本文選題：服裝生產(chǎn)　切入點：任務(wù)智能調(diào)度　出處：《東華大學(xué)》2012年碩士論文　論文類型：學(xué)位論文

【摘要】：作為一類勞動密集型的制造業(yè),服裝生產(chǎn)的任務(wù)調(diào)度與控制主要依賴于生產(chǎn)管理者的知識、經(jīng)驗甚至直覺。這導(dǎo)致生產(chǎn)決策往往不是最優(yōu)并且是不可靠的。同時,隨著小批量生產(chǎn)的流行與產(chǎn)品款式的頻繁變化,單靠人工進行生產(chǎn)任務(wù)調(diào)度已無法滿足當(dāng)前服裝快速生產(chǎn)的要求。本文針對服裝生產(chǎn)中的任務(wù)調(diào)度要求,對生產(chǎn)任務(wù)調(diào)度問題進行了分析與建模,提出了基于一類改進的離散差分進化算法的任務(wù)智能調(diào)度模型。在此基礎(chǔ)上,開發(fā)了集離散事件仿真模型與任務(wù)智能調(diào)度模型于一體的生產(chǎn)調(diào)度專家系統(tǒng),為服裝敏捷制造過程中的生產(chǎn)計劃與控制提供科學(xué)的決策。本論文研究的主要貢獻(xiàn)和創(chuàng)新點如下 1.根據(jù)服裝混合流水線的特點對任務(wù)調(diào)度問題進行數(shù)學(xué)建模,給出了其目標(biāo)函數(shù)與約束條件。針對多目標(biāo)的總體評價問題,提出了效用函數(shù)用于綜合多種效用值,并給出了效用函數(shù)的定義。 2.提出了利用離散事件仿真模型實現(xiàn)對混合流水線動態(tài)過程的模擬,解決了無數(shù)學(xué)方程可直接獲取混合流水線動態(tài)性能目標(biāo)的難題。此外離散事件仿真模型還能為管理者提供直觀的方式用于微調(diào)調(diào)度策略,為管理者手動分配任務(wù)提供一種便捷的途徑。 3.提出一種基于Pareto最優(yōu)集思想的改進離散差分進化算法,該算法有效地增加了種群的多樣性,解決了傳統(tǒng)差分算法過早收斂的問題。同時,將改進的離散差分算法用于服裝生產(chǎn)中的任務(wù)調(diào)度問題。大量實驗結(jié)果表明了該算法的正確性和有效性。 4.開發(fā)了一類適用于服裝企業(yè)手工制衣流水線的任務(wù)智能調(diào)度專家系統(tǒng),給出了其程序結(jié)構(gòu)及主要模塊的實現(xiàn)過程,并對其應(yīng)用結(jié)果進行分析與討論。 本文提出的基于Pareto選擇策略的離散差分進化算法,以及基于此算法的任務(wù)智能調(diào)度專家系統(tǒng),將使國內(nèi)服裝企業(yè)受益于由科學(xué)的任務(wù)分配策略所帶來的生產(chǎn)敏捷性和生產(chǎn)效率的提升,增強它們在全球服裝行業(yè)中的競爭優(yōu)勢。
[Abstract]:As a kind of labor-intensive manufacturing industry, task scheduling and control of garment production mainly depend on the knowledge, experience and even intuition of the production manager. With the popularity of small batch production and the frequent changes of product styles, manual production task scheduling can no longer meet the requirements of rapid garment production. Based on the analysis and modeling of production task scheduling problem, a task intelligent scheduling model based on a class of improved discrete differential evolution algorithm is proposed. A production scheduling expert system which integrates discrete event simulation model and task intelligent scheduling model is developed to provide scientific decision making for production planning and control in garment agile manufacturing process. The main contributions and innovations of this paper are as follows. 1. According to the characteristics of mixed pipeline, the task scheduling problem is modeled, and its objective function and constraint conditions are given. In view of the multi-objective overall evaluation problem, the utility function is used to synthesize various utility values. The definition of utility function is given. 2. A discrete event simulation model is proposed to simulate the dynamic process of mixed pipeline. In addition, the discrete event simulation model can provide a direct way for managers to fine-tune the scheduling strategy, and solve the problem that the mixed pipeline dynamic performance target can be obtained directly without mathematical equations, and the discrete event simulation model can provide an intuitive way for managers to fine-tune scheduling strategy. It provides a convenient way for managers to assign tasks manually. 3. An improved discrete differential evolutionary algorithm based on the idea of Pareto optimal set is proposed. The algorithm effectively increases the diversity of population and solves the problem of premature convergence of traditional difference algorithm. The improved discrete difference algorithm is applied to the task scheduling problem in garment production. A large number of experimental results show that the algorithm is correct and effective. 4. A kind of task intelligent dispatching expert system suitable for garment manufacturer pipeline is developed. The program structure and the realization process of the main modules are given, and the application results are analyzed and discussed. In this paper, a discrete differential evolutionary algorithm based on Pareto selection strategy and a task intelligent scheduling expert system based on this algorithm are proposed. It will enable domestic garment enterprises to benefit from the improvement of production agility and production efficiency brought about by scientific task allocation strategy and enhance their competitive advantage in the global garment industry.
【學(xué)位授予單位】：東華大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：TH186

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 李純蓮,王希誠,趙金城;一種新的遺傳算法停止準(zhǔn)則[J];遼寧工程技術(shù)大學(xué)學(xué)報;2004年01期

2 童剛,李光泉　,劉寶坤;用遺傳算法解決在并行機上帶有不同交貨期窗口的Job-Shop調(diào)度問題[J];系統(tǒng)工程;2000年03期

3 王萬良,吳啟迪,趙燕偉,陳志強,楊旭東;流程工業(yè)CIMS中生產(chǎn)調(diào)度研究進展[J];機電工程;1999年05期

4 舒炎泰，趙文欽，，朱立禹;流程CIM與離散CIM之比較[J];計算機研究與發(fā)展;1996年09期

5 涂雪平;施燦濤;李鐵克;;求解置換流水車間調(diào)度問題的改進遺傳算法[J];計算機工程與應(yīng)用;2009年36期

6 楊妍;陳如清;俞金壽;;差分進化粒子群混合優(yōu)化算法的研究與應(yīng)用[J];計算機工程與應(yīng)用;2010年25期

7 黃斌;陳德禮;;多目標(biāo)優(yōu)化問題的有效Pareto最優(yōu)集[J];計算機與數(shù)字工程;2009年02期

8 唐海波;葉春明;張新功;;應(yīng)用模擬植物生長算法求解置換流水車間調(diào)度問題[J];計算機應(yīng)用研究;2010年10期

9 肖曉偉;肖迪;林錦國;肖玉峰;;多目標(biāo)優(yōu)化問題的研究概述[J];計算機應(yīng)用研究;2011年03期

10 段國林,查建中,徐安平,張滿囤;啟發(fā)式算法及其在工程中的應(yīng)用[J];機械設(shè)計;2000年06期

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