發(fā)布時(shí)間：2018-08-06 15:10

【摘要】：車(chē)間調(diào)度涉及企業(yè)的生產(chǎn)計(jì)劃、采購(gòu)、倉(cāng)庫(kù)、銷(xiāo)售等運(yùn)作管理,作為生產(chǎn)系統(tǒng)的核心,車(chē)間調(diào)度方案的優(yōu)化可以提高生產(chǎn)效益和設(shè)備利用率。由于產(chǎn)品趨于個(gè)性化,工藝路線(xiàn)更加多樣化,迫切需要企業(yè)能夠快速有效地實(shí)現(xiàn)小批量的定制化生產(chǎn),提高生產(chǎn)系統(tǒng)的柔性已成為企業(yè)提升競(jìng)爭(zhēng)力的主要手段之一。車(chē)間調(diào)度問(wèn)題的研究大多是將各種參數(shù)假定為某個(gè)具體數(shù)值,此類(lèi)確定性調(diào)度模型不能很好地反映實(shí)際生產(chǎn)情況。本文研究的模糊柔性調(diào)度能夠更加準(zhǔn)確地描述生產(chǎn)中的加工時(shí)間、交貨期等在一定范圍內(nèi)不能精確描述的數(shù)值,有助于調(diào)度模型的完善。單獨(dú)研究模糊和柔性的車(chē)間調(diào)度的成果已有很多,同時(shí)考慮兩種特性將使問(wèn)題變得復(fù)雜得多。而問(wèn)題隨著規(guī)模變大和約束增多會(huì)變得更加復(fù)雜,數(shù)學(xué)規(guī)劃、規(guī)則啟發(fā)式等方法受到限制,利用智能算法之間的混合有助于調(diào)度問(wèn)題的解決,其中遺傳算法由于操作簡(jiǎn)單,具有魯棒性、兼容性好等優(yōu)點(diǎn),經(jīng)常被用來(lái)和其他算法結(jié)合,本文采用的算法就是在免疫算法和遺傳算法結(jié)合的基礎(chǔ)上加以改進(jìn)。本文針對(duì)考慮模糊加工時(shí)間和模糊交貨期的柔性作業(yè)車(chē)間調(diào)度問(wèn)題,使用加權(quán)目標(biāo)值的方法構(gòu)建了多目標(biāo)模糊柔性作業(yè)車(chē)間調(diào)度模型,給出了改進(jìn)的免疫遺傳算法的設(shè)計(jì)流程。算法中染色體采用玄光男提出的實(shí)數(shù)串編碼,并采用濃度抑制的自適應(yīng)提取疫苗操作,提出了新型的采用模擬退火的疫苗接種操作,接種前先對(duì)疫苗片段上的等位基因運(yùn)用檢測(cè)策略進(jìn)行判斷,具體做法是對(duì)比新舊最優(yōu)個(gè)體的對(duì)應(yīng)基因位,若在記憶庫(kù)中出現(xiàn)概率較小則嚴(yán)格控制交換,判斷是否非法解后再?zèng)Q定選棄。若某基因位在連續(xù)幾代的接種中疫苗基因無(wú)變化,進(jìn)一步對(duì)比該基因位上出現(xiàn)過(guò)的其他數(shù)值所能構(gòu)成最優(yōu)個(gè)體,判斷是否最優(yōu)基因或者陷入局部最優(yōu)。通過(guò)模擬退火以概率進(jìn)行接種可有效地改善早熟收斂和局部搜索能力差的缺點(diǎn),并加入記憶庫(kù)彌補(bǔ)了固定的交叉變異的不靈活性。最后先通過(guò)參考文獻(xiàn)的仿真實(shí)例證實(shí)了算法的可行性和有效性,接著以加工時(shí)間和滿(mǎn)意度為指標(biāo)對(duì)常用作標(biāo)準(zhǔn)算例的Kacem模糊柔性作業(yè)車(chē)間調(diào)度進(jìn)行求解,和單目標(biāo)的遺傳算法的Pareto最優(yōu)解比較,結(jié)果表明了該算法顯著提高了全局搜索能力和收斂速度,再以加工時(shí)間和機(jī)器負(fù)荷為指標(biāo),用8?8和10?10實(shí)例進(jìn)行測(cè)試,該算法比文獻(xiàn)中的其他算法獲得更好或相當(dāng)?shù)腜areto解。最后用極具欺騙性的Rastrigin函數(shù)作為Benchmark進(jìn)行收斂性分析,與文獻(xiàn)中的其他算法對(duì)比,證明了本文改進(jìn)的免疫遺傳算法在求解易陷入局部最優(yōu)的問(wèn)題時(shí)優(yōu)于大部分算法,前期可快速地跳出局部收斂,并彌補(bǔ)了后期接近最優(yōu)解時(shí)出現(xiàn)波動(dòng)震蕩的缺陷。
[Abstract]:Shop scheduling involves the operation management of production planning, purchasing, warehouse, sales and so on. As the core of production system, the optimization of shop scheduling scheme can improve the efficiency of production and the utilization of equipment. Because the products tend to be individualized and the process routes are more diversified, it is urgent for enterprises to realize the customization of small batch production quickly and effectively, and to improve the flexibility of the production system has become one of the main means to enhance the competitiveness of enterprises. Most of the researches on job shop scheduling problem assume various parameters as some specific value, and this kind of deterministic scheduling model can not well reflect the actual production situation. The fuzzy flexible scheduling studied in this paper can more accurately describe the processing time and the due date of production, which can not be accurately described in a certain range, which is helpful to the improvement of the scheduling model. There have been a lot of achievements on fuzzy and flexible job shop scheduling alone, and it will be much more complicated to consider the two characteristics at the