本文關(guān)鍵詞： 柔性車間調(diào)度問題 多目標(biāo)優(yōu)化改進(jìn) 遺傳算法 指數(shù)分布　出處：《天津大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：在全球發(fā)展深化的趨勢(shì)下,我國制造業(yè)正在面臨著巨大的沖擊,所以提高生產(chǎn)效率以及資源利用率,促進(jìn)轉(zhuǎn)型升級(jí)已經(jīng)成為制造業(yè)發(fā)展的必然趨勢(shì)。其中柔性車間調(diào)度問題(FJSP)是該領(lǐng)域的經(jīng)典問題,而多目標(biāo)柔性車間調(diào)度問題在調(diào)度時(shí)要衡量各個(gè)目標(biāo),增加了調(diào)度的難度,是原有FJSP的深化。此類研究已經(jīng)成為了制造業(yè)的重點(diǎn)問題。在現(xiàn)有傳統(tǒng)調(diào)度問題的研究中,一般認(rèn)為工件加工時(shí)間是定值,但是結(jié)合生產(chǎn)實(shí)際情況來看,加工時(shí)間往往是波動(dòng)的,而不可能是單一的定值。所以本文在詳細(xì)文獻(xiàn)綜述的基礎(chǔ)之上,擬提出一種改進(jìn)的遺傳算法來解決此問題,以優(yōu)化現(xiàn)有算法。本文的研究將基于加工時(shí)間服從指數(shù)分布假設(shè)的多目標(biāo)柔性車間調(diào)度問題為對(duì)象,旨在平衡機(jī)臺(tái)負(fù)荷和減小訂單延誤時(shí)間。在研究過程中將OOC編碼與RC編碼技術(shù)結(jié)合用于產(chǎn)生初始化群體,在保證種群多樣性的同時(shí)提高算法的效率。同時(shí)考慮到遺傳算法本身的局限性,采用線性變換,適應(yīng)度變換,自適應(yīng)交叉、變異,使遺傳算法前期保持種群的多樣性,避免早熟,后期提高搜索的效率,更快找到最優(yōu)解。經(jīng)過算法驗(yàn)證,本文提出的改進(jìn)遺傳算法能很好地實(shí)現(xiàn)柔性車間調(diào)度問題的多目標(biāo)優(yōu)化,并在算法性能以及算法收斂速度方面均優(yōu)于自適應(yīng)遺傳算法與標(biāo)準(zhǔn)遺傳算法,有效的優(yōu)化了遺傳算法,對(duì)制造業(yè)實(shí)踐的生產(chǎn)活動(dòng)具有一定的指導(dǎo)意義。
[Abstract]:Under the trend of global development, China's manufacturing industry is facing a huge impact, so improve production efficiency and resource utilization. Promotion of transformation and upgrading has become an inevitable trend in the development of manufacturing industry, in which flexible shop scheduling problem (FJSP) is a classic problem in this field. The multi-objective flexible job shop scheduling problem has to measure each target in scheduling, which increases the difficulty of scheduling. It is the deepening of the original FJSP. This kind of research has become the key problem in the manufacturing industry. In the existing research of traditional scheduling problem, it is generally considered that the processing time of the workpiece is a fixed value, but according to the actual situation of production. Processing time is often fluctuating, but can not be a single fixed value. Therefore, based on the detailed literature review, this paper proposes an improved genetic algorithm to solve this problem. In order to optimize the existing algorithms, the research of this paper will be based on the hypothesis of processing time from the exponential distribution of multi-objective flexible job shop scheduling problem as an object. In order to balance the load of the machine and reduce the delay time of the order, the OOC coding and RC coding technology are combined to generate initialization group in the research process. Considering the limitations of genetic algorithm, linear transformation, fitness transformation, adaptive crossover and mutation are adopted. The genetic algorithm can keep the diversity of population, avoid precocity, improve the efficiency of search and find the optimal solution more quickly. The improved genetic algorithm proposed in this paper can achieve multi-objective optimization of flexible job shop scheduling problem, and is superior to adaptive genetic algorithm and standard genetic algorithm in the performance and convergence speed of the algorithm. The genetic algorithm is optimized effectively, which has a certain guiding significance to the production activities of manufacturing practice.
【學(xué)位授予單位】：天津大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TB497;TP18

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