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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 吳政鴻(Cheng-Hung Wu) | |
| dc.contributor.author | Kuei-Wen Chang | en |
| dc.contributor.author | 張貴雯 | zh_TW |
| dc.date.accessioned | 2022-11-25T06:33:43Z | - |
| dc.date.copyright | 2021-07-09 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-06-28 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/82210 | - |
| dc.description.abstract | 在工業與商業應用領域的混合資料集中，經常會面臨類別屬性和數值屬性之間具有複雜交互作用的問題，在商業領域方面，價格預測是重要的商業問題，而產品與服務價格同時受到各種類別與數值屬性影響，例如電力需求和價格會受到不同季節、商務活動與化石燃料價格等因素影響；而製造系統中，不同機器類和產品屬性組合，對生產率有不同程度的影響，過往在面對混合資料集的預測問題，對於複雜的產品組合模式普遍針對單一資料集建構預測模型，其預測模型無法因應生產現場的產品組合變化而動態調整，導致後續安排加工時間不易並難以精準規劃產品交期。 本研究將針對混合資料集，以類別屬性將資料集進行階層式的切分，藉由階層式展開方法，可以改善過去傳統機器學習方法的缺點，在減少運算的複雜度的同時建立準確度更高的預測模型，當系統中的特徵或屬性增加時，所需要訓練的模型不會隨之大幅度增加，仍然能夠維持有效率的運算效能，另外透過階層式展開與模型選擇，追溯機器學習的推論結果以提升模型的解釋力，並且以階層式展開模型應用至非等效機台的排程問題。數值分析結果顯示，在半導體混合資料集中，與XGBoost模型相比，運用階層式展開方法可以降低17.7%的均方根誤差值，且與張鈺欣(2020)提出的分層組合模型更具運算效能與預測效能；由於階層式展開預測模型的準確度與精確度，以其預測非等效機台的排程問題的最佳化模型參數，能夠有效提升排程的效果，且掌握生產系統中的不確定因子。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-25T06:33:43Z (GMT). No. of bitstreams: 1 U0001-2406202117124900.pdf: 2850132 bytes, checksum: 0a3d01704b40b8f00e113bfd5a21bd02 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 誌謝 I 中文摘要 II ABSTRACT III 目錄 IV 圖 目 錄 VII 表 目 錄 VIII 第一章 緒論 1 1.1.1 混合資料集造成預測困難 1 1.1.2 資料維度過高導致預測效能不足 2 1.1.3 機器學習模型的透明度與可解釋性問題 3 1.1.4 環境不確定性的挑戰 4 1.1.5 研究背景與動機結論 5 1.2 研究目的 5 1.3 研究方法與流程 6 第二章 文獻回顧 8 2.1 分群方法及其應用 8 2.1.1 區分聚類法 (Partitional clustering) 9 2.1.2 階層聚類法 (Hierarchical clustering) 9 2.2 集成學習方法及其應用 10 2.2.1 提升方法(Boosting) 10 2.2.2 引導聚集法(Bagging) 11 2.2.3 堆疊法(Stacking) 12 2.2.4 XGBoost演算法 12 2.3 機器學習模型的透明度與可解釋性問題 14 2.4 隨機最佳化方法 15 2.5 非等效平行機台排程 15 2.6 文獻探討小節 17 第三章 階層式展開演算法 18 3.1 原始資料集描述與資料前處理 18 3.2 各階層展開定義與相對應資料集 19 3.2.1 第零階層展開定義與相對應之資料集 19 3.2.2 第一階層展開定義與相對應之資料集 20 3.2.3 第N階層展開定義與相對應之資料集 21 3.3 階層式展開預測模型定義 23 3.4 預測模型之訓練 23 3.4.1 預測模型之屬性 24 3.4.2 模型超參數設定 25 3.5 階層式展開方法 26 3.5.1 以預測效能為基準的屬性選擇指標 27 3.5.2 以預測值距離為基準的屬性選擇指標 29 3.6 預測模型驗證方法 31 第四章 非等效平行機台排程 34 4.1 生產系統之資料集 34 4.1.1 歷史資料 34 4.1.2 產生資料集 35 4.2 最佳化模型 36 4.2.1 確定性最佳化模型 36 4.2.2 隨機最佳化模型 36 4.2.3 最佳化模型參數 37 4.3 排程結果驗證 39 第五章 資料集驗證與數值分析 40 5.1 資料集介紹 40 5.2 階層式展開方法與模型選擇驗證 42 5.2.1 半導體資料集之階層展開與預測效能驗證 43 5.2.2 全州保險理賠資料集之階層展開與預測效能驗證 51 5.2.3 車子資料集資料集之階層展開與預測效能驗證 64 5.2.4 紐約Airbnb資料集之階層展開與預測效能驗證 71 5.2.5 模型透明度與可解釋性 77 5.2.6 階層式展開預測模型驗證小結 78 5.3 非等效平行機台排程驗證 81 第六章 結論與未來研究方向 88 6.1 結論 88 6.2 未來研究方向 89 參考文獻 91 | |
| dc.language.iso | zh-TW | |
| dc.subject | 排程 | zh_TW |
| dc.subject | 分層方法 | zh_TW |
| dc.subject | 分群 | zh_TW |
| dc.subject | 預測模型 | zh_TW |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | Hierarchical method | en |
| dc.subject | Manufacturing | en |
| dc.subject | Machine learning | en |
| dc.subject | Scheduling | en |
| dc.subject | Regression analysis | en |
| dc.subject | Prediction method | en |
| dc.subject | Clustering | en |
| dc.title | 混合資料集之階層式展開集成學習預測方法 | zh_TW |
| dc.title | Hierarchical Expansion Approach for Ensemble Learning and Prediction in Mixed Datasets | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 洪一薰(Hsin-Tsai Liu),黃奎隆(Chih-Yang Tseng),藍正宏 | |
| dc.subject.keyword | 分層方法,分群,預測模型,機器學習,排程, | zh_TW |
| dc.subject.keyword | Hierarchical method,Clustering,Prediction method,Regression analysis,Machine learning,Manufacturing,Scheduling, | en |
| dc.relation.page | 96 | |
| dc.identifier.doi | 10.6342/NTU202101129 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2021-06-28 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 工業工程學研究所 | zh_TW |
| dc.date.embargo-lift | 2024-07-31 | - |
| 顯示於系所單位： | 工業工程學研究所 | |
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