Responsive image
博碩士論文 etd-0725125-174037 詳細資訊
Title page for etd-0725125-174037
論文名稱
Title
基於多指標於時序上表現以輔助倉儲規劃分析與決策之視覺化方法
Using Data Visualization to Support the Analysis and Decision Making in Warehouse Planning from Multiple Performance Indices over Time
系所名稱
Department
畢業學年期
Year, semester
語文別
Language
學位類別
Degree
頁數
Number of pages
116
研究生
Author
指導教授
Advisor
召集委員
Convenor
口試委員
Advisory Committee
口試日期
Date of Exam
2025-07-11
繳交日期
Date of Submission
2025-08-25
關鍵字
Keywords
倉儲規劃、模擬分析、資料視覺化、瓶頸辨識、決策支援系統
data visualization, warehouse planning, simulation analysis, bottleneck identification, decision support system
統計
Statistics
本論文已被瀏覽 261 次,被下載 0
The thesis/dissertation has been browsed 261 times, has been downloaded 0 times.
中文摘要
面對即時性與效率要求,企業仰賴倉儲營運資料進行異常偵測與流程優化,以提升整體表現。為降低規劃風險,企業常以模擬方式預先評估策略,但隨著模型複雜度增加,傳統表格與靜態圖表已難支援多面向、具時序性的分析需求。本研究透過專家訪談歸納倉儲決策的核心需求,據此設計 WISE 系統,結合視覺化與互動操作,協助使用者掌握方案差異、追蹤變化軌跡與診斷瓶頸,以提升倉儲規劃決策效率與精準度。為全面評估系統效用,本研究採用使用者研究與領域專家訪談兩種方式進行評估。首先,邀請一般使用者實際操作系統,並以 Excel 為對照系統,透過多項量化指標進行成效比較;其次,蒐集具倉儲規劃經驗之專家針對系統功能與應用潛力的回饋,從專業觀點檢視其實用性與可行性。
Abstract
In response to increasing demands for timeliness and efficiency in supply chain operations, enterprises are relying more on warehouse operational data for anomaly detection and process optimization to improve overall performance. To reduce planning risks, simulation is often used to evaluate strategies in advance. However, as simulation models grow in complexity, traditional tools such as spreadsheets and static charts struggle to support multi-dimensional and time-sensitive analysis needs. This study conducted interviews with experts to identify key requirements in warehouse decision-making and developed the WISE system. WISE integrates visualization and interactive features to help users compare alternative strategies, trace performance trends, and diagnose bottlenecks, thereby enhancing the efficiency and accuracy of decision-making. We used user study and expert interview to evaluate the system. In the user study, we compared WISE against the control system across multiple quantitative metrics. After the expert interview, we collected feedback from three warehouse planning experts to assess the system’s functionality and potential in practical use.
目次 Table of Contents
論文審定書 i
誌謝 ii
摘要 iii
Abstract iv
目錄 v
圖次 viii
表次 xii
第一章 緒論 1
第一節、研究背景 1
第二節、研究問題 2
第二章 相關研究 4
第一節、倉庫規劃 4
第二節、倉庫規劃的優化 6
第三節、供應鏈的視覺化設計 8
一、以製造生產資料為主的視覺化 8
二、以倉儲資料為主的視覺化 9
第四節、潛在因果關係與流程關聯的視覺化設計 10
第五節、小結 11
第三章 系統設計 12
第一節、使用者需求 12
第二節、模擬軟體 13
第三節、指標與事件 15
第四節、WISE 18
一、總覽圖 20
二、事件設定介面 21
三、異常與貨物流動分析介面 24
第五節、互動功能 26
一、自定義事件,進行時間資料互動 27
二、指定時段,查看平均值異常比較 28
三、檢視個別實體表現情形 30
第六節、實作方法 31
第七節、使用情境 32
一、單一模型分析 32
二、多模型分析 37
第四章 系統評估 40
第一節、使用者研究 40
一、研究參與者 40
二、資料 42
三、對照系統 43
四、任務介紹 44
五、評估指標 49
六、評估流程 52
七、結果 53
第二節、專家訪談 73
一、參與者 73
二、資料 74
三、操作示範 74
四、訪談問題 74
五、流程 75
六、訪談結果 75
第五章 結論與討論 78
第一節、研究結論 78
第二節、研究限制 80
一、視覺化設計 80
二、系統限制 80
三、情境設計 81
參考文獻 82
附錄 93
附錄一、基本資料問卷 93
附錄二、視覺化測試 94
附錄三、系統操作測試題目 97
附錄四、訪談問題 101
參考文獻 References
尹靜, & 馬常松. (2014). Flexsim 物流系統建模與模擬. 冶金工業.
