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系統識別號 U0026-3107202021135300
論文名稱(中文) 使用以盒形圖為基礎的分數階灰預測模型進行新產品之短期需求預測
論文名稱(英文) Employing Box-Plot based Fractional Grey Models for Forecasting New Product Short Demands
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 108
學期 2
出版年 109
研究生(中文) 黃文奎
研究生(英文) Wen-Kuei Huang
學號 R37071045
學位類別 碩士
語文別 中文
論文頁數 34頁
口試委員 指導教授-利德江
口試委員-張哲榮
口試委員-葉俊吾
口試委員-戴文禮
中文關鍵字 新產品需求預測  短期時間序列資料  灰預測模型  分數階灰預測模型  盒形圖 
英文關鍵字 Demand forecast of new product  short-term time series  grey models  fractional grey model  box-plot 
學科別分類
中文摘要 企業投注新產品開發之成本高昂,使用下游廠商或終端使用者的需求預測做為決斷參考是一可行之策。然此種短期時間序列資料有其學習困難性,在過往的方法中,分數階灰預測模式(fractional grey model, FGM) 已被證實其透過分數累加方式,較傳統的整數累加方式的GM模型有更佳的準確率,然而如何決定適度的分數階值,許多文獻以不同最佳化演算法進行;除分數階值外,如何設定適當的分數階背景值,以進一步地提高其預測準確率,亦值得探究。本研究使用盒形圖,推估資料的發生趨勢,並將此與FGM結合,稱為盒形圖為基礎的分數階灰預測模型(box-plot based FGM, BFGM)。實驗部分,以某知名的設備商為研究對象,透過其生產之商品屬性與公開測試資料來進行效果驗證,經實驗結果顯示,BFGM比FGM有更佳的預測結果。
英文摘要 The cost of investing in new product development is high, and it is a feasible way to use demand forecasts from customer or end-users as a decisive reference. However, this short-term time series data has its learning difficulties. In the past, the fractional grey prediction model (fractional grey model, FGM) has been proved that its cumulative method is better than the traditional integer cumulative of grey model (GM) model. There are many researches using different optimal algorithms to determine the moderate score order. And how to set the coefficient sets of α in grey model is also worth exploring. Therefore, this research reveals a new grey model which used box plot to estimate the trend of data and combined this with FGM, known as the box-plot-based fractional scale prediction model (box-plot-based FGM, BFGM) to improve the accuracy of predictors by setting the coefficient sets of α in traditional grey model. In the experimental, the examined dataset that collected from a well-known equipment manufacturer as the research object. The result verified the effect through the commodity attributes and public test data of its production, and the experimental results show that BFGM has better prediction results than FGM.
論文目次 摘要 ...............II
Abstract ...............III
誌謝 ...............XII
目錄 ...............XIII
表目錄 ...............XV
圖目錄 ...............XVI
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 ...............2
1.4. 研究範圍與限制 ...............3
1.5. 研究流程 ...............3
第二章 文獻探討 ...............5
2.1 常用預測法 ...............5
2.1.1 定量預測法 ...............5
2.1.2 定性預測法 ...............6
2.2 灰色系統理論 ...............7
2.2.1 灰色系統研究內容 ...............8
2.2.2 傳統灰預測模型GM(1,1) ...............10
2.2.3 分數階累加灰預測模型FGM(1,1) ...............10
2.2.4 灰預測模型的應用 ...............12
2.3 小結 ...............13
第三章 研究方法 ...............15
3.1 α值推估 ...............16
3.1.1 盒形圖簡介 ...............16
3.1.2 值域推估 ...............17
3.1.3. 落點資訊 ...............18
3.2 考量落點資訊之灰預測模型 ...............19
第四章 實例驗證 ...............23
4.1 資料蒐集 ...............23
4.1.1 個案公司資料說明 ...............23
4.1.2 MOEA 資料說明 ...............23
4.2 實驗設計 ...............24
4.3 結果驗證 ...............25
第五章 結論與未來展望 ...............28
5.1 研究結果 ...............28
5.2 建議與未來展望 ...............28
參考文獻 ...............29
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【英文部分】
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