本文選題：混合流水車間調(diào)度 + 資源約束�。� 參考：《電子科技大學(xué)》2012年碩士論文

【摘要】：本論文的研究基于四川省青年基金項(xiàng)目“面向迂回生產(chǎn)流程的多目標(biāo)生產(chǎn)規(guī)劃研究”（批準(zhǔn)號(hào)：09ZQ026-054）�；旌狭魉囬g調(diào)度(Hybrid Flow Shop Scheduling,HFSS)問題是兩類經(jīng)典調(diào)度問題Flow Shop和并Parallel Shop的推廣，相比之下，HFSS問題要求在整個(gè)加工流程中，至少有一個(gè)階段具有兩臺(tái)及以上的并行機(jī)器，屬于較為復(fù)雜的NP-Hard難題。此類問題至少需要同時(shí)解決兩個(gè)問題：各工件的加工順序以及各工件在各加工階段加工機(jī)器的分配情況。針對(duì)目前國內(nèi)外車間調(diào)度的研究現(xiàn)狀以及存在的實(shí)際問題，本文重點(diǎn)研究了考慮資源約束的混合流水車間調(diào)度問題以及具有預(yù)防性維修的資源約束混合流水車間調(diào)度問題。 針對(duì)混合流水車間稀缺資源約束的調(diào)度問題，將稀缺資源約束對(duì)最小化最大完工時(shí)間(makespan)的影響考慮到目標(biāo)函數(shù)中建立了基于資源約束的數(shù)學(xué)規(guī)劃模型，有效的解決了多臺(tái)并行機(jī)因競(jìng)爭稀缺資源而造成的時(shí)延等待、設(shè)備利用率低的調(diào)度問題。由于傳統(tǒng)遺傳算法存在早熟收斂的不足，提出采用NEH啟發(fā)式算法產(chǎn)生初始種群的改進(jìn)遺傳算法。實(shí)例仿真結(jié)果表明，在此模型下，改進(jìn)的遺傳算法較傳統(tǒng)遺傳算法能更好的解決有限稀缺資源約束的混合流水車間調(diào)度問題。 在生產(chǎn)過程中，由于需要定期對(duì)設(shè)備進(jìn)行預(yù)防性維修，因此需考慮設(shè)備預(yù)防性維修對(duì)車間調(diào)度的影響。本文在建考慮資源約束調(diào)度模型的基礎(chǔ)上，將預(yù)防性維修與稀缺資源等待對(duì)工序起始加工時(shí)間的影響考慮到目標(biāo)函數(shù)中，建立具有預(yù)防性維修的資源約束混合流水車間調(diào)度模型，該模型能夠滿足合理優(yōu)化配置有限資源以及定期對(duì)設(shè)備進(jìn)行預(yù)防性維修的同時(shí)，使得設(shè)備利用率最高，生產(chǎn)周期最短。為解決此類復(fù)雜約束調(diào)度問題，本文提出新的啟發(fā)式規(guī)則，結(jié)合遺傳算法對(duì)調(diào)度模型進(jìn)行求解。通過實(shí)例驗(yàn)證，改進(jìn)的算法能夠取得較好的調(diào)度方案。 同時(shí)，本文通過收集某風(fēng)電葉片制造廠實(shí)際車間數(shù)據(jù)，，對(duì)文中所提出的兩類調(diào)度模型分別與傳統(tǒng)調(diào)度模型作對(duì)比，通過計(jì)算各項(xiàng)調(diào)度指標(biāo)，驗(yàn)證了本文所建模型能夠很好的解決稀缺資源約束及統(tǒng)籌優(yōu)化設(shè)備預(yù)防性維修與調(diào)度的問題，達(dá)到提高資源利用率、縮短生產(chǎn)周期的目的。
[Abstract]:The research of this thesis is based on Sichuan Youth Foundation Project "Multi-objective production Planning for circuitous production process" (Grant No.: 09ZQ026-054). Hybrid flow shop scheduling (HFSS) problem is a generalization of two classical scheduling problems, flow Shop and parallel Shop. In contrast, HFSS problem requires that there are at least two parallel machines in one stage in the whole process. It belongs to the more complicated NP-Hard problem. This kind of problem needs to solve at least two problems simultaneously: the processing order of each workpiece and the distribution of each workpiece in each processing stage. In view of the current situation and practical problems of job shop scheduling at home and abroad, this paper focuses on the hybrid flow-shop scheduling problem with resource constraints and the resource-constrained hybrid flow-shop scheduling problem with preventive maintenance. In order to solve the scheduling problem of resource constraints in mixed flow shop, a mathematical programming model based on resource constraints is established by taking into account the influence of scarce resource constraints on minimizing the maximum completion time (makespan). It effectively solves the problem of delay waiting caused by competing for scarce resources and low utilization of equipment on many parallel machines. Because the traditional genetic algorithm has the deficiency of premature convergence, an improved genetic algorithm using NEH heuristic algorithm to generate initial population is proposed. The simulation results show that the improved genetic algorithm is better than the traditional genetic algorithm in solving the mixed flow shop scheduling problem with limited scarce resource constraints. In the process of production, the preventive maintenance of equipment is required, so it is necessary to consider the influence of preventive maintenance on workshop scheduling. Based on the resource constrained scheduling model, the effects of preventive maintenance and scarce resource waiting on the process starting processing time are taken into account in the objective function in this paper. A resource constrained hybrid flow shop scheduling model with preventive maintenance is established. The model can meet the requirements of rational allocation of limited resources and periodic preventive maintenance of the equipment, which makes the equipment utilization rate highest and the production cycle shortest. In order to solve this kind of complex constrained scheduling problem, a new heuristic rule is proposed in this paper, and a genetic algorithm is used to solve the scheduling model. An example shows that the improved algorithm can achieve a better scheduling scheme. At the same time, by collecting the actual workshop data of a wind turbine blade factory, the two kinds of scheduling models proposed in this paper are compared with the traditional scheduling models, and each scheduling index is calculated. It is verified that the proposed model can solve the problem of scarce resource constraints and optimize the preventive maintenance and scheduling of equipment as a whole to improve the utilization of resources and shorten the production cycle.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：TH186

