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研究生:黃佩芬
研究生(外文):Huang, Pei-Fen
論文名稱:單機排程下的運輸指派策略
論文名稱(外文):A single machine scheduling problem with air transportation decision
指導教授:游鵬勝游鵬勝引用關係
指導教授(外文):You, Peng-Sheng
學位類別:碩士
校院名稱:國立嘉義大學
系所名稱:運輸與物流工程研究所
學門:運輸服務學門
學類:運輸管理學類
論文種類:學術論文
論文出版年:2009
畢業學年度:97
語文別:中文
論文頁數:67
中文關鍵詞:單機排程到期日基因演算法
外文關鍵詞:Single Machine SchedulingDue dayGenetic Algorithm
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在製造產業的領域中,生產排程及運送策略一直是生產管理或供應鏈管理上的重要議題。生產排程決定了工作順序及產能利用率,目的是為了達到總完工時間最小;而產品運送策略則影響到企業的物流成本。過去研究大多將上述二議題分開討論。
然而,一個企業會同時面臨兩個問題,一是如何在交期內準時達交至顧客端,降低遲到或早到的成本;二則是如何決定運送策略以達到運送成本最小。因此,若能同時考慮此兩階段問題,並發展一套模式求解,進而達到總成本最小化。本研究探討單機組裝排程、多點運送的生產排程及航空運輸的配送問題,期望透過生產排程、航空班次及運送量之決定,達到早到、晚到及運送等總成本最小化,並先以數學規劃建立整合訂單排程與運送排程之排程模式,本研究將此問題建構為一個非線性整數規劃模型,並以套裝軟體Lingo 10.0 求解。數值例測試顯示於合理數據之小規模下,Lingo 10.0皆可在合理時間內求得最佳解,並將此問題發展一混合啟發式演算法,其測試結果顯示演算法之求解品質及效率優於套裝軟體Lingo 10.0。
Production scheduling and transportation strategies in manufacturing systems are two important topics in the fields of production or supply chain management. Production scheduling is usually used to decide which job to schedule next when a work center becomes available so as to reduce the total time span, while transportation strategy usually focuses on the amount shipped between distribution channels so as to reduce logistics costs. These two decisions are usually separately discussed in previous works.
However, since a business may simultaneously face the problems of how to deliver goods to customers on time in order to reduce the sum of early and delay costs, and how to distribute goods between distribution channels to minimize the shipping costs, it needs to develop a model to simultaneously deal with these two problems so as to minimize the total costs. This paper deals with a production scheduling and air-transportation problem with a single-machine and multi-delivery destinations. The purpose of this paper is to develop the production scheduling and transportation strategies in order to minimize the sum of early cost, delay cost and transportation cost. The mathematic model established in this research is a constrained non-linear integer programming problem. Lingo 10.0 was used to obtain solutions. The illustrations show that Lingo 10.0 can get optimal solutions within a reasonable amount of time under small scales. A hybrid heuristic is developed to find solutions to this problem. Computational experiments show that the proposed approach is superior to the well-known Lingo 10.0 software in terms of both solution quality and computational effectiveness.
摘要 I
ABSTRACT II
誌謝 III
目錄 IV
圖目錄 VI
表目錄 VII
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的與範圍 2
1.3 研究方法 3
1.4 研究架構與流程 3
第二章 文獻回顧 6
2.1 生產排程之相關文獻 6
2.1.1 流程式生產之相關文獻 8
2.2 運送排班策略之相關文獻 11
2.3 供應鏈整合與策略分析 12
第三章 問題描述與模式建構 16
3.1 問題描述 16
3.2 基本假設 17
3.3 模式構建 17
第四章 研究方法 24
4.1 基因演算法介紹 24
4.1.1 染色體編碼(Encoding) 25
4.1.2 初始母體(Initial Population) 26
4.1.3 適應值函數(Fitness Function) 26
4.1.4 遺傳運算子(Genetic Operators) 27
4.1.5 終止條件(Termination) 31
4.2 混合式基因演算法與簡例說明 34
第五章 數值例測試 43
5.1 數值例設計 43
5.1.2 數值例比較 47
5.3 實例分析 55
第六章 結論與建議 62
6.1 結論 62
6.2 建議 63
參考文獻 65


圖目錄
圖1.1 研究架構流程圖 5
圖3.1 生產排程運送架構圖 16
圖4.1 二元編碼圖 25
圖4.2 整數編碼圖 26
圖4.3 單點交配圖 28
圖4.4 兩點交配圖 29
圖4.5 字罩交配圖 30
圖4.6 單點突變圖 31
圖4.7 基因流程圖 33
圖4.8 染色體編碼圖 34
圖4.9 基因解碼圖 36
圖4.10 染色體交換配對過程 38
圖4.11 單點突變圖 40
圖4.12 混合式基因演算法流程圖 42
圖5.1 各問題規模組間求解時間之趨勢圖 46
圖5.2 各問題規模下組內、組間求解時間標準差之趨勢圖 46


表目錄
表4.1 初始母體基因組合 35
表4.2 染色體之適應值 37
表4.3 菁英政策選取之染色體 37
表4.4 染色體複製結果 38
表4.5 染色體交換配對前之基因組合 39
表4.6 染色體交換配對後之基因組合 39
表4.7 染色體突變結果 40
表5.1 問題規模大小內容 44
表5.2 訂單相關之各項參數測試值 44
表5.3 各問題規模下之指標值 45
表5.4 問題規模一數值例求解時間與求解品質比較 50
表5.5 問題規模二數值例求解時間與求解品質比較 51
表5.6 問題規模三數值例求解時間與求解品質比較 52
表5.7 問題規模四數值例求解時間與求解品質比較 53
表5.8 問題規模五數值例求解時間與求解品質比較 54
表5.5 訂單相關之各項參數測試值 55
表5.6 基本資料設定 56
表5.7 產品AP90之實際參數資料表 57
表5.8 產品AP90於問題規模一之測試結果(N =3) 58
表5.9 產品AP90於問題規模二之測試結果(N =5) 59
表5.10 產品AP90於問題規模三之測試結果(N =7) 60
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