發(fā)布時(shí)間：2018-08-08 19:28

【摘要】：多品種小批量生產(chǎn)模式是近幾年來較為流行的一種生產(chǎn)模式,生產(chǎn)調(diào)度是該生產(chǎn)模式下企業(yè)管理中十分重要的一個(gè)環(huán)節(jié),對(duì)企業(yè)的產(chǎn)品庫存量、產(chǎn)品生產(chǎn)周期、生產(chǎn)資源利用率等方面都有著重要的影響。經(jīng)典調(diào)度理論主要是針對(duì)傳統(tǒng)模式下的生產(chǎn)調(diào)度問題進(jìn)行研究,和實(shí)際調(diào)度問題之間仍然存在一定差距。20世紀(jì)末以來,各種計(jì)算智能方法在生產(chǎn)調(diào)度中得到了越來越多的應(yīng)用,并且表現(xiàn)出良好的前景,逐步成為現(xiàn)今生產(chǎn)調(diào)度研究領(lǐng)域的主流方法。因此針對(duì)多品種小批量生產(chǎn)模式下的智能調(diào)度方法進(jìn)行研究,對(duì)于提高企業(yè)的生產(chǎn)效率、降低生產(chǎn)成本、增加企業(yè)效益有著重要的意義。 本文對(duì)多品種小批量的生產(chǎn)模式的特點(diǎn)進(jìn)行了深入分析,建立了該模式下生產(chǎn)調(diào)度問題的數(shù)學(xué)模型,在深入研究了遺傳退火算法關(guān)鍵技術(shù)的基礎(chǔ)上,對(duì)其進(jìn)行了適應(yīng)性的改進(jìn),最后設(shè)計(jì)開發(fā)了基于改進(jìn)遺傳退火算法的多品種小批量生產(chǎn)智能調(diào)度原型系統(tǒng),為基于計(jì)算智能的多品種小批量生產(chǎn)調(diào)度技術(shù)的發(fā)展進(jìn)行了有益的探索。論文在理論與實(shí)踐中的主要研究成果如下： (1)在分析生產(chǎn)調(diào)度的經(jīng)典數(shù)學(xué)模型及表達(dá)方式的基礎(chǔ)上,結(jié)合多品種小批量生產(chǎn)模式的特點(diǎn),建立了多品種小批量生產(chǎn)模式下生產(chǎn)調(diào)度問題的數(shù)學(xué)模型。 (2)深入分析了遺傳退火算法各種實(shí)現(xiàn)技術(shù)和設(shè)計(jì)準(zhǔn)則,并針對(duì)傳統(tǒng)遺傳退火算法在面對(duì)實(shí)際生產(chǎn)調(diào)度過程中的不足,對(duì)其進(jìn)行改進(jìn),使之能夠滿足實(shí)際生產(chǎn)過程中對(duì)裝配約束和優(yōu)先級(jí)調(diào)度的需求。 (3)從數(shù)據(jù)結(jié)構(gòu)和調(diào)度算法兩個(gè)方面,對(duì)生產(chǎn)調(diào)度原型系統(tǒng)進(jìn)行了設(shè)計(jì),利用C++與C#的混編技術(shù),開發(fā)了基于改進(jìn)遺傳退火算法的智能調(diào)度原型系統(tǒng)。原型系統(tǒng)主要包括調(diào)度、輸出和統(tǒng)計(jì)三個(gè)功能模塊。實(shí)際應(yīng)用表明原型系統(tǒng)具有較快的計(jì)算速度,參數(shù)設(shè)置靈活,通用性較強(qiáng),有一定的實(shí)用價(jià)值。
[Abstract]:Multi-variety and small-batch production model is a popular production mode in recent years. Production scheduling is a very important part of enterprise management under this production mode. The utilization ratio of production resources has important influence. The classical scheduling theory mainly focuses on the production scheduling problem in the traditional mode. There is still a certain gap between the traditional scheduling problem and the actual scheduling problem. Since the end of the 20th century, a variety of computational intelligence methods have been more and more used in production scheduling. And show good prospects, gradually become the mainstream method in the field of production scheduling research. Therefore, it is of great significance to study the intelligent scheduling method under the multi-variety and small-batch production mode to improve the production efficiency, reduce the production cost and increase the efficiency of the enterprise. In this paper, the characteristics of multi-variety and small-batch production model are deeply analyzed, and the mathematical model of production scheduling problem under this model is established. On the basis of deeply studying the key technology of genetic annealing algorithm, the adaptive improvement is made. Finally, an intelligent scheduling prototype system for multi-variety and small-batch production based on improved genetic annealing algorithm is designed and developed, which provides a useful exploration for the development of multi-variety and small-batch production scheduling technology based on computational intelligence. The main research results in theory and practice are as follows: (1) on the basis of analyzing the classical mathematical model and expression of production scheduling, combined with the characteristics of multi-variety small-batch production model, The mathematical model of production scheduling problem in multi-variety and small-batch production mode is established. (2) various realization techniques and design criteria of genetic annealing algorithm are deeply analyzed. Aiming at the deficiency of the traditional genetic annealing algorithm in the process of actual production scheduling, the paper improves it. It can meet the requirements of assembly constraints and priority scheduling in the actual production process. (3) the prototype system of production scheduling is designed from two aspects of data structure and scheduling algorithm, and the mixed programming technology of C and C # is used. An intelligent scheduling prototype system based on improved genetic annealing algorithm is developed. The prototype system mainly includes three function modules: scheduling, output and statistics. The practical application shows that the prototype system has the advantages of fast calculation speed, flexible parameter setting, strong versatility and practical value.
【學(xué)位授予單位】：中國工程物理研究院
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2011
【分類號(hào)】：TH186

【引證文獻(xiàn)】

相關(guān)期刊論文 前1條

1 韓鵬飛;孫占磊;趙罡;;改進(jìn)離散粒子群算法及其在飛機(jī)裝配任務(wù)調(diào)度中的應(yīng)用研究[J];圖學(xué)學(xué)報(bào);2013年01期

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