半導(dǎo)體生產(chǎn)線動(dòng)態(tài)維護(hù)策略研究
[Abstract]:Semiconductor production line has complex structure, typical reentry characteristics, various kinds of processed products, high integration of equipment and high cost. In recent years, semiconductor manufacturing industry has developed rapidly and the competition is fierce. Reasonable maintenance strategy can maximize the value of equipment, bring higher profits and enhance the market competitiveness of enterprises. In this paper, the Markov decision process MDP (markov decision process) model of equipment production and maintenance system is established. Considering the variable maintenance behavior selection and random state transition, the dynamic maintenance strategy of semiconductor production line equipment is studied, and the maintenance time and behavior synthesis to maximize the benefit are obtained. This dynamic maintenance strategy is applied to a typical semiconductor manufacturing process-Mini-Fab model to further study the scheduling rules of each unit in the model. The optimal dynamic maintenance strategy is obtained by optimizing the coupling problem between manufacturing and maintenance. First of all, this paper introduces the research background and significance, analyzes the domestic and foreign research status from industry and academia, and details the research content and theoretical framework. Secondly, the related theories of reliability analysis and maintenance research, as well as the types and judgment indexes of production scheduling are expounded, which provide theoretical support for the subsequent research. Then, considering variable maintenance behavior selection and random state transition probability, a mathematical model of dynamic maintenance strategy for semiconductor devices based on MDP model is established. The MDP model is used to simulate the state transition in the process of equipment maintenance, and the comprehensive scheme about the maintenance time and behavior is obtained by taking the benefit of the equipment as the objective function. Based on the characteristics of the established mathematical model, the particle swarm optimization algorithm with genetic crossover factor is introduced, and the optimal dynamic maintenance strategy is obtained. Finally, the dynamic maintenance strategy is applied to the Mini-Fab model, and further considering the production process of the system, the problem of joint selection in the conflict between maintenance and processing and the optimal scheduling rules for each unit of the system are studied. Thus, the integrated strategy of production scheduling and dynamic maintenance scheme is obtained.
【學(xué)位授予單位】:廣東工業(yè)大學(xué)
【學(xué)位級(jí)別】:碩士
【學(xué)位授予年份】:2017
【分類號(hào)】:TN305;TP18
【參考文獻(xiàn)】
相關(guān)期刊論文 前10條
1 王松;劉祚時(shí);羅金平;康維星;;威布爾分布下的預(yù)防維修模型在產(chǎn)品生產(chǎn)期內(nèi)的應(yīng)用[J];價(jià)值工程;2017年05期
2 梅海濤;華繼學(xué);王毅;文童;;基于直覺(jué)模糊支配的混合多目標(biāo)粒子群算法[J];計(jì)算機(jī)科學(xué);2017年01期
3 陳光宇;張文;張小民;;威布爾分布下系統(tǒng)全壽命周期成本建模與決策[J];系統(tǒng)工程理論與實(shí)踐;2016年11期
4 李瑞秋;馬慧民;;半導(dǎo)體可重入作業(yè)車間調(diào)度與設(shè)備預(yù)維修聯(lián)合優(yōu)化研究[J];物流工程與管理;2016年11期
5 向懷坤;李偉龍;謝秉磊;;粒子群優(yōu)化神經(jīng)網(wǎng)絡(luò)的交通事件檢測(cè)算法研究[J];計(jì)算機(jī)測(cè)量與控制;2016年02期
6 敖銀輝;常鵬;;基于隨機(jī)衰退過(guò)程的生產(chǎn)線動(dòng)態(tài)維護(hù)建模[J];工業(yè)工程;2015年04期
7 于燮康;;集成電路產(chǎn)業(yè)技術(shù)發(fā)展趨勢(shì)與突破路徑[J];中國(guó)工業(yè)評(píng)論;2015年08期
8 賈文友;江志斌;李友;;面向產(chǎn)品族優(yōu)化時(shí)間窗下可重入批處理機(jī)調(diào)度[J];機(jī)械工程學(xué)報(bào);2015年12期
9 綦法群;周炳海;;基于Markov過(guò)程的集束型設(shè)備預(yù)防維護(hù)策略[J];上海交通大學(xué)學(xué)報(bào);2014年10期
10 楊智;黃學(xué)衛(wèi);黃松華;;基于蒙特卡羅的復(fù)雜可修系統(tǒng)預(yù)防性維修周期決策[J];裝甲兵工程學(xué)院學(xué)報(bào);2014年01期
相關(guān)重要報(bào)紙文章 前1條
1 于燮康;;兼并重組為半導(dǎo)體后來(lái)者提供切入點(diǎn)[N];中國(guó)電子報(bào);2015年
相關(guān)博士學(xué)位論文 前2條
1 侯文瑞;有限時(shí)間區(qū)間內(nèi)制造系統(tǒng)預(yù)防性機(jī)會(huì)維護(hù)策略研究[D];上海交通大學(xué);2014年
2 杜冰;批處理機(jī)調(diào)度問(wèn)題的模型與優(yōu)化方法研究[D];中國(guó)科學(xué)技術(shù)大學(xué);2011年
相關(guān)碩士學(xué)位論文 前10條
1 劉華華;多種資源約束下集成電路芯片最終測(cè)試生產(chǎn)調(diào)度優(yōu)化方法研究[D];西南交通大學(xué);2015年
2 馬紅偉;粒子群算法改進(jìn)及其在數(shù)據(jù)挖掘中的應(yīng)用研究[D];山東師范大學(xué);2014年
3 任志偉;面向數(shù)據(jù)驅(qū)動(dòng)建模的數(shù)據(jù)預(yù)處理方法研究[D];河南科技大學(xué);2013年
4 侯錚亮;產(chǎn)能約束下半導(dǎo)體芯片測(cè)試生產(chǎn)線調(diào)度優(yōu)化研究[D];西南交通大學(xué);2013年
5 張瓊芳;基于批決策的半導(dǎo)體生產(chǎn)線調(diào)度問(wèn)題研究[D];武漢理工大學(xué);2012年
6 紀(jì)雪玲;改進(jìn)粒子群優(yōu)化算法及其在人工神經(jīng)網(wǎng)絡(luò)中的應(yīng)用研究[D];西南林業(yè)大學(xué);2012年
7 楊國(guó)朋;半導(dǎo)體芯片最終測(cè)試多階段可重入調(diào)度優(yōu)化仿真研究[D];西南交通大學(xué);2012年
8 王強(qiáng);半導(dǎo)體并聯(lián)生產(chǎn)線預(yù)防性維修調(diào)度和評(píng)估系統(tǒng)的設(shè)計(jì)與實(shí)現(xiàn)[D];電子科技大學(xué);2010年
9 吳洪飛;基于非齊次馬爾可夫過(guò)程的多動(dòng)作動(dòng)態(tài)維護(hù)策略研究[D];上海交通大學(xué);2008年
10 張頌;半導(dǎo)體生產(chǎn)線預(yù)防性維修周期的研究[D];同濟(jì)大學(xué);2007年
,本文編號(hào):2165209
本文鏈接:http://www.sikaile.net/kejilunwen/dianzigongchenglunwen/2165209.html