劉培德, 王睿, & 閔思源. (2021). FlexSim 供應鏈與物流系統建模仿真及應用. 電子工業.
Abideen, A., & Mohamad, F. B. (2021). Improving the performance of a Malaysian pharmaceutical warehouse supply chain by integrating value stream mapping and discrete event simulation. Journal of Modelling in Management, 16(1), 70–102. https://doi.org/10.1108/JM2-07-2019-0159
Andrienko, N., Andrienko, G., Adilova, L., & Wrobel, S. (2022). Visual Analytics for Human-Centered Machine Learning. IEEE Computer Graphics and Applications, 42(1), 123–133. https://doi.org/10.1109/MCG.2021.3130314
Baruffaldi, G., Accorsi, R., & Manzini, R. (2018). Warehouse management system customization and information availability in 3pl companies: A decision-support tool. Industrial Management & Data Systems, 119(2), 251–273. https://doi.org/10.1108/IMDS-01-2018-0033
Bitterling, C., Koreis, J., Loske, D., & Klumpp, M. (2022). Comparing manual and automated production and picking systems. https://doi.org/10.15480/882.4708
Bostock, M., Ogievetsky, V., & Heer, J. (2011). D3 Data-Driven Documents. IEEE Transactions on Visualization and Computer Graphics, 17(12), 2301–2309. https://doi.org/10.1109/TVCG.2011.185
Brooke, J. (1996). SUS: A 「Quick and Dirty」 Usability Scale. 收入 Usability Evaluation In Industry. CRC Press.
Chen, F., Li, J., Wang, F., Liu, S., Wen, X., Li, P., & Zhu, M. (2023). WarehouseLens: Visualizing and exploring turnover events of digital warehouse. Journal of Visualization, 26(4), 977–998. https://doi.org/10.1007/s12650-023-00913-7
Cogo, E., Žunić, E., Beširević, A., Delalić, S., & Hodžić, K. (2020). Position based visualization of real world warehouse data in a smart warehouse management system. 2020 19th International Symposium INFOTEH-JAHORINA (INFOTEH), 1–6. https://doi.org/10.1109/INFOTEH48170.2020.9066323
Cosma, A., Conte, R., Solina, V., & Ambrogio, G. (2024). Design of KPIs for evaluating the environmental impact of warehouse operations: A case study. Procedia Computer Science, 232, 2701–2708. https://doi.org/10.1016/j.procs.2024.02.087
Dias, L. M. S., Vieira, A. A. C., Pereira, G. A. B., & Oliveira, J. A. (2016). Discrete simulation software ranking—A top list of the worldwide most popular and used tools. 2016 Winter Simulation Conference (WSC), 1060–1071. https://doi.org/10.1109/WSC.2016.7822165
Diba, K., Batoulis, K., Weidlich, M., & Weske, M. (2020). Extraction, correlation, and abstraction of event data for process mining. WIREs Data Mining and Knowledge Discovery, 10(3), e1346. https://doi.org/10.1002/widm.1346
Faber, N., de Koster, R. (Marinus) B. M., & van de Velde, S. L. (2002). Linking warehouse complexity to warehouse planning and control structure. International Journal of Physical Distribution & Logistics Management, 32(5), 381–395. https://doi.org/10.1108/09600030210434161
Fragapane, G., de Koster, R., Sgarbossa, F., & Strandhagen, J. O. (2021). Planning and control of autonomous mobile robots for intralogistics: Literature review and research agenda. European Journal of Operational Research, 294(2), 405–426. https://doi.org/10.1016/j.ejor.2021.01.019
