本文選題：生產(chǎn)計(jì)劃　切入點(diǎn)：調(diào)度約束　出處：《沈陽(yáng)工業(yè)大學(xué)》2017年碩士論文

【摘要】：企業(yè)在生產(chǎn)管理中對(duì)生產(chǎn)計(jì)劃與調(diào)度進(jìn)行集成優(yōu)化已經(jīng)成為一項(xiàng)焦點(diǎn)技術(shù),能夠使企業(yè)在競(jìng)爭(zhēng)中提升優(yōu)勢(shì)�！皬纳献韵隆笔莻鹘y(tǒng)的生產(chǎn)計(jì)劃與調(diào)度的生產(chǎn)方式:先是進(jìn)行生產(chǎn)計(jì)劃,接著再根據(jù)計(jì)劃進(jìn)行調(diào)度,然而這種方式存在的缺陷就是容易對(duì)調(diào)度約束產(chǎn)生忽視,即使制定了生產(chǎn)計(jì)劃也會(huì)產(chǎn)生不能進(jìn)行調(diào)度的情況,使得企業(yè)在發(fā)展中失去競(jìng)爭(zhēng)優(yōu)勢(shì)。因此,要想合理地進(jìn)行生產(chǎn)管理就必須把計(jì)劃和調(diào)度統(tǒng)一進(jìn)行優(yōu)化,使得即使存在一定的約束性也不會(huì)影響整個(gè)計(jì)劃的順利進(jìn)行,將生產(chǎn)計(jì)劃和調(diào)度進(jìn)行同步優(yōu)化,要把握整個(gè)生產(chǎn)中供需平衡問(wèn)題,不能在短時(shí)間內(nèi)經(jīng)常性地變化產(chǎn)品的類型。把工作的時(shí)間降低,使得設(shè)備占有率降低,可使得企業(yè)的生產(chǎn)速度增加、可產(chǎn)出量提升。因此本文提出一個(gè)算法,對(duì)中、小型企業(yè)的Job Shop中生產(chǎn)計(jì)劃和調(diào)度的集成建模與優(yōu)化進(jìn)行研究分析。根據(jù)大量的國(guó)內(nèi)外文獻(xiàn)資料,探究和分析了企業(yè)在加工車間進(jìn)行生產(chǎn)計(jì)劃和調(diào)度的集成優(yōu)化的情況。本文利用批量生產(chǎn)和分解的方式構(gòu)建數(shù)學(xué)模型,在理論上滿足了對(duì)生產(chǎn)計(jì)劃和調(diào)度集成優(yōu)化的生產(chǎn)需求,主要集中在目標(biāo)的多樣化上。根據(jù)批量生產(chǎn)計(jì)劃和存在的約束對(duì)問(wèn)題進(jìn)行整體評(píng)判得到確定函數(shù),實(shí)現(xiàn)在整個(gè)生產(chǎn)周期內(nèi)所需要的各種費(fèi)用總和最小(包括庫(kù)存費(fèi)、調(diào)度費(fèi)、生產(chǎn)費(fèi)等),同時(shí)保證最大完工時(shí)間最小化。所涉及的約束條件不僅指生產(chǎn)能力約束還有調(diào)度規(guī)則,也就是機(jī)器的加工能力和加工工序順序的問(wèn)題,另外還要保證庫(kù)存不能太少或者爆滿,進(jìn)而使得生產(chǎn)計(jì)劃順利進(jìn)行。本文不僅提出啟發(fā)式規(guī)則和蟻群算法,而且還涉及到它們的結(jié)合使用,使得迭代速度加快不會(huì)出現(xiàn)僵局,同時(shí)又保證運(yùn)算穩(wěn)定性,并且降低了常規(guī)算法在分析螞蟻轉(zhuǎn)移概率方面的復(fù)雜性,也對(duì)比和研究了遺傳算法和模擬退火算法的性能。使用該種算法不僅能夠?qū)崿F(xiàn)生產(chǎn)計(jì)劃和調(diào)度集成優(yōu)化的問(wèn)題,還能對(duì)實(shí)例進(jìn)行了仿真模擬。結(jié)果顯示提出的新算法一方面計(jì)算所需的時(shí)間短,另一方面對(duì)全局也具有一定的收斂作用。依據(jù)上述理論的基礎(chǔ),在敏捷制造環(huán)境下以某機(jī)床廠中、小批量生產(chǎn)車間為背景,通過(guò)實(shí)際案例對(duì)本文提出的算法進(jìn)行了對(duì)比演算,得出仿真效果,說(shuō)明了本文提出的模型和求解方法都是可行的,其優(yōu)化結(jié)果遠(yuǎn)好于基于NEH排序準(zhǔn)則編寫(xiě)的遺傳算法的結(jié)果。
[Abstract]:Integrated optimization of production planning and scheduling in production management has become a focus technology. "from top to bottom" is the traditional production mode of production planning and scheduling: first the production plan, then the scheduling according to the plan, However, the defect of this way is that it is easy to ignore the scheduling constraints, even if the production plan is made, it will lead to the situation that the scheduling can not be carried out, which makes the enterprise lose its competitive advantage in the development. In order to carry out the production management reasonably, the planning and scheduling must be optimized in a unified way, so that even if there are certain constraints, the smooth progress of the whole plan will not be affected, and the production plan and scheduling should be optimized synchronously. In order to grasp the problem of balance between supply and demand in the whole production, we should not change the type of products regularly in a short period of time. By reducing the working time and reducing the share of equipment, we can increase the production speed of enterprises. Therefore, this paper proposes an algorithm to study and analyze the integrated modeling and optimization of production planning and scheduling in Job Shop of small and medium-sized enterprises. This paper probes into and analyzes the integrated optimization of production planning and scheduling in the processing workshop. In this paper, a mathematical model is constructed by means of batch production and decomposition, which meets the production requirements of integrated optimization of production planning and scheduling theoretically. It is mainly focused on the diversification of objectives. According to the overall evaluation of the problem according to the batch production plan and existing constraints, the definite function is obtained, and the sum of all kinds of costs required in the whole production cycle is minimized (including inventory cost, scheduling cost, etc.). Production costs, etc., while ensuring maximum completion time minimization. The constraints involved not only refer to production capacity constraints but also scheduling rules, that is, the machining capacity of machines and the sequence of processing processes, In addition, we also need to ensure that the inventory is not too small or full, so that the production plan can proceed smoothly. This paper not only proposes heuristic rules and ant colony algorithm, but also involves their combined use, so that the iteration speed can be accelerated without deadlock. At the same time, the computational stability is guaranteed, and the complexity of the conventional algorithm in analyzing ant transfer probability is reduced. The performance of genetic algorithm (GA) and simulated annealing algorithm (SA) are compared and studied. The simulation results show that the proposed new algorithm has a certain convergent effect on the whole, on the one hand, the computing time is short, on the other hand, it has a certain convergent effect on the whole. Based on the background of a machine tool factory in an agile manufacturing environment, the algorithm proposed in this paper is compared and calculated by a practical case, and the simulation results are obtained. The results show that the proposed model and solution method are feasible. The result of optimization is much better than that of genetic algorithm based on NEH ranking criterion.
【學(xué)位授予單位】：沈陽(yáng)工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP18;TB497

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