多种群遗传算法在消费性电子产品车间调度的应用

发布时间：2018-03-26 03:11

本文选题：遗传算法　切入点：多种群遗传算法　出处：《大连交通大学》2014年硕士论文

【摘要】：自从工业革命开始,世界逐步进入工业社会。工业生产能力成为各国综合国力的最重要指标,因此各国为能使自己富强,在工业生产领域投入巨大资源进行研究。尤其是二次世界大战后,各个工业强国相继使用信息化技术参与生产制造,进而产生国际化,全球化制造的格局,我国更是成为世界制造的工厂。为保持我国的在世界制造领域的份额,对工业生产更深入的研究刻不容缓。这其中生产车间的管理至关重要,尤其是车间调度问题就是其中最关键的突破点。车间调度就是将实际的生产问题经过抽取关键因素,简化成在一定资源和生产约束的前提下,合理调配生产设备,安排工件的生产加工顺序,在最短的时间内完成生产计划的问题。以在全球最大的产品制造商——FOXCONN(富士康科技集团)工作期间进行消费性电子产品生产的经验为基础,结合改进的多种群遗传算法,对客观实际的一线车间调度问题进行研究。提出的新遗传算法在传统的单种群自适应遗传算法上,引入辅助种群形成多种群遗传算法。并从实际生产应用方面讨论了求解车间作业调度问题的标准遗传算法优缺点,认为改进的遗传算法在消费性电子产品车间生产调度中有较好的使用价值。有效的解决了遗传算法本身具有一些缺点,如局部搜索能力差,求解耗费时间长,算法运行后期的搜索效率较低,容易陷入“早熟收敛”的陷阱等问题。本文实现了利用多种群遗传算法解决车间调度问题,并结合在烟台富士康消费性电子产品事业处(CCPBG) CNB-成型部门的生产SONY笔记本的工作经历进行评估。实际测试了算法的使用效率,并将改进的算法应用到实际生产调度模拟系统中,结果证明多种群遗传算法是可行的,求解速度较快,在CNB-成型1课的车间生产调度问题上有良好的实用性、高效性。显著提高了其中两条产线的生产效率,获得企业认可。
[Abstract]:Since the beginning of the industrial revolution, the world has gradually entered the industrial society. The industrial production capacity has become the most important indicator of the overall national strength of all countries, so that each country can make itself rich and strong. Great resources have been invested in the field of industrial production, especially after the second World War. After World War II, various industrial powers have successively used information technology to participate in production and manufacturing, thus creating a pattern of internationalization and globalization of manufacturing. In order to maintain our country's share in the world manufacturing field, it is imperative to study industrial production more deeply. Among these, the management of production workshops is of great importance. Especially the job shop scheduling problem is the most critical breakthrough point. Job shop scheduling is to extract the key factors from the actual production problem and simplify it into the rational deployment of production equipment under the premise of certain resources and production constraints. Arrange the production and processing order of the workpiece and complete the production plan in the shortest time. Based on experience in consumer electronics production during the work of Foxconn, the world's largest product manufacturer, combined with improved multi-population genetic algorithms, In this paper, the objective and practical first-line job-shop scheduling problem is studied. A new genetic algorithm is proposed, which is based on the traditional single-population adaptive genetic algorithm. The advantages and disadvantages of the standard genetic algorithm for solving job shop scheduling problems are discussed in terms of practical production applications. It is considered that the improved genetic algorithm has better use value in the shop shop scheduling of consumer electronic products. It effectively solves some disadvantages of genetic algorithm, such as poor local search ability and time-consuming solution. The search efficiency of the algorithm is low, and it is easy to fall into the trap of "premature convergence". In this paper, multi-population genetic algorithm is used to solve job shop scheduling problem. Combined with the working experience of producing SONY notebooks in the CNB- forming department of the Consumer Electronics Department of Yantai Foxconn, the efficiency of the algorithm is tested, and the improved algorithm is applied to the actual production scheduling simulation system. The results show that the multi-population genetic algorithm is feasible, the solution speed is fast, and it has good practicability and high efficiency in the shop shop scheduling problem of CNB-1 class. The production efficiency of two of the production lines has been improved significantly and the enterprises have approved it.
【学位授予单位】：大连交通大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TB497;TP18

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