典型制造车间生产调度优化方法的研究

发布时间：2019-05-16 03:13

【摘要】：日益发展的经济形势下,不断变革的生产方式要求企业必须提高生产资源的利用率和生产工作的效率,而生产调度的优化则是其中的关键问题,它是MES系统实施的关键,是ERP实施的核心。合理有效的调度算法是生产调度领域的一个重要分支,它是学术界与企业界关注的热点问题,但大多数调度问题属于NP-hard问题,对于该问题的求解尚未形成一个系统的理论体系。本文系统地研究了生产调度问题和微粒群算法,提出基于微粒群算法的改进方法,实现了该算法在三种典型生产调度问题中的应用,并研究了相应的软件应用系统。首先,在流程型、离散型和混合流程型生产调度问题的国内外研究的基础上,深入研究近年来日益受到关注的新型智能算法——微粒群算法,针对微粒群算法容易陷入局部最优解、后期收敛速度慢等缺点,对微粒群算法提出改进,包括基于混沌的微粒群算法和基于免疫混沌的微粒群算法。其次,基于冶金工业项目、西航精密件加工项目和烟草排程项目,归纳出流程型、离散型和混合流程型三种典型生产调度问题的定义和约束条件,建立相应的数学模型,设计出用于求解三种调度问题的微粒群算法,详细介绍求解过程中任务编码的实现。结合实际工程项目验证了算法的收敛性,给出相应的调度方案。最后,本文设计了生产调度应用软件的总体框架和功能,并对系统各模块的功能进行了研究,在此基础上嵌入三种微粒群优化算法,从系统实用性的角度将理论研究成果进行了恰当地融合。
[Abstract]:Under the developing economic situation, the constantly changing mode of production requires enterprises to improve the utilization rate of production resources and the efficiency of production work, and the optimization of production scheduling is the key problem, which is the key to the implementation of MES system. It is the core of ERP implementation. Reasonable and effective scheduling algorithm is an important branch in the field of production scheduling, which is a hot issue concerned by academia and business circles, but most of the scheduling problems belong to NP-hard problem. A systematic theoretical system has not yet been formed for solving the problem. In this paper, the production scheduling problem and particle swarm optimization algorithm are systematically studied, an improved method based on particle swarm optimization algorithm is proposed, the application of the algorithm in three typical production scheduling problems is realized, and the corresponding software application system is studied. First of all, based on the research of process, discrete and hybrid process production scheduling problems at home and abroad, a new intelligent algorithm, particle swarm optimization (Particle Swarm Optimization), which has attracted more and more attention in recent years, is deeply studied. In view of the shortcomings of particle swarm optimization algorithm, such as easy to fall into local optimal solution and slow convergence speed in the later stage, the particle swarm optimization algorithm is improved, including particle swarm optimization algorithm based on chaos and particle swarm optimization algorithm based on immune chaos. Secondly, based on the metallurgical industry project, Xihang precision parts processing project and tobacco scheduling project, the definitions and constraints of three typical production scheduling problems, process type, discrete type and mixed process type, are summarized, and the corresponding mathematical models are established. A particle swarm optimization algorithm for solving three scheduling problems is designed, and the implementation of task coding in the process of solving the problem is introduced in detail. Combined with the actual engineering project, the convergence of the algorithm is verified, and the corresponding scheduling scheme is given. Finally, this paper designs the overall framework and function of the production scheduling application software, and studies the functions of each module of the system, on the basis of which three kinds of particle swarm optimization algorithms are embedded. The theoretical research results are properly integrated from the point of view of system practicability.
【学位授予单位】：北京交通大学
【学位级别】：硕士
【学位授予年份】：2011
【分类号】：TH186

【引证文献】