本文選題：生產(chǎn)線平衡 + Arena仿真　； 參考：《蘭州理工大學(xué)》2017年碩士論文

【摘要】：生產(chǎn)線各工作站間負(fù)荷的不平衡,嚴(yán)重影響著生產(chǎn)線效率、設(shè)備使用率以及生產(chǎn)成本,對(duì)企業(yè)效益和效能的提高有著重大影響,因此研究生產(chǎn)線平衡問(wèn)題,對(duì)制造企業(yè)具有十分重要的意義。本文研究了L公司兩條典型的生產(chǎn)線。針對(duì)生產(chǎn)線存在的現(xiàn)實(shí)問(wèn)題設(shè)計(jì)并實(shí)現(xiàn)了快速有效的算法,優(yōu)化了生產(chǎn)線,提高了生產(chǎn)線的生產(chǎn)能力。首先,本文對(duì)解決生產(chǎn)線平衡問(wèn)題所需要的理論和方法進(jìn)行詳細(xì)介紹,對(duì)L公司生產(chǎn)線現(xiàn)狀做出分析,運(yùn)用Arena仿真軟件對(duì)B生產(chǎn)線瓶頸工位的生產(chǎn)能力、設(shè)備利用率以及工作人員疲勞強(qiáng)度等進(jìn)行驗(yàn)證并改善。記錄了生產(chǎn)線各工序的加工時(shí)間,遵照流程圖繪制工序間先后關(guān)系約束圖。建立了生產(chǎn)線平衡數(shù)學(xué)模型,并建立適應(yīng)度函數(shù),為解決生產(chǎn)線平衡問(wèn)題奠定基礎(chǔ)。其次,運(yùn)用C語(yǔ)言編程實(shí)現(xiàn)了遍歷搜索算法,用以對(duì)A生產(chǎn)線平衡問(wèn)題進(jìn)行研究。由于A生產(chǎn)線工序數(shù)量較少,工序關(guān)系不太復(fù)雜,可行的作業(yè)排序數(shù)量有限,本文運(yùn)用遍歷搜索算法將生產(chǎn)線上所有可行的作業(yè)排序全部搜索出來(lái),隨后逐一檢驗(yàn)是否為最優(yōu)的作業(yè)排序方案,最終把最優(yōu)的方案查找出來(lái)。該算法準(zhǔn)確性高,平衡效果顯著。然后,運(yùn)用C語(yǔ)言編程實(shí)現(xiàn)了遺傳算法,用以對(duì)B生產(chǎn)線平衡問(wèn)題進(jìn)行研究。對(duì)于復(fù)雜的B生產(chǎn)線,在優(yōu)化求解過(guò)程中存在的潛在解數(shù)量巨大,遍歷搜索算法在短時(shí)間內(nèi)不能全部搜索出所有可行的作業(yè)排序。本文闡述了應(yīng)用遺傳算法進(jìn)行生產(chǎn)線平衡優(yōu)化的求解過(guò)程。首先,應(yīng)用遍歷搜索算法,搜出部分可行的作業(yè)排序,然后從中隨機(jī)選出一部分作為遺傳算法的初始種群。為了證明求得的解的可靠性,本文設(shè)計(jì)的算法中的種群規(guī)模、迭代次數(shù)以及變異概率等值都可以改變,從而觀察計(jì)算的結(jié)果是否收斂。最后,本文分別運(yùn)用遍歷搜索算法和遺傳算法對(duì)A、B兩條生產(chǎn)線進(jìn)行了平衡優(yōu)化。由優(yōu)化結(jié)果可知,A生產(chǎn)線的平衡率由最初的51%提高到90%的較優(yōu)水平,B生產(chǎn)線的平衡率由最初的67%提高的92%的較優(yōu)水平。本文通過(guò)設(shè)計(jì)和實(shí)現(xiàn)兩種優(yōu)化算法解決了L公司生產(chǎn)線的平衡問(wèn)題,提高了生產(chǎn)線的生產(chǎn)效率,降低了L公司制造成本。由于計(jì)算機(jī)技術(shù)優(yōu)化和遺傳算法都是普適性的技術(shù),因此,本論文所采用的方法和技術(shù)也具有一定的現(xiàn)實(shí)意義。
[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.
【學(xué)位授予單位】：蘭州理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP18;F273;F426

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本文編號(hào)：1995058