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研究生:林晏生
研究生(外文):Yen-Seng Lin
論文名稱:成衣業馬克排版最佳化之研究使用基因演算法
論文名稱(外文):A Study of Using Genetic Algorithms Marker Marking Optimization
指導教授:馬恆馬恆引用關係
指導教授(外文):Heng Ma
學位類別:碩士
校院名稱:中華大學
系所名稱:科技管理學系(所)
學門:商業及管理學門
學類:其他商業及管理學類
論文種類:學術論文
論文出版年:2007
畢業學年度:95
語文別:中文
論文頁數:62
中文關鍵詞:馬克排版基因演算法半離散式表示法
外文關鍵詞:Marker MarkingGenetic AlgorithmsSemi-Discrete Representations
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馬克排版是指放大、縮小之後的全套尺碼樣版,以訂單數量搭配種類,以裁床長度及其他相關因素,繪製成拉布裁剪時所應用之省布排列圖,從事大量生產時以經濟使用布料為原則;也就是將多種款式的尺碼樣版排列、放置於紙張上,作為裁剪依據的裁片輪廓圖,在成衣業的製程中具有相當的重要性。在過去,馬克排版的完成是由多年經驗的師傅在耗費許多時間下完成,會造成製程上的負擔,隨著電腦科技的進步,馬克排版的方式亦從手工安排演變為半自動化安排,縮短排版時間並獲得有效的排版結果才能獲取合理的利潤。馬克排版亦為一種典型的二維排版問題,但是其排版比一般金屬板材排版多考量紋路方向,在實務上,衣服紋路並非完全固定不變,因此可容許少量的旋轉角度,且成衣排版亦容許樣片間有少量的重疊,藉由這些方式可達成減少廢料的目的。針對上述問題,首先在解決樣片間的重疊問題上,本研究利用輪廓偏移的方法來解決重疊的問題;並運用半離散式表示法來表達馬克排版問題中的樣片與馬克紙,在過去,排版研究對於排版的2D圖形,都採用離散式或連續式資料來表達,兩種方法各有優缺點,採其個別優點,本研究利用半離散式表示法來表達馬克排版中的樣片與馬克紙;在安排位置的搜尋上,本研究運用基因演算法(GA)計算樣片的順序並利用半離散演算法來完成馬克排版。從本研究的實驗結果証明,使用本研究的方法,先找到合理安排的順序,再根據此順序進行樣片的安排,最後在可接受的時間內搜尋出合理的安排結果,期望藉由此結果能縮短馬克排版的時間、減少布料的浪費、獲取合理的利潤,而本研究所提之方法也適用於有類似情況的二維排版問題上。
Marker Marking is the outfit size sample plate after grading, match the sort with the quantity of the order, with the length of cutting bed and other relevant factors, drawing the arranging picture is saving clothes while cutting and spreading, take using economically the cloth as the principle while production on large scale. That is also to say arranging and putting the many kinds of size sample plate on the paper, as cuts out to the cutting piece outline picture basic. Marker Marking has quite importance in the ready-made clothes industry Processes. In the past, the completion of marker marking is consuming a lot of time by the worker has experience of many years, it will make burden on the progress. With the progress of computer science and technology, the way of marking nesting also from manual finishing developing into the semi-automation. That is shorting composing time and obtaining the effective nesting result, can obtain the reasonable profit. Marker Markings is also a kind of typical two-dimensional nesting question, but it considers more grain directions than general metal panel nesting. On the practice, but composing its consider more line directions than general metal panel composing, on the practice, the grain of the clothes does not entire changeless, so can permit a small amount of rotation angle, and Marker Markings also permits to have a small amount of overlap among pieces, and then it can achieve the purpose to reduce waste materials in these ways. To above problems, on solving the overlap among pieces at first, this research utilizes the method offsetting the outline method to solve the overlap problem, and uses semi-discrete representations method to represent piece and marker in the question of Marker Markings. In the past, two-dimensional pictures in nesting represented using discrete type or continuous type data, two method have pluses and minuses each, this research utilizes semi-discrete representations method to represent piece and marker in Marker Markings. In searching and arranging the position, this research uses the Genetic Algorithms (GA) to calculate the sequence of pieces and utilizes semi-discrete algorithm to finish Marker Markings. Prove from the experimental result of this research, using the method of this research, find the sequence of the arrangement rationally first, and then basing the sequence on arranging the pieces, searching out the rational the result of arrangement within acceptable time finally. It expect that base the result on shorting the time of Marker Marking, reducing waste of clothes, and obtaining the reasonable profit. and the method that this research propose also is suitable to having the similar situation in the two-dimensional nesting question.
摘 要 i Abstract ii 誌 謝 iv 目 錄 v 圖目錄 vii 表目錄 ix 第一章 緒論 1 1.1 研究背景 1 1.2 研究動機 2 1.3 研究目的 3 1.4 研究範疇 3 1.5 主要貢獻 4 第二章 文獻探討 5 2.1 二維排版問題 5 2.2 成衣排版問題 8 2.3 Offset(偏移)問題 9 2.4 半離散式表示法 10 2.5 基因演算法 13 2.5.1 基因演算法簡介 13 2.5.2 相關文獻 14 第三章 利用基因演算法處理馬克排版 15 3.1 前言 15 3.1.1 研究用語定義 16 3.1.2 研究假設 16 3.1.3 樣片與馬克紙的圖形表示與處理 17 3.2 Offset(偏移)演算方法 19 3.2.1 處理重疊問題假設 19 3.2.2 偏移方法說明 20 3.3 半離散演算表示法 23 3.4 基因演算法 25 3.4.1.編碼(Encoding) 26 3.4.2.初始母體產生(Population) 27 3.4.3.進行排版 27 3.4.4.計算適合度函數(Fitness Function) 28 3.4.5.複製母體(reproduction) 28 3.4.6.交配(Crossover) 29 3.4.8.基因演算法流程 30 第四章 實例探討 32 4.1 馬克排版程式介面簡介 32 4.2 馬克排版實例一說明 34 4.3 馬克排版實例二說明 38 4.4 馬克排版實例三說明 41 4.5 馬克排版實例四說明 44 第五章 結論與未來之展望 49 5.1 結論 49 5.2 後續發展及建議 49 參考文獻 51
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