same time. The problem will become more complex with the increase of scale and constraints, and the methods of mathematical programming, rule heuristics and so on are restricted. The use of the mixture of intelligent algorithms is helpful to solve the scheduling problem, in which the genetic algorithm is easy to operate. Because of its good robustness and good compatibility, it is often used to combine with other algorithms. The algorithm used in this paper is improved on the basis of the combination of immune algorithm and genetic algorithm. Aiming at the flexible job shop scheduling problem with fuzzy processing time and fuzzy due date, the multi-objective fuzzy flexible job shop scheduling model is constructed by using weighted target value method, and the design flow of the improved immune genetic algorithm is given. In the algorithm, the chromosome is encoded by real number string proposed by Xuan Guang male, and the adaptive extraction vaccine operation of concentration suppression is used. A new vaccination operation using simulated annealing is proposed. Before inoculation, the alleles on the vaccine fragments were judged by using the detection strategy. The specific method was to compare the corresponding gene sites of the new and the old optimal individuals. If the probability of appearing in the memory bank was small, the exchange would be strictly controlled. Decide whether the solution is illegal before you decide to abandon it. If there is no change in the vaccine gene in successive generations of inoculation, further comparison of the other values on the gene site can constitute the optimal individual, determine whether the optimal gene or fall into the local optimal. Inoculation with probability by simulated annealing can effectively improve premature convergence and poor local search ability, and add memory bank to make up for the inflexibility of fixed cross mutation. Finally, the feasibility and effectiveness of the algorithm are verified by a simulation example in reference. Then, the Kacem fuzzy flexible job shop scheduling, which is often used as a standard example, is solved by taking the processing time and satisfaction degree as the index. Compared with the Pareto optimal solution of the single objective genetic algorithm, the results show that the algorithm improves the global search ability and convergence speed significantly, and then takes the processing time and machine load as the index, and tests with 8 / 8 and 10 / 10 examples. This algorithm obtains better or equivalent Pareto solutions than other algorithms in literature. Finally, the Rastrigin function is used as the Benchmark to analyze the convergence. Compared with other algorithms in the literature, the improved immune genetic algorithm is superior to most of the algorithms in solving the problem which is prone to fall into local optimum. The early stage can jump out of the local convergence quickly and make up for the defect of wave oscillation when the latter is near the optimal solution.
【學(xué)位授予單位】：重慶交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TB497;TP18

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