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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 黃奎隆 | zh_TW |
dc.contributor.advisor | Kui-Long Huang | en |
dc.contributor.author | 陸冠綸 | zh_TW |
dc.contributor.author | Kuan-Lun Lu | en |
dc.date.accessioned | 2023-07-11T16:19:09Z | - |
dc.date.available | 2023-11-10 | - |
dc.date.copyright | 2023-07-11 | - |
dc.date.issued | 2022 | - |
dc.date.submitted | 2002-01-01 | - |
dc.identifier.citation | Ahn, K., Lee, K., Yeon, J., & Park, J. (2021). Congestion-aware dynamic routing for an overhead hoist transporter system using a graph convolutional gated recurrent unit., IISE Transactions, 54(8), 803-816 Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and Regression Trees.CRC press. Chen, T., & Guestrin, C. (2016). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, 785-794. Friedman, J. H. (2001). Greedy function approximation: a gradient boosting machine. Annals of statistics, 1189-1232. Gal, Y., & Ghahramani, Z. (2016). A theoretically grounded application of dropout in recurrent neural networks. Advances in neural information processing systems, 29, 1019-1027. Gurmu, Z., & Fan, W. (2014). Artificial Neural Network Travel Time Prediction Model for Buses Using Only GPS Data. Journal of Public Transportation, 17, 45-65. Huang, H., Pouls, M., Meyer, A., & Pauly, M. (2020). Travel time prediction using tree-based ensembles. In International Conference on Computational Logistics, 412-427. Jeong, R., & Rilett, R. (2004). Bus arrival time prediction using artificial neural network model. In Proceedings of the 7th International IEEE Conference on Intelligent Transportation Systems, 988-993. Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Liu, T.-Y. (2017). Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems, 30, 3146-3154. Kim, H., Lim, D. E., & Lee, S. (2020). Deep Learning-Based Dynamic Scheduling for Semiconductor Manufacturing With High Uncertainty of Automated Material Handling System Capability. IEEE Transactions on Semiconductor Manufacturing, 33(1), 13-22. Liang, S. K., & Wang, C. N. (2005). Modularized simulation for lot delivery time forecast in automatic material handling systems of 300 mm semiconductor manufacturing. International journal of advanced manufacturing technology, 26(5-6), 645-652. Liao, D. Y., & Wang, C. N. (2004). Neural-network-based delivery time estimates for prioritized 300-mm automatic material handling operations. IEEE Transactions on Semiconductor Manufacturing, 17(3), 324-332. Liu, H., Xu, H., Yan, Y., Cai, Z., Sun, T., & Li, W. (2020). Bus Arrival Time Prediction Based on LSTM and Spatial-Temporal Feature Vector. IEEE Access, 8, 11917-11929. Luo, S., Zhang, L., & Fan, Y. (2021). Dynamic multi-objective scheduling for flexible job shop by deep reinforcement learning. Computers & Industrial Engineering, 159, 107489. Mei, Z., Xiang, F., & Zhen-hui, L. (2018). Short-term traffic flow prediction based on combination model of xgboost-lightgbm. In Proceedings of the 2018 International Conference on Sensor Networks and Signal Processing (SNSP), 322-327. Nazzal, D., & McGinnis, L. F. (2007). Expected Response Times for Closed-Loop Multivehicle AMHS. IEEE Transactions on Automation Science and Engineering, 4(4), 533-542. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Dubourg, V. (2011). Scikit-learn: Machine learning in Python. the Journal of machine Learning research, 12, 2825-2830. Salminen, J., Corporan, J., Marttila, R., Salenius, T., & Jansen, B. J. (2019). Using Machine Learning to Predict Ranking of Webpages in the Gift Industry: Factors for Search-Engine Optimization. In Proceedings of the 9th International Conference on Information Systems and Technologies,1-8. Wang, D., Zhang, J., Cao, W., Li, J., & Zheng, Y. (2018). When will you arrive? estimating travel time based on deep neural networks. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence and Thirtieth Innovative Applications of Artificial Intelligence Conference and Eighth AAAI Symposium on Educational Advances in Artificial Intelligence;305, 2500–2507. Wang, D., Zhang, Y., & Zhao, Y. (2017). LightGBM: An Effective miRNA Classification Method in Breast Cancer Patients. In Proceedings of the 2017 International Conference on Computational Biology and Bioinformatics (ICCBB 2017). Association for Computing Machinery, 7–11. Wang, F., Cheng, H., Dai, H., & Han, H. (2021). Freeway Short-Term Travel Time Prediction Based on LightGBM Algorithm. Paper presented at the IOP Conference Series: Earth Environmental Science; 638, 12–29. Yang, S., Wu, J., Du, Y., He, Y., & Chen, X. (2017). Ensemble learning for short-term traffic prediction based on gradient boosting machine. Journal of Sensors, 2017, 1-15. Zhan, Z.