基于改进遗传模拟退火算法的生产调度研究

发布时间：2018-01-20 06:51

本文关键词： 生产调度模拟退火算法遗传算法遗传模拟退火算法　出处：《郑州航空工业管理学院》2017年硕士论文　论文类型：学位论文

【摘要】：生产调度是企业生产制造过程中不可或缺的一部分,对制造企业的整体运营起到至关重要的作用。随着网络技术、通信技术的发展与广泛运用,世界范围内的交流越来越频繁,经济全球化趋势越来越明显;同时,社会物质条件的改善导致市场需求的变化,市场渐趋多样化、定制化。全球化的竞争以及市场的变化导致企业传统的生产方式很难满足企业长期生存的要求,企业急需要转变生产方式。小品种、定制化的生产方式渐渐被越来越多的众多企业所采纳,这样的生产方式给车间调度提出了更高的要求。如何利用现代化的技术与方法实现对生产调度的统筹优化,准确快速的实现调度安排,快速调配资源,加快企业的生产效率已成为众多制造企业急需解决的问题。本文针对企业车间生产调度问题,研究了企业车间生产调度问题的数学模型以及模拟退火算法和遗传算法在车间调度中的应用。针对两种算法的各自特性与优缺点提出并设计了改进遗传模拟退火算法。并且,文章应用典型案例的求解结果对比,分析证实了算法的有效性与良好的性能。最后,通过应用改进遗传模拟退火算法对实际案例进行求解,进一步验证算法的可行性与实用性。文章对于遗传模拟退火算法在生产调度问题中的应用与算法的优化进行了深入的研究,通过改进算法的变异算子、选择算子提高了算法的优化求解性能以及规避“早熟”问题的能力。
[Abstract]:Production scheduling is an indispensable part of the manufacturing process, which plays a vital role in the overall operation of manufacturing enterprises. With the development and wide application of network technology, communication technology. Worldwide exchanges are becoming more and more frequent, and the trend of economic globalization is becoming more and more obvious. At the same time, the improvement of social material conditions leads to the change of market demand and the diversification of market. Customization. The globalization of competition and market changes lead to the traditional mode of production is difficult to meet the long-term survival requirements of enterprises, enterprises urgently need to change production methods. Small varieties. The customized mode of production is gradually adopted by more and more enterprises. This mode of production puts forward higher requirements for job shop scheduling. How to use modern technology and methods to achieve the overall optimization of production scheduling, accurate and rapid implementation of scheduling arrangements, rapid allocation of resources. Speeding up the production efficiency of enterprises has become a problem that many manufacturing enterprises urgently need to solve. The mathematical model of shop shop scheduling problem and the application of simulated annealing algorithm and genetic algorithm in job shop scheduling are studied. The improved genetic simulated annealing is proposed and designed according to the respective characteristics, advantages and disadvantages of the two algorithms. Algorithm. And. In this paper, the results of typical cases are compared, and the effectiveness and good performance of the algorithm are verified. Finally, the improved genetic simulated annealing algorithm is used to solve the actual cases. Further verify the feasibility and practicability of the algorithm. In this paper, the genetic simulated annealing algorithm in production scheduling problems and optimization of the application of in-depth research, by improving the algorithm mutation operator. The selection operator improves the optimization performance of the algorithm and the ability to avoid the precocious problem.
【学位授予单位】：郑州航空工业管理学院
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP18;TB497

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