發(fā)布時間：2018-10-23 07:18

【摘要】：柔性流水車間調(diào)度問題(Flexible Flowshop Scheduling Problem, FFSP)屬于現(xiàn)實(shí)生產(chǎn)調(diào)度領(lǐng)域抽象出的簡化模型,該問題是并行機(jī)與排序問題的擴(kuò)展,它的主要特征是在某些工序或者全部工序上存在并行的機(jī)器,廣泛存在于流程制造業(yè)中,在企業(yè)生產(chǎn)管理中占有核心地位。企業(yè)只有根據(jù)需求利用合理的調(diào)度方案分配制造資源才能夠有效的提高生產(chǎn)效率,從而提高自身的競爭力。但是目前還沒有一個行之有效的智能算法來解決該問題,因此,對于該問題的智能算法研究具有十分重要的理論意義與現(xiàn)實(shí)價值。 遺傳算法(Genetic Algorithm)是自然選擇和遺傳學(xué)機(jī)理的生物進(jìn)化過程的計(jì)算模型,是一種通過模擬自然進(jìn)化過程搜索最優(yōu)解的方法。其特點(diǎn)是簡單通用、魯棒性強(qiáng)、適合于并行處理等,如今已作為一種新的被廣泛應(yīng)用的全局優(yōu)化智能搜索算法,尤其是在解決生產(chǎn)調(diào)度問題中取得了很大的突破。但是在算法發(fā)展的過程中,簡單的遺傳算法還有一些缺點(diǎn)存在,主要存在搜索效率低下、過早地收斂、容易陷入局部極值等等問題�；谝陨显�,需要對簡單的遺傳優(yōu)化算法進(jìn)行了改進(jìn),從而能夠應(yīng)用于實(shí)際的柔性流水車間調(diào)度問題求解。 本文主要做了以下工作：首先,針對目前國內(nèi)外車間調(diào)度的研究現(xiàn)狀以及存在的問題,本文對柔性流水車間調(diào)度問題進(jìn)行了詳細(xì)地研究。其次,以某鋁廠的生產(chǎn)車間的調(diào)度問題為原型提出了帶調(diào)整時間的柔性流水車間調(diào)度問題,并建立了數(shù)學(xué)模型。之后根據(jù)該數(shù)學(xué)模型的特點(diǎn)提出了一種基于三元矩陣的新型編碼方法,以最小生產(chǎn)時間為目標(biāo),對于傳統(tǒng)遺傳算法存在的收斂速度慢、易陷入局部最優(yōu)的缺點(diǎn)進(jìn)行了改進(jìn),設(shè)計(jì)了一種采用精英保留策略的自適應(yīng)的改進(jìn)遺傳算法,該算法改進(jìn)了交叉算子和變異算子,使其隨著適應(yīng)度函數(shù)的變化而自適應(yīng)變化從而提高了算法的效率。最后,對于本文提出的算法以一個實(shí)例進(jìn)行驗(yàn)證并與傳統(tǒng)遺傳算法進(jìn)行了對比,實(shí)驗(yàn)結(jié)果表明本文算法得到了一個較好的調(diào)度結(jié)果,同時無論是調(diào)度的結(jié)果還是算法的收斂速度上本文提出的算法都有明顯的優(yōu)越性。
[Abstract]:Flexible flow shop scheduling problem (Flexible Flowshop Scheduling Problem, FFSP) is an abstract simplified model in the field of real production scheduling. It is an extension of parallel machine and scheduling problem. Its main feature is that there are parallel machines in some or all processes. Widely exist in the process manufacturing industry, in the enterprise production management occupies the core position. Only when enterprises allocate manufacturing resources according to their needs can they improve their production efficiency and improve their competitiveness. However, there is not an effective intelligent algorithm to solve the problem, so the research of intelligent algorithm has very important theoretical significance and practical value. Genetic algorithm (Genetic Algorithm) is a computational model of biological evolutionary processes based on natural selection and genetic mechanism. It is a method to search for optimal solutions by simulating natural evolutionary processes. Its characteristics are simple and general, strong robustness, suitable for parallel processing, etc. It has been a new and widely used global optimization intelligent search algorithm, especially in the production scheduling problem has made a great breakthrough. However, in the process of the development of the algorithm, the simple genetic algorithm still has some shortcomings, such as low search efficiency, premature convergence, easy to fall into local extremum and so on. For the above reasons, it is necessary to improve the simple genetic optimization algorithm, so that it can be applied to the practical flexible flow shop scheduling problem. The main work of this paper is as follows: firstly, aiming at the present situation and existing problems of job shop scheduling at home and abroad, the flexible flow shop scheduling problem is studied in detail in this paper. Secondly, a flexible flow-shop scheduling problem with adjustment time is proposed based on the scheduling problem of a production shop in an aluminum plant, and a mathematical model is established. Then, according to the characteristics of the mathematical model, a new coding method based on ternary matrix is proposed. Aiming at the minimum production time, the traditional genetic algorithm has the disadvantages of slow convergence speed and easy to fall into local optimum. An adaptive genetic algorithm with elite reservation strategy is designed. The algorithm improves the crossover operator and mutation operator and makes them change adaptively with the change of fitness function so as to improve the efficiency of the algorithm. Finally, the proposed algorithm is verified by an example and compared with the traditional genetic algorithm. The experimental results show that the proposed algorithm has a better scheduling result. At the same time, both the scheduling results and the convergence rate of the algorithm presented in this paper have obvious advantages.
【學(xué)位授予單位】：廣東工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP18;TB497

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