智能制造中的生产调度优化问题研究

发布时间：2018-05-24 03:49

本文选题：智能制造 + 生产调度　；参考：《西南科技大学》2016年硕士论文

【摘要】：随着德国“工业4.0”的提出,智能制造已成为学术界和产业界的研究热点,其中,生产调度是智能制造中的一个关键问题,研究这类问题具有实际意义。本文针对智能制造中的生产调度优化问题,开展了以下研究工作:(1)智能车间生产调度问题的深入分析。对智能加工车间进行描述并分析了其生产调度的特点。(2)智能生产调度问题的数学模型建立。定义了智能生产调度问题的车间结构并对参数设置不同水平值;建立了智能生产调度问题的数学模型,利用启发式调度规则算法对模型进行求解。(3)启发式调度规则算法的求解与分析。测试了不同调度目标下所有启发式调度规则算法的调度性能,分析了不同车间结构参数对调度规则的影响,得出满足每个调度目标函数的最优调度规则。(4)免疫调度算法的设计与仿真测试。设计了一种基于独特型网络的免疫调度算法,以实现调度的智能化。仿真测试表明,免疫调度算法能动态控制调度规则且能满足多个调度目标。结果表明:建立的智能生产调度模型合理,采用动态调度方法和免疫调度算法对生产调度问题进行优化求解,取得了较好效果,实现了生产调度的智能化。
[Abstract]:With the development of "Industrial 4.0" in Germany, intelligent manufacturing has become a hot research topic in academia and industry. Among them, production scheduling is a key problem in intelligent manufacturing, and it is of practical significance to study this kind of problem. Aiming at the production scheduling optimization problem in intelligent manufacturing, the following research work is carried out in this paper: 1) the in-depth analysis of intelligent shop scheduling problem. This paper describes the intelligent production shop and analyzes the characteristics of production scheduling. The mathematical model of intelligent production scheduling problem is established. This paper defines the shop structure of intelligent production scheduling problem and sets up different level values of parameters, and establishes the mathematical model of intelligent production scheduling problem. The heuristic scheduling rule algorithm is used to solve the model. The scheduling performance of all heuristic scheduling algorithms under different scheduling objectives is tested, and the influence of different job shop structure parameters on scheduling rules is analyzed. The optimal scheduling rule. 4) the immune scheduling algorithm for each scheduling objective function is designed and simulated. An immune scheduling algorithm based on idiotypic network is designed to realize intelligent scheduling. Simulation results show that the immune scheduling algorithm can dynamically control the scheduling rules and meet multiple scheduling objectives. The results show that the intelligent production scheduling model is reasonable, the dynamic scheduling method and immune scheduling algorithm are used to optimize the production scheduling problem, and good results are obtained, and the intelligent production scheduling is realized.
【学位授予单位】：西南科技大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TP301.6;TB497

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