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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 黃奎隆(Kwei-Long Huang) | |
| dc.contributor.author | Chih-Hsiang Nien | en |
| dc.contributor.author | 粘至翔 | zh_TW |
| dc.date.accessioned | 2022-11-25T05:33:50Z | - |
| dc.date.available | 2025-08-01 | |
| dc.date.copyright | 2021-11-09 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-08-19 | |
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Using machine learning and big data approaches to predict travel time based on historical and real-time data from Taiwan electronic toll collection. Soft Computing, 22(17), 5707-5718. 27. Breiman, L., Friedman, J., Stone, C. J., Olshen, R. A. (1984). Classification and regression trees. CRC press. 28. Cheng, J., Li, G., Chen, X. (2018). Research on travel time prediction model of freeway based on gradient boosting decision tree. IEEE Access, 7, 7466-7480. 29. Li, X., Bai, R. (2016). Freight vehicle travel time prediction using gradient boosting regression tree. In 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA), 1010-1015. 30. Natekin, A., Knoll, A. (2013). Gradient boosting machines, a tutorial. Frontiers in neurorobotics, 7, 21. 31. Chien, S. I. J., Kuchipudi, C. M. (2003). Dynamic travel time prediction with real-time and historic data. Journal of Transportation Engineering, 129(6), 608-616. 32. Lin, H. E., Zito, R., Taylor, M. (2005). 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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/81999 | - |
| dc.description.abstract | 隨著全球疫情持續延燒，大大改變了人們原本的生活方式，也侷限了人們的社交行為，遠距學習與線上會議已是社會的常態，同時也加劇了企業、校園以及家庭對於3C產品的需求。面板和半導體產業因為疫情的關係，生產業務反而逆向急速成長。雖然國內面板廠和半導體廠的高科技生產設備以及技術都已日漸成熟，邁向高度自動化、電腦整合化，然因典型的生產流程大多將物料搬運設備視為服務或支援系統，普遍採用需求發生時才派遣的即時派工策略，當生產作業驟增時會發生搬運系統塞車、延誤、指令大量累積等，以致搬運效率明顯不佳。當工廠的生產作業爆量增加，在產能全開且吃緊下常發現依循既定法則執行的自動物料搬運系統，無法進行有效物料搬運，成為訂單驟增下的生產瓶頸。具有智慧能迎合需求變動敏捷派遣的物料搬運系統，已為高科技業生產系統邁入智慧製造不可避免的挑戰。 本研究案將著重在給定生產排程下TFT-LCD面板搬運的議題，主要研究範圍為自動倉儲設置與無人搬運車連結的搬運系統，藉由機器學習方法來對總運輸時間做出預測，從已知的歷史資料加以分析，利用整理各個需搬運物件在各站點的等候時間，以及等候搬運之物件數量，以及各物件在各個站點之間所需要的傳送時間，同時也考慮各個物件搬運的優先權重等，並且根據工廠實際狀況模擬可能發生之問題，得出資料中之規律，並利用此規律來對未來的資料進行預測。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-25T05:33:50Z (GMT). No. of bitstreams: 1 U0001-1808202114570100.pdf: 3384450 bytes, checksum: 16c8ebdf25c2a8dcf7e6a18c14e92d7b (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 中文摘要 i Abstract ii 目錄 iii 圖目錄 iv 表目錄 vi 第一章 緒論 1 1.1 物料控制系統 1 1.2 研究背景與動機 2 1.3 研究目的與架構 2 第二章 文獻探討 5 2.1 旅行時間問題 5 2.2 機器學習 9 2.3 決策樹演算法 13 第三章 問題描述以及研究方法 18 3.1 問題描述 18 3.2 研究方法 20 第四章 案例分析與結果 24 4.1 特徵參數定義 24 4.2 自動倉儲設備 25 4.3 自動引導搬運車 39 第五章 運輸設備合併之實驗結果 46 5.1 資料合併說明 46 5.2 自動倉儲設備A與自動引導搬運車資料合併之預測結果 47 5.3 自動倉儲設備A與自動倉儲設備B資料合併之預測結果 50 5.4 三台運輸設備資料合併之預測結果 53 第六章 結論 58 6.1 研究總結 58 6.2 未來方向 59 參考文獻 61 | |
| dc.language.iso | zh-TW | |
| dc.subject | 自動倉儲設置 | zh_TW |
| dc.subject | 資料分析 | zh_TW |
| dc.subject | 時間預測 | zh_TW |
| dc.subject | 無人搬運車 | zh_TW |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | data analysis | en |
| dc.subject | automatic stocker | en |
| dc.subject | automation guided vehicle | en |
| dc.subject | machine learning | en |
| dc.subject | time prediction | en |
| dc.title | 機器學習於無人搬運車與自動倉儲設置間之搬運時間預測 | zh_TW |
| dc.title | Machine Learning for Transportation Time Prediction between AGV and Automatic Stocker | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 楊烽正(Hsin-Tsai Liu),丁慶榮(Chih-Yang Tseng) | |
| dc.subject.keyword | 機器學習,自動倉儲設置,無人搬運車,資料分析,時間預測, | zh_TW |
| dc.subject.keyword | automatic stocker,automation guided vehicle,machine learning,time prediction,data analysis, | en |
| dc.relation.page | 64 | |
| dc.identifier.doi | 10.6342/NTU202102468 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2021-08-19 | |
| 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 | 2025-08-01 | - |
| 顯示於系所單位： | 工業工程學研究所 | |
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