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 寧維巍;雷毅;仲華惟;閆光榮;;無等待排列流水車間提前/延期調(diào)度問題[J];北京郵電大學(xué)學(xué)報(bào);2009年06期

2 馬佳;高立群;石剛;李丹;;求解柔性作業(yè)車間調(diào)度問題的免疫遺傳算法[J];東北大學(xué)學(xué)報(bào)(自然科學(xué)版);2008年07期

3 李波;吳志明;王強(qiáng);;多產(chǎn)品半柔性并聯(lián)生產(chǎn)線預(yù)防性維修調(diào)度模型研究[J];電子科技大學(xué)學(xué)報(bào);2011年05期

4 周炳海;蔣舒宇;王世進(jìn);吳斌;奚立峰;;集成生產(chǎn)與預(yù)防性維護(hù)的流水線車間調(diào)度算法[J];大連海事大學(xué)學(xué)報(bào);2007年03期

5 孫志峻,朱劍英;雙資源作業(yè)車間智能優(yōu)化調(diào)度[J];東南大學(xué)學(xué)報(bào)(自然科學(xué)版);2005年03期

6 曹政才;余紅霞;喬非;;基于Petri網(wǎng)與遺傳算法的半導(dǎo)體生產(chǎn)線建模與優(yōu)化調(diào)度[J];電子學(xué)報(bào);2010年02期

7 唐大志;;用Hopfield神經(jīng)網(wǎng)絡(luò)解決作業(yè)車間調(diào)度問題[J];遼寧工程技術(shù)大學(xué)學(xué)報(bào);2004年S1期

8 雷衛(wèi)東;車阿大;;帶時(shí)間窗口的自動(dòng)化混流生產(chǎn)線調(diào)度方法[J];工業(yè)工程;2011年05期

9 金玉蘭;蔣祖華;;預(yù)防性維修計(jì)劃和生產(chǎn)調(diào)度的多目標(biāo)優(yōu)化[J];哈爾濱工程大學(xué)學(xué)報(bào);2011年09期

10 劉曉霞;謝里陽;崔敬巍;;雙資源生產(chǎn)車間調(diào)度問題的研究[J];計(jì)算機(jī)工程與應(yīng)用;2007年06期

相關(guān)博士學(xué)位論文 前3條

1 曾強(qiáng);離散制造企業(yè)批量生產(chǎn)車間調(diào)度智能優(yōu)化研究[D];重慶大學(xué);2010年

2 張超勇;基于自然啟發(fā)式算法的作業(yè)車間調(diào)度問題理論與應(yīng)用研究[D];華中科技大學(xué);2007年

3 蔡蘭;制造業(yè)車間生產(chǎn)調(diào)度及其評(píng)估理論的研究和應(yīng)用[D];武漢理工大學(xué);2008年

相關(guān)碩士學(xué)位論文 前1條

1 劉捷;不確定條件下基于遺傳算法的柔性作業(yè)車間調(diào)度問題研究[D];華中科技大學(xué);2009年

本文編號(hào)：2112893