基于价值流的生产线平衡方法及其应用研究

发布时间：2018-04-10 17:45

本文选题：价值流 + 生产线平衡　；参考：《浙江理工大学》2017年硕士论文

【摘要】：本文的研究内容是在H公司“生产线流程改善与车间设施规划分析”的项目的背景下进行,论文主要以价值流为基础,进行生产线平衡的研究,在现状价值流图的基础上发现了车间装配生产线上的一些问题,并根据目标要求进行了数学模型的建立和求解,再绘制未来价值流程图,通过实施来减少公司的浪费及其他的相关问题。本文在计算机优化仿真的背景下,对装配生产线的平衡问题进行仿真求解,考虑采用粒子群优化算法并引入遗传算法的优势进行求解。粒子群算法也被称作粒子群优化算法,英文名为Particle Swarm Optimization,缩写为PSO,其来源是模拟鸟群的觅食行为,是一种以群体协作为基础的随机搜索算法,一般也被认为是一种群体智能。粒子群算法其优化基础是迭代的方法,最初的系统有一组随机解,不断通过迭代的方法来计算最优解,是其他粒子在一个特定的空间中追随着最优的粒子进行搜索最优解,其优势也是非常的明显,当在动态的目标或者连续不断的多维空间里会体现出质量高、速度很快、鲁棒性较好的特性。但是生产线平衡具有离散性的特点,而粒子群算法没有过于复杂的编码和其他变异的操作过程,非常有可能得不到一个最优解。针对粒子群算法在装配生产线平衡方面的问题,本文的解决方法是考虑通过引进遗传算法,因为遗传算法就是为了解决在传统数学方法不能有效快速的求出相对大规模的复杂难题,也具有特定的优势,比如遗传算法是随机、迭代的并且是不厌其烦的搜寻目标的最优解。所以综合考虑,引入遗传算法之后,基本上可以解决粒子群算法在离散性上的问题。第一章,文章绪论。介绍了本文研究的相关背景和研究意义,分析了价值流图析技术和生产线平衡问题的国内外的研究现状并分析了今后的发展趋势,阐述了论文的相关内容及体系结构的安排。第二章,将与论文相关的理论知识进行了详细的介绍,包括价值流图析技术与精益生产领域的生产平衡问题的计算和相关分析。第三章,根据在现场的测量和记录数据,结合H公司DTZ545型仪表的实际情况,绘制了车间布局图和车间物流图,并绘制了产品工艺图,进行了数据的收集,以收集到的数据为基础,绘制了价值流现状图,并根据现状图分析了生产线存在的问题。第四章,对生产线平衡问题进行了数学建模和仿真优化,分析了粒子群优化算法的原理及其优缺点,并对其在生产线平衡问题上的应用进行了讨论,根据算法特点,考虑引入遗传算法对缺点进行改进。第五章,模型建立后进行仿真,通过算法在Mat lab中进行求解,根据求解的结果可以绘制出价值流的未来状态图,并进行实施,来实现未来的价值流。第六章,总结和展望。分析了本文的创新点和文章对实际生产的意义,并指出了文章存在的不足。
[Abstract]:The research content of this paper is carried out under the background of the project of "production line process improvement and workshop facility planning analysis" in H Company. The paper mainly studies the balance of production line based on value flow.On the basis of the current value flow graph, some problems in workshop assembly line are found, and the mathematical model is established and solved according to the objective requirements, and then the future value flow chart is drawn.Reduce waste and other related issues through implementation.In this paper, under the background of computer optimization simulation, the balance problem of assembly line is simulated, particle swarm optimization algorithm is considered and the advantage of genetic algorithm is introduced to solve the problem.Particle Swarm Optimization (PSO) is also called PSO (Particle Swarm Optimization), which is derived from the simulation of the foraging behavior of birds and is a random search algorithm based on swarm cooperation. It is also considered to be a kind of swarm intelligence.Particle swarm optimization algorithm is based on iterative method. The initial system has a set of random solutions, and the iterative method is constantly used to calculate the optimal solution. The other particles follow the optimal particle in a particular space to search for the optimal solution.Its advantages are also very obvious, when the dynamic target or continuous multi-dimensional space will reflect the characteristics of high quality, fast speed, good robustness.But the balance of production line is discrete, and particle swarm optimization has no complicated operation process of coding and other mutation, so it is very likely that we can not get an optimal solution.Aiming at the problem of particle swarm optimization in the balance of assembly line, the solution of this paper is to introduce genetic algorithm.Because genetic algorithm is to solve the traditional mathematical method can not effectively and quickly solve relatively large complex problems, but also has certain advantages, such as genetic algorithm is random,Iterative and painstaking search for the target's optimal solution.Therefore, after introducing genetic algorithm, particle swarm optimization (PSO) can solve the discreteness problem.Chapter one, introduction to the article.This paper introduces the background and significance of this research, analyzes the current research situation of value flow graph analysis technology and production line balance at home and abroad, analyzes the development trend in the future, and expounds the related contents of the paper and the arrangement of the system structure.In the second chapter, the theoretical knowledge related to the paper is introduced in detail, including the calculation and analysis of the balance of production in the field of lean production.In the third chapter, according to the field measurement and recording data, combined with the actual situation of H company DTZ545 instrument, the workshop layout diagram and workshop logistics diagram are drawn, and the product process diagram is drawn, and the data collection is carried out.Based on the collected data, the value flow status chart is drawn, and the existing problems of production line are analyzed according to the status chart.In the fourth chapter, mathematical modeling and simulation optimization of production line balance problem are carried out, the principle of particle swarm optimization algorithm and its advantages and disadvantages are analyzed, and the application of particle swarm optimization algorithm in production line balance problem is discussed, according to the characteristics of the algorithm.Consider introducing genetic algorithm to improve the shortcomings.In the fifth chapter, the model is simulated and solved in Mat lab. According to the result, the future state diagram of the value flow can be drawn and implemented to realize the future value flow.Chapter six, summary and prospect.This paper analyzes the innovation of this paper and the significance of the article to actual production, and points out the shortcomings of the article.
【学位授予单位】：浙江理工大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TH708;TP18

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