本文選題：價(jià)值流 + 生產(chǎn)線平衡�。� 參考：《浙江理工大學(xué)》2017年碩士論文

【摘要】：本文的研究?jī)?nèi)容是在H公司“生產(chǎn)線流程改善與車間設(shè)施規(guī)劃分析”的項(xiàng)目的背景下進(jìn)行,論文主要以價(jià)值流為基礎(chǔ),進(jìn)行生產(chǎn)線平衡的研究,在現(xiàn)狀價(jià)值流圖的基礎(chǔ)上發(fā)現(xiàn)了車間裝配生產(chǎn)線上的一些問題,并根據(jù)目標(biāo)要求進(jìn)行了數(shù)學(xué)模型的建立和求解,再繪制未來價(jià)值流程圖,通過實(shí)施來減少公司的浪費(fèi)及其他的相關(guān)問題。本文在計(jì)算機(jī)優(yōu)化仿真的背景下,對(duì)裝配生產(chǎn)線的平衡問題進(jìn)行仿真求解,考慮采用粒子群優(yōu)化算法并引入遺傳算法的優(yōu)勢(shì)進(jìn)行求解。粒子群算法也被稱作粒子群優(yōu)化算法,英文名為Particle Swarm Optimization,縮寫為PSO,其來源是模擬鳥群的覓食行為,是一種以群體協(xié)作為基礎(chǔ)的隨機(jī)搜索算法,一般也被認(rèn)為是一種群體智能。粒子群算法其優(yōu)化基礎(chǔ)是迭代的方法,最初的系統(tǒng)有一組隨機(jī)解,不斷通過迭代的方法來計(jì)算最優(yōu)解,是其他粒子在一個(gè)特定的空間中追隨著最優(yōu)的粒子進(jìn)行搜索最優(yōu)解,其優(yōu)勢(shì)也是非常的明顯,當(dāng)在動(dòng)態(tài)的目標(biāo)或者連續(xù)不斷的多維空間里會(huì)體現(xiàn)出質(zhì)量高、速度很快、魯棒性較好的特性。但是生產(chǎn)線平衡具有離散性的特點(diǎn),而粒子群算法沒有過于復(fù)雜的編碼和其他變異的操作過程,非常有可能得不到一個(gè)最優(yōu)解。針對(duì)粒子群算法在裝配生產(chǎn)線平衡方面的問題,本文的解決方法是考慮通過引進(jìn)遺傳算法,因?yàn)檫z傳算法就是為了解決在傳統(tǒng)數(shù)學(xué)方法不能有效快速的求出相對(duì)大規(guī)模的復(fù)雜難題,也具有特定的優(yōu)勢(shì),比如遺傳算法是隨機(jī)、迭代的并且是不厭其煩的搜尋目標(biāo)的最優(yōu)解。所以綜合考慮,引入遺傳算法之后,基本上可以解決粒子群算法在離散性上的問題。第一章,文章緒論。介紹了本文研究的相關(guān)背景和研究意義,分析了價(jià)值流圖析技術(shù)和生產(chǎn)線平衡問題的國(guó)內(nèi)外的研究現(xiàn)狀并分析了今后的發(fā)展趨勢(shì),闡述了論文的相關(guān)內(nèi)容及體系結(jié)構(gòu)的安排。第二章,將與論文相關(guān)的理論知識(shí)進(jìn)行了詳細(xì)的介紹,包括價(jià)值流圖析技術(shù)與精益生產(chǎn)領(lǐng)域的生產(chǎn)平衡問題的計(jì)算和相關(guān)分析。第三章,根據(jù)在現(xiàn)場(chǎng)的測(cè)量和記錄數(shù)據(jù),結(jié)合H公司DTZ545型儀表的實(shí)際情況,繪制了車間布局圖和車間物流圖,并繪制了產(chǎn)品工藝圖,進(jìn)行了數(shù)據(jù)的收集,以收集到的數(shù)據(jù)為基礎(chǔ),繪制了價(jià)值流現(xiàn)狀圖,并根據(jù)現(xiàn)狀圖分析了生產(chǎn)線存在的問題。第四章,對(duì)生產(chǎn)線平衡問題進(jìn)行了數(shù)學(xué)建模和仿真優(yōu)化,分析了粒子群優(yōu)化算法的原理及其優(yōu)缺點(diǎn),并對(duì)其在生產(chǎn)線平衡問題上的應(yīng)用進(jìn)行了討論,根據(jù)算法特點(diǎn),考慮引入遺傳算法對(duì)缺點(diǎn)進(jìn)行改進(jìn)。第五章,模型建立后進(jìn)行仿真,通過算法在Mat lab中進(jìn)行求解,根據(jù)求解的結(jié)果可以繪制出價(jià)值流的未來狀態(tài)圖,并進(jìn)行實(shí)施,來實(shí)現(xiàn)未來的價(jià)值流。第六章,總結(jié)和展望。分析了本文的創(chuàng)新點(diǎn)和文章對(duì)實(shí)際生產(chǎn)的意義,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.
【學(xué)位授予單位】：浙江理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TH708;TP18

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