【摘要】：隨著客戶要求普遍向高層次轉(zhuǎn)變、個(gè)性化需求愈趨明顯,大多數(shù)企業(yè)的生產(chǎn)方式也由大量生產(chǎn)轉(zhuǎn)向多品種小批量,并意識(shí)到不僅需要精湛的生產(chǎn)技術(shù)和先進(jìn)的生產(chǎn)設(shè)備來提高生產(chǎn)效率,更重要的是提高生產(chǎn)管理的水平。生產(chǎn)車間中存在的問題僅僅依靠設(shè)備布局原則、庫(kù)存管理、質(zhì)量控制已不能從根本上解決,多品種生產(chǎn)方式中總是存在各種復(fù)雜的因素影響整個(gè)生產(chǎn)過程的效率。因此,調(diào)度就成為企業(yè)深入解決問題的重點(diǎn),合理的調(diào)度不僅能減少資源浪費(fèi)、降低成本,加強(qiáng)生產(chǎn)過程中的柔性,而且還能提高生產(chǎn)效率和管理能力。并行機(jī)調(diào)度模型更是廣泛存在于多種領(lǐng)域,為促進(jìn)我國(guó)制造業(yè)的發(fā)展,解決并行機(jī)調(diào)度問題成為研究重點(diǎn)。本文以離散生產(chǎn)線的混合流水車間為背景,在零件、設(shè)備數(shù)量種類確定的情況下,將最小化最大完工時(shí)間定為優(yōu)化目標(biāo),結(jié)合eM-Plant仿真軟件尋找相同并行機(jī)車間調(diào)度的較優(yōu)調(diào)度方案。本文主要做出以下三方面的工作:1.建立數(shù)學(xué)模型。在離散生產(chǎn)線的背景下,針對(duì)相同并行機(jī)混合流水線調(diào)度問題進(jìn)行研究,并以調(diào)整時(shí)間作為主要約束條件,以最小化最大完工時(shí)間為優(yōu)化目標(biāo),構(gòu)建其數(shù)學(xué)模型。2.一次優(yōu)化的仿真分析。收集車間內(nèi)的數(shù)據(jù),對(duì)其生產(chǎn)現(xiàn)狀進(jìn)行仿真分析并優(yōu)化。首先利用遺傳算法模塊對(duì)優(yōu)化結(jié)果進(jìn)行首次優(yōu)化,證明了遺傳算法的有效性;利用算法的收斂性找出符合本文零件規(guī)模的遺傳算法參數(shù),采用新的交叉變異參數(shù)對(duì)現(xiàn)狀進(jìn)行仿真,得出加工時(shí)間及其加工排序。3.二次優(yōu)化的仿真分析。通過改變初始加工順序進(jìn)行第二次優(yōu)化:根據(jù)零件的規(guī)格和加工設(shè)備,對(duì)零件進(jìn)行分組,改變零件加工排序。采用新的排序,對(duì)模型進(jìn)行仿真,使完工時(shí)間得到優(yōu)化,從而找到更滿意的加工排序。
[Abstract]:With the general change of customer demand to high level, the individuation demand becomes more and more obvious, and the mode of production of most enterprises also changes from mass production to multi-variety and small batch. And realize that not only the exquisite production technology and advanced production equipment are needed to improve the production efficiency, but also the level of production management. The existing problems in the production workshop only depend on the equipment layout principle, inventory management, quality control can not be fundamentally solved, there are always a variety of complex factors affecting the efficiency of the entire production process. Therefore, scheduling becomes the focus of the enterprise to solve the problem in depth. Reasonable scheduling can not only reduce the waste of resources, reduce the cost, strengthen the flexibility in the production process, but also improve the production efficiency and management ability. Parallel machine scheduling model is widely used in many fields. In order to promote the development of our manufacturing industry, solving the parallel machine scheduling problem has become the focus of research. In this paper, based on the mixed flow workshop of discrete production line, the optimization goal is to minimize the maximum completion time when the number of parts and equipment is determined. Combined with eM-Plant simulation software, the optimal scheduling scheme of the same parallel machine shop scheduling is found. This paper mainly does the following three aspects of work: 1. Establish mathematical model. In the background of discrete production line, the mixed pipeline scheduling problem of the same parallel machine is studied, and the adjustment time is taken as the main constraint condition, and the optimization objective is to minimize the maximum completion time, and the mathematical model is constructed. 2. The simulation analysis of one optimization. Collect the data in the workshop, analyze and optimize the production status. Firstly, the genetic algorithm module is used to optimize the optimization results for the first time, which proves the effectiveness of the genetic algorithm. By using the convergence of the algorithm to find out the parameters of genetic algorithm which accord with the size of the parts in this paper, the new cross-mutation parameters are used to simulate the current situation, and the processing time and processing order are obtained. Simulation analysis of quadratic optimization. The second optimization is carried out by changing the initial processing order: according to the specification of the parts and the processing equipment, the parts are grouped, and the processing order of the parts is changed. A new sort is used to simulate the model, so that the completion time is optimized and a more satisfactory processing order can be found.
【學(xué)位授予單位】：天津理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TB497

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