本文選題：同等并行機(jī)調(diào)度　切入點(diǎn)：離散差分進(jìn)化算法　出處：《華東理工大學(xué)》2012年碩士論文　論文類型：學(xué)位論文

【摘要】：生產(chǎn)調(diào)度的研究在過(guò)去幾十年中發(fā)展迅速,人們對(duì)調(diào)度問(wèn)題的模型和方法都做了大量的研究工作。由于許多實(shí)際調(diào)度問(wèn)題屬于NP完全問(wèn)題,經(jīng)典的調(diào)度理論和方法解決實(shí)際調(diào)度問(wèn)題仍然面臨各種難題。智能優(yōu)化調(diào)度方法是近年來(lái)興起的解決調(diào)度問(wèn)題簡(jiǎn)單有效的方法,這類方法在不需要復(fù)雜數(shù)學(xué)模型的情況下即可獲得較為滿意的調(diào)度方案,是解決實(shí)際調(diào)度的最有效的途徑之一 本文研究了同等并行機(jī)調(diào)度問(wèn)題,首先針對(duì)以制造期為目標(biāo)的并行機(jī)調(diào)度模型,在離散差分進(jìn)化算法(DDE)中融入變鄰域下降(VND)的局部搜索策略,提出了DDE_VND算法。由于此算法融合了DDE和VND的優(yōu)點(diǎn),因此提高了DDE算法的搜索性能和效率,通過(guò)對(duì)DDE_VND算法和DDE算法的測(cè)試,表明了VND的有效性。在仿真實(shí)驗(yàn)中,采用標(biāo)準(zhǔn)算例,在相同運(yùn)行條件下將DDE VND算法與DDE算法和遺傳算法進(jìn)行比較,實(shí)驗(yàn)結(jié)果表明了DDE_VND算法的效果更為顯著。 另外針對(duì)以總拖期為目標(biāo)的同等并行機(jī)調(diào)度問(wèn)題,本文對(duì)DDE_VND算法做了進(jìn)一步改進(jìn),在DDE_VND算法中加入了一種改進(jìn)的交貨期規(guī)則(MDD)用于種群的初始化階段,得到了改進(jìn)后的MDDE_VND算法。并通過(guò)幾個(gè)算例的驗(yàn)證和比較說(shuō)明了此規(guī)則的有效性。仿真實(shí)驗(yàn)中采用標(biāo)準(zhǔn)算例,在同等運(yùn)行條件下將MDDE_VND算法與DDE和克隆選擇粒子群(CSPSO)算法進(jìn)行了比較,達(dá)優(yōu)率以及進(jìn)化收斂曲線均體現(xiàn)了MDDE_VND算法的明顯優(yōu)勢(shì)。并且利用統(tǒng)計(jì)學(xué)的方差分析(ANOVA)方法對(duì)算法的參數(shù)設(shè)置進(jìn)行了討論,選取了較好的一組參數(shù)值用于仿真實(shí)驗(yàn)。
[Abstract]:The research of production scheduling has developed rapidly in the past few decades, and people have done a lot of research on the models and methods of scheduling problems, because many practical scheduling problems belong to NP-complete problems. Classical scheduling theory and methods are still faced with various problems in solving practical scheduling problems. Intelligent optimal scheduling method is a simple and effective method to solve scheduling problems which has emerged in recent years. This kind of method is one of the most effective ways to solve the problem of practical scheduling, because it can obtain a more satisfactory scheduling scheme without the need of complicated mathematical models. In this paper, the equivalent parallel machine scheduling problem is studied. Firstly, the local search strategy of variable neighborhood descent (VND) is incorporated into the discrete differential evolutionary algorithm (DDEA) for the parallel machine scheduling model with the manufacturing period as the target. DDE_VND algorithm is proposed. Because this algorithm combines the advantages of DDE and VND, it improves the search performance and efficiency of DDE algorithm. Through the test of DDE_VND algorithm and DDE algorithm, the effectiveness of VND is proved. Under the same running conditions, the DDE VND algorithm is compared with the DDE algorithm and the genetic algorithm. The experimental results show that the DDE_VND algorithm is more effective. In addition, for the same parallel machine scheduling problem with total tardiness, the DDE_VND algorithm is further improved, and an improved due date rule is added to the DDE_VND algorithm for the initialization phase of the population. The improved MDDE_VND algorithm is obtained, and the validity of the rule is verified and compared by several examples. A standard example is used in the simulation experiment. The MDDE_VND algorithm is compared with DDE and Clone selective Particle Swarm Optimization (CSP) algorithm under the same running conditions. The excellent rate and the evolutionary convergence curve reflect the obvious advantages of the MDDE_VND algorithm. The parameter setting of the algorithm is discussed by using the ANOVA method of statistics, and a good set of parameter values are selected for the simulation experiment.
【學(xué)位授予單位】：華東理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：TH186

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