本文選題：服裝生產　切入點：任務智能調度　出處：《東華大學》2012年碩士論文　論文類型：學位論文

【摘要】：作為一類勞動密集型的制造業(yè),服裝生產的任務調度與控制主要依賴于生產管理者的知識、經驗甚至直覺。這導致生產決策往往不是最優(yōu)并且是不可靠的。同時,隨著小批量生產的流行與產品款式的頻繁變化,單靠人工進行生產任務調度已無法滿足當前服裝快速生產的要求。本文針對服裝生產中的任務調度要求,對生產任務調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智能調度模型。在此基礎上,開發(fā)了集離散事件仿真模型與任務智能調度模型于一體的生產調度專家系統(tǒng),為服裝敏捷制造過程中的生產計劃與控制提供科學的決策。本論文研究的主要貢獻和創(chuàng)新點如下 1.根據服裝混合流水線的特點對任務調度問題進行數學建模,給出了其目標函數與約束條件。針對多目標的總體評價問題,提出了效用函數用于綜合多種效用值,并給出了效用函數的定義。 2.提出了利用離散事件仿真模型實現對混合流水線動態(tài)過程的模擬,解決了無數學方程可直接獲取混合流水線動態(tài)性能目標的難題。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調度策略,為管理者手動分配任務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中的任務調度問題。大量實驗結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智能調度專家系統(tǒng),給出了其程序結構及主要模塊的實現過程,并對其應用結果進行分析與討論。 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智能調度專家系統(tǒng),將使國內服裝企業(yè)受益于由科學的任務分配策略所帶來的生產敏捷性和生產效率的提升,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.
【學位授予單位】：東華大學
【學位級別】：碩士
【學位授予年份】：2012
【分類號】：TH186

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