基于遍历搜索与遗传算法的L公司生产线平衡研究

发布时间：2018-06-08 07:09

本文选题：生产线平衡 + Arena仿真　；参考：《兰州理工大学》2017年硕士论文

【摘要】：生产线各工作站间负荷的不平衡,严重影响着生产线效率、设备使用率以及生产成本,对企业效益和效能的提高有着重大影响,因此研究生产线平衡问题,对制造企业具有十分重要的意义。本文研究了L公司两条典型的生产线。针对生产线存在的现实问题设计并实现了快速有效的算法,优化了生产线,提高了生产线的生产能力。首先,本文对解决生产线平衡问题所需要的理论和方法进行详细介绍,对L公司生产线现状做出分析,运用Arena仿真软件对B生产线瓶颈工位的生产能力、设备利用率以及工作人员疲劳强度等进行验证并改善。记录了生产线各工序的加工时间,遵照流程图绘制工序间先后关系约束图。建立了生产线平衡数学模型,并建立适应度函数,为解决生产线平衡问题奠定基础。其次,运用C语言编程实现了遍历搜索算法,用以对A生产线平衡问题进行研究。由于A生产线工序数量较少,工序关系不太复杂,可行的作业排序数量有限,本文运用遍历搜索算法将生产线上所有可行的作业排序全部搜索出来,随后逐一检验是否为最优的作业排序方案,最终把最优的方案查找出来。该算法准确性高,平衡效果显著。然后,运用C语言编程实现了遗传算法,用以对B生产线平衡问题进行研究。对于复杂的B生产线,在优化求解过程中存在的潜在解数量巨大,遍历搜索算法在短时间内不能全部搜索出所有可行的作业排序。本文阐述了应用遗传算法进行生产线平衡优化的求解过程。首先,应用遍历搜索算法,搜出部分可行的作业排序,然后从中随机选出一部分作为遗传算法的初始种群。为了证明求得的解的可靠性,本文设计的算法中的种群规模、迭代次数以及变异概率等值都可以改变,从而观察计算的结果是否收敛。最后,本文分别运用遍历搜索算法和遗传算法对A、B两条生产线进行了平衡优化。由优化结果可知,A生产线的平衡率由最初的51%提高到90%的较优水平,B生产线的平衡率由最初的67%提高的92%的较优水平。本文通过设计和实现两种优化算法解决了L公司生产线的平衡问题,提高了生产线的生产效率,降低了L公司制造成本。由于计算机技术优化和遗传算法都是普适性的技术,因此,本论文所采用的方法和技术也具有一定的现实意义。
[Abstract]:The imbalance of load among workstations in production line seriously affects the efficiency of production line, the utilization rate of equipment and the production cost, and has a great impact on the improvement of enterprise efficiency and efficiency. Therefore, the problem of production line balance is studied. It is of great significance to manufacturing enterprises. This paper studies two typical production lines of L Company. A fast and effective algorithm is designed and implemented to solve the practical problems in the production line. The production line is optimized and the production capacity is improved. First of all, this paper introduces the theory and method needed to solve the problem of production line balance in detail, analyzes the present situation of production line of L Company, and applies Arena simulation software to the production capacity of bottleneck position of production line B. Equipment utilization and staff fatigue strength are verified and improved. The processing time of each production line is recorded and the relationship between the processes is drawn according to the flowchart. The mathematical model of production line equilibrium is established and the fitness function is established, which lays a foundation for solving the problem of production line balance. Secondly, the ergodic search algorithm is realized by C language programming, which is used to study the balance problem of A production line. Due to the small number of processes in production line A, the process relationship is not too complex, and the number of feasible job ranking is limited, this paper uses the traversal search algorithm to search all feasible jobs on the production line. Then the optimal scheduling scheme is checked one by one, and the optimal scheme is finally found out. The accuracy of the algorithm is high and the balance effect is remarkable. Then, the genetic algorithm is implemented by C language, which is used to study the balance problem of B production line. For complex B production line, the number of potential solutions in the optimization process is huge, and the traversal search algorithm can not search all feasible job order in a short time. In this paper, genetic algorithm is used to solve the balance optimization of production line. First, the traversal search algorithm is used to search out some feasible job order, and then a part of the genetic algorithm is randomly selected as the initial population of the genetic algorithm. In order to prove the reliability of the obtained solution, the population size, iteration times and mutation probability equivalence of the proposed algorithm can be changed, and the convergence of the calculated results can be observed. Finally, the ergodic search algorithm and genetic algorithm are used to optimize the balance between the two production lines. The results of optimization show that the equilibrium rate of production line A is increased from 51% to 90%, and the balance rate of production line B increases from 67% to 92%. In this paper, two optimization algorithms are designed and implemented to solve the balance problem of L Company's production line, improve the production efficiency of the production line, and reduce the manufacturing cost of L Company. Because computer technology optimization and genetic algorithm are universal techniques, the methods and techniques used in this paper also have some practical significance.
【学位授予单位】：兰州理工大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP18;F273;F426

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