本文關(guān)鍵詞： 制造執(zhí)行系統(tǒng) 作業(yè)車間調(diào)度 遺傳算法 混合遺傳算法　出處：《長春工業(yè)大學(xué)》2012年碩士論文　論文類型：學(xué)位論文

【摘要】：當(dāng)今時代,自動化控制和計算機(jī)技術(shù)發(fā)展飛速,制造執(zhí)行系統(tǒng)由于其能較大程度的優(yōu)化利用企業(yè)資源,以及大大提高企業(yè)生產(chǎn)效率方面的優(yōu)越性,使其被越來越多的企業(yè)所認(rèn)可。對于柔性作業(yè)車間這樣一個資源使用情況復(fù)雜、事件離散且動態(tài)變化的系統(tǒng),調(diào)度問題就顯得非常重要。優(yōu)秀的調(diào)度計劃在提高生產(chǎn)效率和降低產(chǎn)品成本兩個方面起著非常重要的作用,正是因為如此,也使得車間調(diào)度問題越來越受到人們的關(guān)注,這也是本文研究意義之所在。本文首先對遺傳算法的理論知識進(jìn)行了研究,并在遺傳算法的思想基礎(chǔ)上設(shè)計了基于vc++的遺傳算法程序平臺；并基于所設(shè)計的遺傳算法平臺求解了FT06(6x6)和FT10(10x10)兩個Benchmark問題,并與基于啟發(fā)式規(guī)則的啟發(fā)式算法進(jìn)行比較,得到比較理想的效果,也印證了本文所設(shè)計的遺傳算法程序的有效性。最后針對柔性制造車間的問題,本文以159廠的一期工程為背景,分析和研究了制造執(zhí)行系統(tǒng)(MES)功能結(jié)構(gòu),針對該廠柔性制造車間的車間調(diào)度方面問題開展了工作,并基于本文設(shè)計的基于機(jī)器最短加工時間的混合遺傳算法,求解了該工程問題,最終得出可用于指導(dǎo)生產(chǎn)調(diào)度的Gantt圖。本文是基于車間的靜態(tài)調(diào)度的基礎(chǔ)上進(jìn)行研究的,許多突發(fā)的、不確定的隨機(jī)因素都不再調(diào)度范圍之內(nèi)。 本文將機(jī)器最短加工時間的概念融入算法中,針對柔性車間出現(xiàn)的某一工序多臺可選擇機(jī)床的情況,使用機(jī)器最短加工時間的啟發(fā)式方法來選定機(jī)床,可實施解的局部優(yōu)化,大大減少了遺傳算法的搜索空間,并以此提高算法的效率與染色體個體的質(zhì)量。將標(biāo)準(zhǔn)遺傳算法與基于機(jī)器最短加工時間的啟發(fā)式方法相互結(jié)合,計算所得到的生產(chǎn)周期要遠(yuǎn)低于標(biāo)準(zhǔn)遺傳算法。由此可見,混合遺傳算法充分體現(xiàn)了其探索解空間的能力,由此也證明了基于機(jī)器最短加工時間的混合遺傳算法充分地結(jié)合啟發(fā)式方法與遺傳算法的優(yōu)勢。
[Abstract]:Nowadays, with the rapid development of automation control and computer technology, manufacturing execution system can optimize the utilization of enterprise resources to a large extent and greatly improve the production efficiency of enterprises. It is recognized by more and more enterprises. For a system with complex resource usage, discrete events and dynamic change, flexible job shop, Scheduling problem is very important. Excellent scheduling plan plays a very important role in improving production efficiency and reducing product cost. Because of this, job shop scheduling problem has attracted more and more attention. This is also the significance of this study. Firstly, the theoretical knowledge of genetic algorithm is studied, and based on the idea of genetic algorithm, a genetic algorithm program platform based on VC is designed. Based on the designed genetic algorithm platform, two Benchmark problems, FT06X6) and FT1010x10), are solved, and compared with heuristic algorithms based on heuristic rules, the results are satisfactory. It also proves the validity of the genetic algorithm program designed in this paper. Finally, aiming at the problem of flexible manufacturing workshop, this paper analyzes and studies the functional structure of manufacturing execution system (mes) based on the first phase project of 159 factory. Based on the hybrid genetic algorithm designed in this paper, the engineering problem is solved based on the hybrid genetic algorithm based on the minimum machining time of the machine. Finally, the Gantt diagram which can be used to guide production scheduling is obtained. This paper is based on the static scheduling of the job shop. Many sudden and uncertain random factors are no longer within the scope of scheduling. In this paper, the concept of the shortest machining time of the machine is integrated into the algorithm, and the heuristic method of the shortest processing time of the machine is used to select the machine tool, aiming at the situation of a flexible workshop in which several machine tools can be selected in a certain process, and the local optimization of the solution can be implemented. The search space of genetic algorithm is greatly reduced, and the efficiency of the algorithm and the quality of chromosome individuals are improved. The standard genetic algorithm is combined with the heuristic method based on the shortest processing time of the machine. The production cycle obtained by the calculation is much lower than that of the standard genetic algorithm. Thus, the hybrid genetic algorithm fully embodies its ability to explore the solution space. It is also proved that the hybrid genetic algorithm based on the shortest processing time of the machine fully combines the advantages of the heuristic method and the genetic algorithm.
【學(xué)位授予單位】：長春工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：TH166;TP18

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 張s，

本文編號：1536417