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系統識別號 U0026-1706201921445400
論文名稱(中文) 以基因演算法與整體趨勢擴散技術為基礎的非等間距灰模型預測供應商庫存失效品數量
論文名稱(英文) Using GA- and MTD-based Non-Equigap Grey Models for Predicting Defective Item Quantities of Vendor Inventory
校院名稱 成功大學
系所名稱(中) 工業與資訊管理學系碩士在職專班
系所名稱(英) Department of Industrial and Information Management (on the job class)
學年度 107
學期 2
出版年 108
研究生(中文) 李佳穎
研究生(英文) Chia-Yin,Lee
學號 R37061197
學位類別 碩士
語文別 中文
論文頁數 62頁
口試委員 指導教授-利德江
口試委員-戴文禮
口試委員-葉俊吾
中文關鍵字 供應商存貨管理  存貨失效品  非等間距灰預測模式  基因演算法  整體趨勢擴展技術 
英文關鍵字 vendor-managed-inventory  inventory defective product  non-equigap grey model  genetic algorithm  mega-trend-diffusion 
學科別分類
中文摘要 在今日競爭日益劇烈的全球市場中,有效的進行供應商存貨管理是企業追求利潤、競爭力、與永續經營的控制策略。然若存貨過剩,除增加企業成本,亦可能導致存貨失效品的產生。為了及時滿足客戶需求,企業需要立即提供新物料並返還失效品,此種換貨退貨流程,將產生額外龐大的成本,如何依據短期需求之變化數據以掌握存貨失效品的數量來做為庫存準備之參考是一項重要的議題。非等間距灰預測模式NGM(1,1)在過往的研究中已被證實其對於短期時間序列資料具有高度的預測準確性,然其亦被驗證可透過背景值的搜尋而改善其預測值,其中背景值是由參數α所決定。因此本研究使用整體趨勢擴散技術(mega-trend-diffusion, MTD)學習α的初始值,並透過基因演算法(genetic algorithm, GA)搜尋最適α值,稱為GA-MNGM(1,1)。本研究之個案公司為液晶面板產業中之供應商,並以兩筆從個案公司所取得之存貨失效品數量進行實驗,結果顯示使用MNGM(1,1)所產生的預測值確較NGM(1,1)以及其他衍伸方法,包含ANGM(1,1)、BNGM(1,1)、MNGM(1,1)有更佳的準確性。
英文摘要 The TFT-LCD (thin film transistor liquid crystal display) industry is the mainstream of flat panel display development in Taiwan, However, in order to provide better services to customers, most Taiwanese companies have established their warehouse in China for vendor-managed-inventory (VMI).Due to industry characteristics, product diversification and short product life cycle, it is difficult for suppliers to control VMI's inventory level, especially for the control of defective product returns. The prediction on future inventory defective product can be consider, in order to avoid shortage inventory and maintain high customer satisfaction. The non-equigap grey model (NGM) is applied to short-term time series data and has proven to be an effective tool. By determining the value of the parameter (α values) to obtain a more appropriate background value, the NGM prediction can be effectively improved. This paper uses the mega-trend-diffusion (MTD) technique to learn the initial value of α, and then uses genetic algorithm (GA) to adjust these values to the optimal, This NGM with the best alpha value is therefore called GA-MNGM (1,1). In the experiment, two real-world case data sets collected by TFT-LCD suppliers were used for validity verification. The experimental results show that the overall result of GA-MNGM(1,1) is better than MNGM(1,1) and its extended version.
論文目次 摘要 I
致謝 VIII
表目錄 XI
圖目錄 XII
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機 5
1.3 研究目的 7
1.4 研究範圍 7
1.5 研究流程 7
第二章 文獻探討 10
2.1 需求預測 10
2.1.1 需求預測特性 11
2.1.2 需求預測步驟 12
2.1.3 需求預測方法 14
2.2 灰色系統理論 17
2.2.1 灰色系統理論研究內容 18
2.2.2 傳統灰預測模型GM(1, 1) 20
2.2.3 非等間距灰預測模型NGM(1,1) 22
2.2.4 灰色預測的改良 25
2.3 基因演算法 26
2.3.1 基因演算法之特性 27
2.3.2 基因演算法之步驟 28
2.4 小結 34
第三章 研究方法 36
3.1 計算背景值 36
3.2 整體趨勢擴展技術 38
3.3 基因演算法求取最適參數mathbf{alpha}值 40
3.4重建NGM(1,1)模型 44
第四章 實例驗證 45
4.1 個案資料說明 45
4.2 實驗環境 47
4.2.1 執行平台 47
4.2.2 實驗方法 47
4.2.3 預測誤差評估指標 48
4.3 實驗結果 48
第五章 結論 53
5.1 研究結論 53
5.2 未來研究方向 54
參考文獻 55
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