本文選題：分批　切入點(diǎn)：調(diào)度　出處：《廣東工業(yè)大學(xué)》2011年碩士論文

【摘要】：多品種小批量訂貨式生產(chǎn)在制造業(yè)中占據(jù)著很重要的地位。柔性制造單元(FMC)是用于多品種、中小批量生產(chǎn)的具有高柔性且自動化程度高的制造系統(tǒng)。近年來,許多中心批量生產(chǎn)的企業(yè)已經(jīng)將此單元應(yīng)用在實(shí)際生產(chǎn)中。 交貨期是訂貨型企業(yè)的生命線。在車間安排生產(chǎn)任務(wù)時(shí),多難以兼顧多訂單的交貨期要求。FMC的生產(chǎn)環(huán)境下,為降低單元的使用成本,提高設(shè)備的利用率,可以將不同訂單的相同類型工件組成若干批次進(jìn)行加工。組批和調(diào)度方案的好壞直接影響著企業(yè)經(jīng)濟(jì)效益和聲譽(yù)。因此,對這樣的實(shí)際問題如何編制合理優(yōu)化的組批調(diào)度方案是非常有必要的。 本文以一家大型輪胎模具生產(chǎn)企業(yè)為背景,研究一類集成批量計(jì)劃和平行機(jī)調(diào)度的問題,該問題具有訂單交貨期、到達(dá)時(shí)間和加工準(zhǔn)備時(shí)間等約束。首先在訂單到達(dá)時(shí)間確定的情況下,建立單個(gè)數(shù)學(xué)模型描述集成問題,以降低單元加工費(fèi)用和訂單拖期懲罰費(fèi)用為目標(biāo)；提出一種帶啟發(fā)式規(guī)則的遺傳模擬退火兩階段算法(GASA)。算法引入啟發(fā)式規(guī)則生成基礎(chǔ)批有效減少了染色體長度,從而加快搜索速度。遺傳算法對基礎(chǔ)批進(jìn)行全局搜索,在批量確定的情況下,模擬退火進(jìn)行局部搜索,得到當(dāng)前分批情況下的優(yōu)值。然后以此模具企業(yè)的實(shí)際生產(chǎn)例檢驗(yàn)該算法的收斂性,證明該算法在可以接受的時(shí)間內(nèi)是有效可行的；通過對比GASA和GA算法的求解效果,說明相比于GA算法,這種帶啟發(fā)式規(guī)則的混合算法確實(shí)能夠更快更好的求得問題的較優(yōu)解；選取五種不同規(guī)模的實(shí)際生產(chǎn)例進(jìn)行數(shù)值仿真,分別采用該算法和一種經(jīng)典算法在相同的計(jì)算時(shí)間內(nèi)進(jìn)行求解。對計(jì)算結(jié)果對比分析表明隨著是任務(wù)規(guī)模的增大,該種算法的優(yōu)勢更加明顯,從而說明了該模型和算法針對這一類特殊問題更為有效和可行。接著下一步,針對上述分批調(diào)度數(shù)學(xué)模型,考慮到實(shí)際生產(chǎn)中的訂單到達(dá)時(shí)間不確定因素,且訂單的到達(dá)過程符合泊松過程,從而建立期望值模型,在隨機(jī)變量的概率密度函數(shù)已知的情況下,將問題模型按照確定性模型來處理,采用前文所提帶啟發(fā)式規(guī)則的遺傳模擬退火兩階段算法進(jìn)行求解。通過大規(guī)模的數(shù)值仿真實(shí)驗(yàn),證明該算法對待訂單到達(dá)時(shí)間不確定問題的求解同樣有效,并且這一類問題的求解過對訂程單到達(dá)時(shí)間不敏感。最后,運(yùn)用先進(jìn)的建模和仿真工具eM-Plant,結(jié)合應(yīng)用面向?qū)ο蟮姆椒?進(jìn)一步深入面向平行機(jī)的分批調(diào)度模型。利用eM-Plant中的SimTalk語言對各個(gè)對象及活動進(jìn)行編程控制,還原現(xiàn)實(shí)生產(chǎn)環(huán)境。對比仿真模型運(yùn)行結(jié)果和GASA結(jié)算結(jié)果,證明GASA在現(xiàn)實(shí)生產(chǎn)中具有可行性。
[Abstract]:The flexible manufacturing unit (FMC) is a manufacturing system with high flexibility and high automation, which is used in many varieties, medium and small batch production. Many enterprises in the center of mass production have applied this unit to actual production. Delivery time is the lifeline of an order-oriented enterprise. In order to reduce the cost of unit use and improve the utilization rate of equipment, it is difficult to take account of the production environment of multi-order delivery time requirement .FMC when the workshop arranges production tasks. The same type of workpieces of different orders can be assembled into several batches for processing. The quality of the group batch and scheduling scheme directly affects the economic efficiency and reputation of the enterprise. It is necessary to work out a reasonably optimized batch scheduling scheme for such practical problems. Based on the background of a large tire mould manufacturing enterprise, this paper studies a class of integrated batch planning and parallel machine scheduling problems, which have order delivery time. First of all, a single mathematical model is established to describe the integration problem in order to reduce the unit processing cost and the penalty cost of the order delay when the order arrival time is determined. A two-stage genetic simulated annealing algorithm with heuristic rules is proposed. The heuristic rule is introduced to generate the base batch, which can effectively reduce the chromosome length and speed up the search. In the case of batch determination, simulated annealing performs local search to get the best value in the current batch condition. Then the convergence of the algorithm is verified by the actual production examples of the die and mould enterprises. It is proved that the algorithm is effective and feasible in the acceptable time, and by comparing the results of GASA and GA algorithms, it is proved that the hybrid algorithm with heuristic rules can obtain the optimal solution of the problem faster and better than GA algorithm. Five practical production examples of different scales are selected for numerical simulation. The algorithm and a classical algorithm are used to solve the problem in the same time. The comparison and analysis of the calculated results show that with the increase of the scale of the task, The advantages of this algorithm are more obvious, which shows that the model and algorithm are more effective and feasible for this kind of special problems. Considering the uncertainty of order arrival time in actual production, and the arrival process of order accords with Poisson process, the expected value model is established. When the probability density function of random variable is known, the probability density function of random variable is known. The problem model is treated by deterministic model, and the genetic simulated annealing two-stage algorithm with heuristic rule is used to solve the problem. It is proved that the algorithm is equally effective in solving the uncertain order arrival time problem, and that the solution of this kind of problem is not sensitive to the arrival time of single order. Finally, the advanced modeling and simulation tool eM-Plantis used in combination with the object-oriented method. Further deeper into the batch scheduling model for parallel machines. Using the SimTalk language in eM-Plant to control each object and activities, restore the real production environment, compare the results of the simulation model and GASA settlement, compare the results of the simulation model and the results of GASA settlement, and compare the results of the simulation model with the results of GASA settlement. It is proved that GASA is feasible in practical production.
【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】：2011
【分類號】：TH186

【引證文獻(xiàn)】

相關(guān)碩士學(xué)位論文 前2條

1 徐武來;具有完工期和工裝數(shù)量約束的平行機(jī)調(diào)度方法[D];廣東工業(yè)大學(xué);2012年

2 陳在德;隨機(jī)多資源模式模具項(xiàng)目群調(diào)度方法[D];廣東工業(yè)大學(xué);2012年

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本文編號：1681370