改進的蟻群算法在硫化車間調(diào)度問題中的應用
發(fā)布時間：2018-08-06 13:50
【摘要】：輪胎制造行業(yè)是一個生產(chǎn)規(guī)模較大，資源和勞動力密集的行業(yè)，良好的生產(chǎn)計劃的制定對企業(yè)的生產(chǎn)過程和實際收益具有重大的意義。在輪胎生產(chǎn)中，硫化工序作為瓶頸工序，其調(diào)度計劃制定好壞直接影響整個輪胎生產(chǎn)流程的效率，，因此本文主要研究硫化車間中的生產(chǎn)調(diào)度問題。 首先，本文介紹了車間調(diào)度的研究現(xiàn)狀，包括研究的車間調(diào)度的分類、特點、研究方法和發(fā)展趨勢。 其次，本文根據(jù)硫化車間真實的生產(chǎn)情況，如各種約束條件和企業(yè)目標，提出并建立了硫化車間的數(shù)學模型。 再次，本文針對針對基于最小化最大完成時間的硫化車間調(diào)度問題的特點，同時為了克服蟻群算法易陷入局部最優(yōu)的缺點，提出了一種以硫化車間調(diào)度問題為背景的改進的蟻群算法。算法將遺傳算法融入到了蟻群算法的每次迭代過程中以加強算法的局部搜索能力，同時保持搜索解的多樣性；并利用蟻群算法正反饋的特性，加強整個算法的收斂速度，提高其求解效率。以硫化車間某生產(chǎn)小組為例，利用改進后的蟻群算法進行系統(tǒng)仿真，仿真結果結果與ACS算法、GAAA算法進行比較，證明本文提出的算法在求解質(zhì)量和收斂速度方面都更加有效。 然后，本文針對多目標硫化車間調(diào)度問題進行分析，并根據(jù)其特點對改進的蟻群算法的函數(shù)進行設計和改進，使之能夠滿足多目標的求解需求，通過系統(tǒng)仿真實驗驗證，本算法在求解質(zhì)量和收斂速度上都較MACS算法、MOGA算法更優(yōu)。 最后，本文針對動態(tài)不確定條件下的硫化車間生產(chǎn)調(diào)度問題，本文以硫化機故障為例，采用改進的蟻群算法和滾動再調(diào)度技術相結合的方式，成功解決了這類調(diào)度問題，仿真結果十分有效。
[Abstract]:The tire manufacturing industry is an industry with large production scale and intensive resources and labor force. The formulation of a good production plan is of great significance to the production process and actual income of the enterprise. In tire production, the vulcanization process is used as a bottleneck process, and its scheduling plan has a good effect on the efficiency of the whole tire production process. This paper mainly studies the production scheduling problem in vulcanizing workshop.
Firstly, this paper introduces the research status of job shop scheduling, including the classification, characteristics, research methods and development trend of job shop scheduling.
Secondly, according to the actual production situation of vulcanization workshop, such as various constraints and enterprise objectives, the mathematical model of vulcanization workshop is proposed and established.
Thirdly, aiming at the characteristics of the vulcanization shop scheduling problem based on minimizing the maximum completion time, and in order to overcome the disadvantage that the ant colony algorithm is easy to fall into the local optimum, an improved ant colony algorithm based on the scheduling problem of vulcanization workshop is proposed. The algorithm integrates the genetic algorithm into the iterative process of the ant colony algorithm. In order to strengthen the local search ability of the algorithm, and maintain the diversity of the search solution, and use the characteristics of the positive feedback of the ant colony algorithm, the convergence speed of the whole algorithm is strengthened and its efficiency is improved. The system simulation is carried out by the improved ant colony algorithm, the result of simulation results and the ACS algorithm, the GAAA algorithm. The comparison shows that the algorithm proposed in this paper is more effective in solving the quality and convergence speed.
Then, this paper analyzes the scheduling problem of multi-objective vulcanization shop, and designs and improves the function of the improved ant colony algorithm according to its characteristics so that it can meet the needs of multi target solution. Through the system simulation experiment, it is proved that the algorithm is better than the MACS algorithm and the MOGA algorithm in the solution quality and convergence speed.
Finally, this paper, aiming at the production scheduling problem of the vulcanization workshop under the dynamic uncertainty, uses the improved ant colony algorithm and the rolling re scheduling technique to solve the scheduling problem successfully, and the simulation results are very effective.
