离散制造业中的多目标柔性智能调度问题的研究与应用
发布时间：2018-11-20 09:00
【摘要】：传统作业车间调度问题的拓展是多目标柔性车间调度,多目标柔性车间调度更符合现在车间的实际生产情况,对该问题的研究具有现实意义。本文依托宁夏某仪表制造有限公司为背景,该企业是一家离散型制造阀门的企业,实现的是多品种、少批量、多批次的符合现代市场动态的生产方式,企业生产通常受到多个方面的因素的限制。在满足客户需求的情况下我们从企业生产实际出发,抽取出企业需满足的三个目标函数,分别是企业最大利益下的最小机器负载、最短加工时间和最小成本3个目标函数。如果希望3个目标达到预期的值使企业盈利,那么就需要一个合理的车间调度模型和有效地生产调度算法。本文在综合分析国内外关于车间调度问题的基础上,考虑本研究的柔性作业车间运作的实际情况,对多目标作业车间调度问题进行了一个系统的研究。本篇论文所做的主要工作有:(1)从现有车间调度模型的不足之处出发,在本文中给出了基于分层的面向对象的有色Petri网的建模方法;以往的Petri网模型会引起空间爆炸,没有模块性和缺乏可重用性,本文中Petri网建模通过分层的思想和面向对象的技术可以克服这些缺点。(2)针对该宁夏某企业存在的车间调度问题,给出了蚁群粒子群混合车间调度算法。因为粒子群算法的特点是迭代速度非常快,而且容易在最优解附近震荡;而蚁群算法的特点是初始信息素匮乏;利用蚁群粒子群算法优势互补的思想进行求解车间调度。首先介绍了算法的编码解码,目标的归一化,然后给出了两种算法求解多目标车间调度的流程图,最后对流程图进行了详细的介绍。(3)将蚁群粒子群两种算法结合求解实际生产中的多目标柔性车间调度算例。通过对粒子群算法和两种混合算法的实验结果中的非劣解和甘特图进行分析对比,发现混合算法更有效。
[Abstract]:The extension of the traditional job shop scheduling problem is multi-objective flexible job shop scheduling, and the multi-objective flexible job shop scheduling is more in line with the actual production situation of the present job shop, so it is of practical significance to study this problem. Based on the background of Ningxia instrument Manufacturing Co., Ltd., this enterprise is a discrete manufacturing valve enterprise, which realizes the production mode of multi-variety, less batch, multi-batch, in line with the modern market dynamics. Enterprise production is usually limited by multiple factors. In the case of satisfying the customer's demand, we extract three objective functions, which are the minimum machine load, the shortest processing time and the minimum cost, which the enterprise needs to satisfy. If the three goals are expected to make the enterprise profitable, a reasonable job shop scheduling model and an effective production scheduling algorithm are needed. Based on the comprehensive analysis of job shop scheduling problems at home and abroad and considering the actual situation of flexible job shop operation in this paper, a systematic study of multi-objective job shop scheduling problem is carried out. The main work of this thesis is as follows: (1) based on the shortcomings of the existing job shop scheduling model, the modeling method of colored Petri nets based on hierarchical object-oriented is presented; Previous Petri net models can cause space explosion, no modularity and lack of reusability. In this paper, Petri net modeling can overcome these shortcomings through hierarchical thinking and object-oriented technology. (2) aiming at the workshop scheduling problem of a certain enterprise in Ningxia, an ant colony particle swarm hybrid job-shop scheduling algorithm is presented. Particle swarm optimization (PSO) algorithm is characterized by fast iteration speed and easy to concussion near the optimal solution; ant colony algorithm is characterized by the lack of initial pheromone; the ant colony PSO algorithm is used to solve job shop scheduling using the idea of complementary advantages of ant colony Particle Swarm Optimization (APSO) algorithm. Firstly, the coding and decoding of the algorithm and the normalization of the target are introduced, and then the flow chart of the two algorithms to solve the multi-objective job shop scheduling is given. Finally, the flow chart is introduced in detail. (3) the ant colony particle swarm optimization algorithm is combined to solve the multi-objective flexible job shop scheduling example. By analyzing and comparing the non-inferior solution and Gantt diagram in the experimental results of particle swarm optimization and two hybrid algorithms, it is found that the hybrid algorithm is more effective.
【学位授予单位】：宁夏大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP18;TB497
本文编号：2344480
[Abstract]:The extension of the traditional job shop scheduling problem is multi-objective flexible job shop scheduling, and the multi-objective flexible job shop scheduling is more in line with the actual production situation of the present job shop, so it is of practical significance to study this problem. Based on the background of Ningxia instrument Manufacturing Co., Ltd., this enterprise is a discrete manufacturing valve enterprise, which realizes the production mode of multi-variety, less batch, multi-batch, in line with the modern market dynamics. Enterprise production is usually limited by multiple factors. In the case of satisfying the customer's demand, we extract three objective functions, which are the minimum machine load, the shortest processing time and the minimum cost, which the enterprise needs to satisfy. If the three goals are expected to make the enterprise profitable, a reasonable job shop scheduling model and an effective production scheduling algorithm are needed. Based on the comprehensive analysis of job shop scheduling problems at home and abroad and considering the actual situation of flexible job shop operation in this paper, a systematic study of multi-objective job shop scheduling problem is carried out. The main work of this thesis is as follows: (1) based on the shortcomings of the existing job shop scheduling model, the modeling method of colored Petri nets based on hierarchical object-oriented is presented; Previous Petri net models can cause space explosion, no modularity and lack of reusability. In this paper, Petri net modeling can overcome these shortcomings through hierarchical thinking and object-oriented technology. (2) aiming at the workshop scheduling problem of a certain enterprise in Ningxia, an ant colony particle swarm hybrid job-shop scheduling algorithm is presented. Particle swarm optimization (PSO) algorithm is characterized by fast iteration speed and easy to concussion near the optimal solution; ant colony algorithm is characterized by the lack of initial pheromone; the ant colony PSO algorithm is used to solve job shop scheduling using the idea of complementary advantages of ant colony Particle Swarm Optimization (APSO) algorithm. Firstly, the coding and decoding of the algorithm and the normalization of the target are introduced, and then the flow chart of the two algorithms to solve the multi-objective job shop scheduling is given. Finally, the flow chart is introduced in detail. (3) the ant colony particle swarm optimization algorithm is combined to solve the multi-objective flexible job shop scheduling example. By analyzing and comparing the non-inferior solution and Gantt diagram in the experimental results of particle swarm optimization and two hybrid algorithms, it is found that the hybrid algorithm is more effective.
【学位授予单位】：宁夏大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP18;TB497
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