,得到比较理想的效果,也印证了本文所设计的遗传算法程序的有效性。最后针对柔性制造车间的问题,本文以159厂的一期工程为背景,分析和研究了制造执行系统(MES)功能结构,针对该厂柔性制造车间的车间调度方面问题开展了工作,并基于本文设计的基于机器最短加工时间的混合遗传算法,求解了该工程问题,最终得出可用于指导生产调度的Gantt图。本文是基于车间的静态调度的基础上进行研究的,许多突发的、不确定的随机因素都不再调度范围之内。本文将机器最短加工时间的概念融入算法中,针对柔性车间出现的某一工序多台可选择机床的情况,使用机器最短加工时间的启发式方法来选定机床,可实施解的局部优化,大大减少了遗传算法的搜索空间,并以此提高算法的效率与染色体个体的质量。将标准遗传算法与基于机器最短加工时间的启发式方法相互结合,计算所得到的生产周期要远低于标准遗传算法。由此可见,混合遗传算法充分体现了其探索解空间的能力,由此也证明了基于机器最短加工时间的混合遗传算法充分地结合启发式方法与遗传算法的优势。
[Abstract]:Nowadays, with the rapid development of automation control and computer technology, manufacturing execution system can optimize the utilization of enterprise resources to a large extent and greatly improve the production efficiency of enterprises. It is recognized by more and more enterprises. For a system with complex resource usage, discrete events and dynamic change, flexible job shop, Scheduling problem is very important. Excellent scheduling plan plays a very important role in improving production efficiency and reducing product cost. Because of this, job shop scheduling problem has attracted more and more attention. This is also the significance of this study. Firstly, the theoretical knowledge of genetic algorithm is studied, and based on the idea of genetic algorithm, a genetic algorithm program platform based on VC is designed. Based on the designed genetic algorithm platform, two Benchmark problems, FT06X6) and FT1010x10), are solved, and compared with heuristic algorithms based on heuristic rules, the results are satisfactory. It also proves the validity of the genetic algorithm program designed in this paper. Finally, aiming at the problem of flexible manufacturing workshop, this paper analyzes and studies the functional structure of manufacturing execution system (mes) based on the first phase project of 159 factory. Based on the hybrid genetic algorithm designed in this paper, the engineering problem is solved based on the hybrid genetic algorithm based on the minimum machining time of the machine. Finally, the Gantt diagram which can be used to guide production scheduling is obtained. This paper is based on the static scheduling of the job shop. Many sudden and uncertain random factors are no longer within the scope of scheduling. In this paper, the concept of the shortest machining time of the machine is integrated into the algorithm, and the heuristic method of the shortest processing time of the machine is used to select the machine tool, aiming at the situation of a flexible workshop in which several machine tools can be selected in a certain process, and the local optimization of the solution can be implemented. The search space of genetic algorithm is greatly reduced, and the efficiency of the algorithm and the quality of chromosome individuals are improved. The standard genetic algorithm is combined with the heuristic method based on the shortest processing time of the machine. The production cycle obtained by the calculation is much lower than that of the standard genetic algorithm. Thus, the hybrid genetic algorithm fully embodies its ability to explore the solution space. It is also proved that the hybrid genetic algorithm based on the shortest processing time of the machine fully combines the advantages of the heuristic method and the genetic algorithm.
【学位授予单位】：长春工业大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：TH166;TP18

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