【學位授予單位】：青島科技大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP18;TB497
本文編號：2167927
[Abstract]:The tire manufacturing industry is an industry with large production scale and intensive resources and labor force. The formulation of a good production plan is of great significance to the production process and actual income of the enterprise. In tire production, the vulcanization process is used as a bottleneck process, and its scheduling plan has a good effect on the efficiency of the whole tire production process. This paper mainly studies the production scheduling problem in vulcanizing workshop.
Firstly, this paper introduces the research status of job shop scheduling, including the classification, characteristics, research methods and development trend of job shop scheduling.
Secondly, according to the actual production situation of vulcanization workshop, such as various constraints and enterprise objectives, the mathematical model of vulcanization workshop is proposed and established.
Thirdly, aiming at the characteristics of the vulcanization shop scheduling problem based on minimizing the maximum completion time, and in order to overcome the disadvantage that the ant colony algorithm is easy to fall into the local optimum, an improved ant colony algorithm based on the scheduling problem of vulcanization workshop is proposed. The algorithm integrates the genetic algorithm into the iterative process of the ant colony algorithm. In order to strengthen the local search ability of the algorithm, and maintain the diversity of the search solution, and use the characteristics of the positive feedback of the ant colony algorithm, the convergence speed of the whole algorithm is strengthened and its efficiency is improved. The system simulation is carried out by the improved ant colony algorithm, the result of simulation results and the ACS algorithm, the GAAA algorithm. The comparison shows that the algorithm proposed in this paper is more effective in solving the quality and convergence speed.
Then, this paper analyzes the scheduling problem of multi-objective vulcanization shop, and designs and improves the function of the improved ant colony algorithm according to its characteristics so that it can meet the needs of multi target solution. Through the system simulation experiment, it is proved that the algorithm is better than the MACS algorithm and the MOGA algorithm in the solution quality and convergence speed.
Finally, this paper, aiming at the production scheduling problem of the vulcanization workshop under the dynamic uncertainty, uses the improved ant colony algorithm and the rolling re scheduling technique to solve the scheduling problem successfully, and the simulation results are very effective.
【學位授予單位】：青島科技大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP18;TB497
【參考文獻】
相關期刊論文 前10條
1 丁建立,陳增強,袁著祉;遺傳算法與螞蟻算法的融合[J];計算機研究與發(fā)展;2003年09期
2 �？×�;郭西進;馬小平;;并行機成組調(diào)度問題的啟發(fā)式算法[J];計算機工程與應用;2007年04期
3 劉愛軍;楊育;邢青松;陸惠;張煜東;周振宇;吳光輝;趙小華;;柔性作業(yè)車間多目標動態(tài)調(diào)度[J];計算機集成制造系統(tǒng);2011年12期
4 魏唯;歐陽丹彤;呂帥;馮宇軒;;動態(tài)不確定環(huán)境下多目標路徑規(guī)劃方法[J];計算機學報;2011年05期
5 毛寧;顧軍華;譚慶;宋潔;;蟻群遺傳混合算法[J];計算機應用;2006年07期
6 肖曉偉;肖迪;林錦國;肖玉峰;;多目標優(yōu)化問題的研究概述[J];計算機應用研究;2011年03期
7 馬永杰;云文霞;;遺傳算法研究進展[J];計算機應用研究;2012年04期
8 方劍,席裕庚;基于遺傳算法的滾動調(diào)度策略[J];控制理論與應用;1997年04期
9 熊銳,吳澄;車間生產(chǎn)調(diào)度問題的技術現(xiàn)狀與發(fā)展趨勢[J];清華大學學報(自然科學版);1998年10期
10 公茂果;焦李成;楊咚咚;馬文萍;;進化多目標優(yōu)化算法研究[J];軟件學報;2009年02期
相關博士學位論文 前2條
1 高小永;基于多目標蟻群算法的土地利用優(yōu)化配置[D];武漢大學;2010年
2 張建明;基于改進量子進化算法的生產(chǎn)調(diào)度問題研究[D];華東理工大學;2013年
本文編號：2167927
本文鏈接：http://www.wukwdryxk.cn/guanlilunwen/gongchengguanli/2167927.html
最近更新
教材專著
