多品种小批量生产模式下的智能调度方法研究
发布时间：2018-08-08 19:28
【摘要】：多品种小批量生产模式是近几年来较为流行的一种生产模式,生产调度是该生产模式下企业管理中十分重要的一个环节,对企业的产品库存量、产品生产周期、生产资源利用率等方面都有着重要的影响。经典调度理论主要是针对传统模式下的生产调度问题进行研究,和实际调度问题之间仍然存在一定差距。20世纪末以来,各种计算智能方法在生产调度中得到了越来越多的应用,并且表现出良好的前景,逐步成为现今生产调度研究领域的主流方法。因此针对多品种小批量生产模式下的智能调度方法进行研究,对于提高企业的生产效率、降低生产成本、增加企业效益有着重要的意义。 本文对多品种小批量的生产模式的特点进行了深入分析,建立了该模式下生产调度问题的数学模型,在深入研究了遗传退火算法关键技术的基础上,对其进行了适应性的改进,最后设计开发了基于改进遗传退火算法的多品种小批量生产智能调度原型系统,为基于计算智能的多品种小批量生产调度技术的发展进行了有益的探索。论文在理论与实践中的主要研究成果如下： (1)在分析生产调度的经典数学模型及表达方式的基础上,结合多品种小批量生产模式的特点,建立了多品种小批量生产模式下生产调度问题的数学模型。 (2)深入分析了遗传退火算法各种实现技术和设计准则,并针对传统遗传退火算法在面对实际生产调度过程中的不足,对其进行改进,使之能够满足实际生产过程中对装配约束和优先级调度的需求。 (3)从数据结构和调度算法两个方面,对生产调度原型系统进行了设计,利用C++与C#的混编技术,开发了基于改进遗传退火算法的智能调度原型系统。原型系统主要包括调度、输出和统计三个功能模块。实际应用表明原型系统具有较快的计算速度,参数设置灵活,通用性较强,有一定的实用价值。
[Abstract]:Multi-variety and small-batch production model is a popular production mode in recent years. Production scheduling is a very important part of enterprise management under this production mode. The utilization ratio of production resources has important influence. The classical scheduling theory mainly focuses on the production scheduling problem in the traditional mode. There is still a certain gap between the traditional scheduling problem and the actual scheduling problem. Since the end of the 20th century, a variety of computational intelligence methods have been more and more used in production scheduling. And show good prospects, gradually become the mainstream method in the field of production scheduling research. Therefore, it is of great significance to study the intelligent scheduling method under the multi-variety and small-batch production mode to improve the production efficiency, reduce the production cost and increase the efficiency of the enterprise. In this paper, the characteristics of multi-variety and small-batch production model are deeply analyzed, and the mathematical model of production scheduling problem under this model is established. On the basis of deeply studying the key technology of genetic annealing algorithm, the adaptive improvement is made. Finally, an intelligent scheduling prototype system for multi-variety and small-batch production based on improved genetic annealing algorithm is designed and developed, which provides a useful exploration for the development of multi-variety and small-batch production scheduling technology based on computational intelligence. The main research results in theory and practice are as follows: (1) on the basis of analyzing the classical mathematical model and expression of production scheduling, combined with the characteristics of multi-variety small-batch production model, The mathematical model of production scheduling problem in multi-variety and small-batch production mode is established. (2) various realization techniques and design criteria of genetic annealing algorithm are deeply analyzed. Aiming at the deficiency of the traditional genetic annealing algorithm in the process of actual production scheduling, the paper improves it. It can meet the requirements of assembly constraints and priority scheduling in the actual production process. (3) the prototype system of production scheduling is designed from two aspects of data structure and scheduling algorithm, and the mixed programming technology of C and C # is used. An intelligent scheduling prototype system based on improved genetic annealing algorithm is developed. The prototype system mainly includes three function modules: scheduling, output and statistics. The practical application shows that the prototype system has the advantages of fast calculation speed, flexible parameter setting, strong versatility and practical value.
【学位授予单位】：中国工程物理研究院
【学位级别】：硕士
【学位授予年份】：2011
【分类号】：TH186
本文编号：2172818
[Abstract]:Multi-variety and small-batch production model is a popular production mode in recent years. Production scheduling is a very important part of enterprise management under this production mode. The utilization ratio of production resources has important influence. The classical scheduling theory mainly focuses on the production scheduling problem in the traditional mode. There is still a certain gap between the traditional scheduling problem and the actual scheduling problem. Since the end of the 20th century, a variety of computational intelligence methods have been more and more used in production scheduling. And show good prospects, gradually become the mainstream method in the field of production scheduling research. Therefore, it is of great significance to study the intelligent scheduling method under the multi-variety and small-batch production mode to improve the production efficiency, reduce the production cost and increase the efficiency of the enterprise. In this paper, the characteristics of multi-variety and small-batch production model are deeply analyzed, and the mathematical model of production scheduling problem under this model is established. On the basis of deeply studying the key technology of genetic annealing algorithm, the adaptive improvement is made. Finally, an intelligent scheduling prototype system for multi-variety and small-batch production based on improved genetic annealing algorithm is designed and developed, which provides a useful exploration for the development of multi-variety and small-batch production scheduling technology based on computational intelligence. The main research results in theory and practice are as follows: (1) on the basis of analyzing the classical mathematical model and expression of production scheduling, combined with the characteristics of multi-variety small-batch production model, The mathematical model of production scheduling problem in multi-variety and small-batch production mode is established. (2) various realization techniques and design criteria of genetic annealing algorithm are deeply analyzed. Aiming at the deficiency of the traditional genetic annealing algorithm in the process of actual production scheduling, the paper improves it. It can meet the requirements of assembly constraints and priority scheduling in the actual production process. (3) the prototype system of production scheduling is designed from two aspects of data structure and scheduling algorithm, and the mixed programming technology of C and C # is used. An intelligent scheduling prototype system based on improved genetic annealing algorithm is developed. The prototype system mainly includes three function modules: scheduling, output and statistics. The practical application shows that the prototype system has the advantages of fast calculation speed, flexible parameter setting, strong versatility and practical value.
【学位授予单位】：中国工程物理研究院
【学位级别】：硕士
【学位授予年份】：2011
【分类号】：TH186
【引证文献】
相关期刊论文 前1条
1 韩鹏飞;孙占磊;赵罡;;改进离散粒子群算法及其在飞机装配任务调度中的应用研究[J];图学学报;2013年01期
，本文编号：2172818
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