【摘要】：隨著能源價(jià)格的不斷上漲和環(huán)境問題的日益突出,傳統(tǒng)制造業(yè)的發(fā)展正受到能源成本和環(huán)境問題的雙重制約。輪胎制造業(yè)是高耗能、高污染的企業(yè),通過降低生產(chǎn)過程中的能源消耗來減少能耗成本是企業(yè)進(jìn)行成本控制的有效手段之一。生產(chǎn)調(diào)度作為生產(chǎn)管理的重要組成部分,是企業(yè)實(shí)現(xiàn)節(jié)能減排的潛在方向。針對輪胎密煉車間的機(jī)器調(diào)度優(yōu)化策略未考慮能耗因素的問題,建立了基于影響因子的能耗優(yōu)化模型,該模型將總完成時(shí)間和能耗成本作為構(gòu)成要素,以這兩個(gè)要素的綜合成本作為求解目標(biāo),同時(shí),加入影響因子表示生產(chǎn)中對時(shí)間和能耗成本的關(guān)注程度。對于已建立的能耗優(yōu)化模型,本文設(shè)計(jì)了一種改進(jìn)的自適應(yīng)遺傳算法(Another Adaptive Genetic Algorithm, AAGA)對調(diào)度優(yōu)化問題進(jìn)行求解。AAGA算法在分析“早熟”原因的基礎(chǔ)上,提出對每一代群體個(gè)體差異程度進(jìn)行評價(jià)的方法,然后根據(jù)該評價(jià)指標(biāo)在種群進(jìn)化過程中動(dòng)態(tài)地調(diào)整每一代群體的交叉和變異概率的上下限。同時(shí),每一代群體中個(gè)體的交叉和變異概率根據(jù)個(gè)體適應(yīng)性自適應(yīng)調(diào)整�；谏鲜鼋徊婧妥儺惒呗�,采用Tillard提供的流水車間數(shù)據(jù)集進(jìn)行實(shí)驗(yàn),實(shí)驗(yàn)結(jié)果表明,AAGA算法能夠搜索到更優(yōu)的解。最后,結(jié)合實(shí)際生產(chǎn)數(shù)據(jù),應(yīng)用上述所提算法對能耗優(yōu)化問題進(jìn)行求解,對比數(shù)據(jù)表明 AAGA 與 SGA (Simple Genetic Algorithm,SGA)和 AGA (Adaptive Genetic Algorithm,AGA)的應(yīng)用效果相比具有一定優(yōu)勢。進(jìn)一步,采用AAGA算法對基于影響因子的能耗優(yōu)化模型進(jìn)行驗(yàn)證,說明本文建立的能耗優(yōu)化模型對影響因子的不同取值,可以達(dá)到不同程度的節(jié)能效果。
[Abstract]:With the rising of energy price and the increasing of environmental problems, the development of traditional manufacturing industry is restricted by energy cost and environmental problems. Tire manufacturing industry is a high energy consumption and high pollution enterprise. It is one of the effective means to reduce the energy consumption cost by reducing the energy consumption in the production process. As an important part of production management, production scheduling is the potential direction for enterprises to achieve energy saving and emission reduction. Aiming at the problem that the energy consumption factor is not considered in the machine scheduling optimization strategy of tire mill workshop, the energy consumption optimization model based on the influence factor is established. The total completion time and energy consumption cost are taken as the constituent elements in the model. The comprehensive cost of these two elements is taken as the goal to solve the problem. At the same time, the influence factor is added to indicate the degree of attention to the cost of time and energy consumption in production. For the established energy consumption optimization model, an improved adaptive genetic algorithm (Another Adaptive Genetic Algorithm, AAGA) is designed to solve the scheduling optimization problem. The AAGA algorithm is based on the analysis of the causes of precocity. A method for evaluating the degree of individual difference in each generation is proposed, and then the upper and lower limits of crossover and variation probability of each generation are dynamically adjusted according to the evaluation index during the evolution of the population. At the same time, the crossover and mutation probability of each generation is adaptively adjusted according to individual adaptability. Based on the above crossover and mutation strategies, the flow shop data set provided by Tillard is used to experiment. The experimental results show that the AAGA algorithm can find a better solution. Finally, the energy consumption optimization problem is solved by using the proposed algorithm based on the actual production data. The comparison data show that the application effect of AAGA, SGA (Simple Genetic Algorithm,SGA) and AGA (Adaptive Genetic Algorithm,AGA) has some advantages. Furthermore, the AAGA algorithm is used to verify the energy consumption optimization model based on the influence factor, which shows that the energy consumption optimization model established in this paper can achieve different energy saving effects.
【學(xué)位授予單位】：西安理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TQ330.8;TP18

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