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研究生:侯琬伊
研究生(外文):Wan-Yi Hou
論文名稱:應用資料探勘建構物流中心訂單需求模式
論文名稱(外文):Applying Data Mining Techniques to Model the Order Demand of Distribution Center
指導教授:陳穆臻陳穆臻引用關係
口試委員:陳凱瀛黃承龍
口試日期:2006-06-20
學位類別:碩士
校院名稱:國立臺北科技大學
系所名稱:商業自動化與管理研究所
學門:商業及管理學門
學類:企業管理學類
論文種類:學術論文
論文出版年:2006
畢業學年度:94
語文別:中文
論文頁數:130
中文關鍵詞:物流中心資料探勘關聯法則關聯式分類序列樣式時間區間序列樣式
外文關鍵詞:Distribution CenterData MiningAssociation RuleAssociative ClassificationSequential PatternTime-Interval Sequential Pattern.
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  • 被引用被引用:3
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隨著消費方式轉變為「少量多樣且一次購足」的型式,生產者也從一貫作業之生產邏輯調整為專業分工的方式。因此,物流產業在供應鏈中扮演角色變得更為重要。為達成最佳化物流系統效能、提升服務水準及設計良善配送路徑規劃排程之目的而衍生專業物流中心(Distribution Center, DC)的設置需求,也成為必然之發展趨勢。物流中心在商品的實體配銷過程中扮演集中分配的角色機能,內部作業中,尤其以訂單處理與揀貨作業最為重要與繁雜,訂單揀貨能否於有限時間內完成,密切關係物流中心之運作效率與服務水準。但供應鏈中存在著需求不確定性,需求不確定性反應在物流中心上將產生「緊急插單(Rush Orders)」,緊急插單表示物流中心無法掌握訂單出現之時機與規則,其可能導致物流中心之庫存無法滿足該訂單之需求,使服務水準下降等問題;此外,緊急插單亦可能無法與其它訂單共同批次揀貨處理,而造成整體揀貨效率降低,故物流中心能否掌握訂單需求規則是訂單揀貨作業能否有效率進行的重要前提之一。此外,部分物流中心甚至規模擴大直接與終端顧客接觸,並延伸涉及產品設計與開發自有品牌,因此,物流中心更有主動瞭解市場需求之必要性。本研究藉由資料探勘方法中之關聯法則(Association Rule)、關聯式分類(Associative Classification)以及序列樣式探勘(Sequential Pattern)方法,建構物流中心訂單需求模式。協助物流中心瞭解訂購產品之時機、訂購產品之訂單與顧客的屬性以及產品訂購序列特徵,期望藉由掌握訂單需求與減少緊急插單,以降低訂單別揀貨頻次與訂單需求不確定性之影響,進而達成提升訂單批次揀貨效能及主動瞭解顧客需求等之目的。
With the way of customers’ purchasing behavior being converted to one stop shopping, small size and high diversity, manufacturers and distributors are changing their operations from vertical integration to specialized divisions. Thus, the role of logistics is becoming more important in the supply chain. To optimize the efficiency of a logistics system, to raise service level, and to design a good routing plan, the distribution center (DC) is being desired sharply, the need of which is increasing and the setting of which is a virtually inevitable trend. DC plays a role of centralized distribution in the process of physical products’ distribution. Among internal operations in DC, order processing and order picking are especially the most important and complicated. Whether the order picking is finished within the limited time highly influences the operation efficiency and service levels. Due to demand uncertainty existed in supply chain, such uncertainty reflected in DC is “rush order.” Rush orders represent that DC cannot know the timing and the rule of order placing well, which may result in that the inventory in DC cannot fulfill the demand of rush orders, and thus decrease the service level. Additionally, rush orders cannot be batch picked together with the other orders, which decreases the efficiency of order picking. Therefore, provided that DC can obtain the rules of order demand, the operations of order picking can be conducted more efficiently. Moreover, the scale of some DCs is so huge that such DCs can contact with end consumers, design their own products, and develop their own Ordnance Bench Mark (OBM). Consequently, it is imperative for DC to understand the market demand actively. In this thesis, data mining techniques including association rule, associative classification, and sequential pattern are applied to establish an order demand model for DC, which can aid DC to realize the timing of product purchasing, and the properties of customers and orders of purchased products, and the sequential characteristics of purchased products. The proposed approach in this thesis intends to acquire the order demand and decrease rush orders, which can decrease the frequency of single-order picking and the effect caused by order demand uncertainty, and then increase the frequency of batch picking and understand the customers’ needs actively.
摘 要 i
ABSTRACT ii
誌 謝 iv
目 錄 v
表目錄 viii
圖目錄 x
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機 4
1.3 研究目的 6
1.4 研究流程與架構 8
1.5 章節總結 10
第二章 文獻探討 11
2.1 物流管理 11
2.2 物流中心機能 13
2.2.1 物流中心之定義 16
2.2.2 物流中心於供應鏈上所扮演之角色 17
2.2.3 物流中心之作業流程 20
2.2.4 訂單批次處理作業 24
2.3 資料探勘 26
2.3.1 資料探勘之定義 26
2.3.2 資料探勘之技術彙整概述 27
2.3.3 資料探勘之應用 29
2.4 關聯法則 31
2.4.1 關聯法則之定義 31
2.4.2 關聯法則之重要演算法彙整 33
2.5 關聯式分類 35
2.5.1 關聯式分類之定義 36
2.5.2 關聯式分類之重要演算法彙整 38
2.6 序列樣式 41
2.6.1 序列樣式之定義 41
2.6.2 序列樣式之重要演算法彙整 45
2.7 關聯法則與序列樣式之時間性 46
2.7.1 具時間性之關聯法則 46
2.7.2 具時間性之序列樣式 47
第三章 探勘訂單需求模式方法 49
3.1 概述 49
3.2 資料前處理與轉換 50
3.3 關聯法則演算法 50
3.3.1 Apriori演算法流程說明 50
3.3.2 簡例說明 56
3.4 關聯式分類演算法 60
3.4.1 CBA演算法流程說明 60
3.4.2 簡例說明 68
3.5 序列樣式演算法 73
3.5.1 AprioriAll演算法流程說明 73
3.5.2 簡例說明 79
3.6 建構訂單需求網狀模式 83
第四章 實例應用 87
4.1 資料來源與資料選取 87
4.2 資料前處理與轉換 90
4.3 關聯法則分析 95
4.4 關聯式分類分析 96
4.5 序列樣式分析 98
4.6 訂單需求分析系統 100
4.7 物流中心訂單需求網狀模式 117
4.8 情境分析與管理應用 119
第五章 結論與建議 121
5.1 研究結果 121
5.2 未來研究建議 123
參考文獻 125
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