-H., You, Z.-H., Li, L.-P., Zhou, Y., & Yi, H.-C. (2018). Accurate prediction of ncRNA-protein interactions from the integration of sequence and evolutionary information. Frontiers in Genetics, 9, 458. 張博毓. (2014). 大型晶圓廠自動物料搬運系統高效率運輸之研究. 逢甲大學, 顏豪君. (2005). 快速評估12吋晶圓廠AMHS的模擬方法. 國立交通大學, | - |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/87685 | - |
dc.description.abstract | 本研究為探討連接多個製程機台間之自動搬運設備其搬運時間預測,由於搬運工作量分布的不平衡,導致部分機台上的在製品無法及時搬離上游機台輸出端或無法及時將物料搬運至下游機台進行加工,因回堵及缺料等原因造成加工機台的停機,間接影響部分廠區生產排程規劃。因此本研究透過梯度提升樹架構之演算法預測自動搬運設備之總搬運時間,以利掌握整體產線狀況,進而規劃出更為緊湊、更高產能之生產排程。在特徵構造的階段根據分析原始資料、製程特性與指令派工邏輯,創造出更多有利於模型訓練之特徵,如指令產生當下在製品堆積在機台輸出端數量、搬運設備之間暫存區狀態、搬運路徑過往搬運平均時間、搬運路徑長等特徵。本研究為預測數值之迴歸問題,其資料型態包含連續型的特徵與類別型的特徵,與類神經網路架構模型,如長短期記憶網路(LSTM)、深度神經網路(DNN)比較後,得出梯度提升樹(GBDT)架構之演算法更有利於同時處理這兩類型特徵值,因此採取GBDT作為預測模型。預測模型分為全範圍設備預測模型與單一設備預測模型,結合兩者每當指令完成一站搬運後再度進行預測以修正誤差,並將平均誤差由原本使用原始特徵的88%透過特徵構造、調整模型與分段預測後下降至17%。 | zh_TW |
dc.description.abstract | The study is aim to predict the transportation time of AMHSs between numbers of process machines in panel industry. Some of the AMHSs have heavier workload which usually can’t finish the tasks in time. It will cause the upstream machine blocked or downstream machine shut down due to lack of material. This research creates kinds of new features based on original features to help us improve the prediction accuracy, such as moving distance, buffer status, queue length. In this study, data include not only numerical type but also categorical type. Gradient boosting decision tree (GBDT) has the greatest result in two types of data when compare with long short-term memory (LSTM) and deep neuron network (DNN). This is the main reason why GBDT be chosen as prediction model. With generate new features and multi-phase prediction, which combined with single machine model and the whole area model will renew the prediction time when a task arrives at the next AMHS. Finally, MAPE can be reduced from 88% to 17% by feature construction, fine-tuning model and multi-phase prediction. | en |
dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-07-11T16:19:09Z No. of bitstreams: 0 | en |
dc.description.provenance | Made available in DSpace on 2023-07-11T16:19:09Z (GMT). No. of bitstreams: 0 | en |
dc.description.tableofcontents | 中文摘要 i ABSTRACT iii 目錄 iv Chapter 1 緒論 1 1.1. 研究背景與動機 1 1.2. 研究目的與架構 1 Chapter 2 文獻回顧 4 2.1 自動化無人搬運系統 4 2.2 AMHS搬運時間預測 5 2.3 旅行時間預測 5 2.4 GBDT 7 Chapter 3 問題描述與研究方法 12 3.1 問題描述 12 3.1.1 自動搬運設備描述 13 3.1.2 預測目標 15 3.1.3 評估指標 17 3.1.4 資料洩漏 (Data Leakage) 17 3.2 研究方法 18 Chapter 4 案例分析與單台模型預測 20 4.1 案例基本概述 20 4.1.1 研究範圍描述 20 4.1.2 資料描述 21 4.2 資料清理 22 4.2.1 異常資料刪除規則 23 4.2.2 異常停頓等待 23 4.2.3 系統判定異常導致移動時間過長 24 4.2.4 資料在搬運設備間遺失 25 4.3 特徵構造 26 4.3.1 等待執行指令數量相關特徵 27 4.3.2 路徑、端點編號與距離相關特徵 29 4.3.3 兩AMHS間之暫存區狀態 31 4.3.4 歷史資料 32 4.3.5 指令間接收時間差 34 4.3.6 預測插隊與被插隊次數 36 4.3.7 其他特徵 39 4.4 單台模型預測 41 4.4.1 以特徵類別進行分析 43 4.4.2 以搬運設備進行分析 47 4.5 第四章小結 48 Chapter 5 資料合併與全域預測 49 5.1 資料合併 49 5.2 合併預測及結果 53 5.3 分段預測作法及結果 57 5.4 第五章小結 59 Chapter 6 結論 60 6.1 總結 60 6.2 未來研究方向 60 參考文獻 62 | - |
dc.language.iso | zh_TW | - |
dc.title | 以梯度提升樹之演算法預測自動搬運設備之搬運時間–以面板產業為例 | zh_TW |
dc.title | Using gradient boosting decision tree to predict the total transportation time between multiple automated material handling systems-Take the panel industry as an example | en |
dc.type | Thesis | - |
dc.date.schoolyear | 110-2 | - |
dc.description.degree | 碩士 | - |
dc.contributor.oralexamcommittee | 藍俊宏;楊朝龍 | zh_TW |
dc.contributor.oralexamcommittee | Jun-Hong Lan;Chao-Long Yang | en |
dc.subject.keyword | 機器學習,搬運時間預測,自動搬運設備,梯度提升樹,多階段預測, | zh_TW |
dc.subject.keyword | machine learning,travel time prediction,automatic material handling system(AMHS),gradient boosting decision tree,multi-phase prediction, | en |
dc.relation.page | 64 | - |
dc.identifier.doi | 10.6342/NTU202201392 | - |
dc.rights.note | 同意授權(限校園內公開) | - |
dc.date.accepted | 2022-07-11 | - |
dc.contributor.author-college | 工學院 | - |
dc.contributor.author-dept | 工業工程學研究所 | - |
顯示於系所單位: | 工業工程學研究所 |
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