Gu, J., Goetschalckx, M., & McGinnis, L. F. (2007a). Research on warehouse operation: A comprehensive review. European Journal of Operational Research, 177(1), 1–21. https://doi.org/10.1016/j.ejor.2006.02.025
Gu, J., Goetschalckx, M., & McGinnis, L. F. (2007b). Research on warehouse operation: A comprehensive review. European Journal of Operational Research, 177(1), 1–21. https://doi.org/10.1016/j.ejor.2006.02.025
Gu, J., Goetschalckx, M., & McGinnis, L. F. (2010). Research on warehouse design and performance evaluation: A comprehensive review. European Journal of Operational Research, 203(3), 539–549. https://doi.org/10.1016/j.ejor.2009.07.031
Huihui, S., Xiaoxia, M., & Xiangguo, M. (2016). Simulation and Optimization of Warehouse Operation Based on Flexsim.
Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and practice. OTexts.
Keim, D. A., Mansmann, F., Schneidewind, J., Thomas, J., & Ziegler, H. (2008). Visual Analytics: Scope and Challenges. 收入 S. J. Simoff, M. H. Böhlen, & A. Mazeika (編輯), Visual Data Mining: Theory, Techniques and Tools for Visual Analytics (頁 76–90). Springer. https://doi.org/10.1007/978-3-540-71080-6_6
Klodawski, M., Jachimowski, R., Jacyna-Golda, I., & Izdebski, M. (2018). Simulation Analysis of Order Picking Efficiency with Congestion Situations. International Journal of Simulation Modelling, 17(3), 431–443. https://doi.org/10.2507/IJSIMM17(3)438
Klug, B. (2017). An Overview of the System Usability Scale in Library Website and System Usability Testing. Weave: Journal of Library User Experience, 1(6). https://doi.org/10.3998/weave.12535642.0001.602
Koreis, J., Loske, D., Klumpp, M., & Glock, C. H. (2025). We belong together—A system-level investigation regarding AGV-assisted order picking performance. International Journal of Production Economics, 282, 109527. https://doi.org/10.1016/j.ijpe.2025.109527
Latsou, C., Ariansyah, D., Salome, L., Ahmet Erkoyuncu, J., Sibson, J., & Dunville, J. (2024). A unified framework for digital twin development in manufacturing. Advanced Engineering Informatics, 62, 102567. https://doi.org/10.1016/j.aei.2024.102567
Lestari, B., Rifiani, P. I., & Gati, A. B. (2021). The Use of the Usability Scale System as an Evaluation of the Kampung Heritage Kajoetangan Guide Ebook Application. European Journal of Business and Management Research, 6(6), Article 6. https://doi.org/10.24018/ejbmr.2021.6.6.1113
Liu, S., Weng, D., Tian, Y., Deng, Z., Xu, H., Zhu, X., Yin, H., Zhan, X., & Wu, Y. (2022). ECoalVis: Visual Analysis of Control Strategies in Coal-fired Power Plants. IEEE Transactions on Visualization and Computer Graphics, 1–11. https://doi.org/10.1109/TVCG.2022.3209430
Liu, Z., Kerr, B., Dontcheva, M., Grover, J., Hoffman, M., & Wilson, A. (2017). CoreFlow: Extracting and Visualizing Branching Patterns from Event Sequences. Computer Graphics Forum, 36(3), 527–538. https://doi.org/10.1111/cgf.13208
Liviu I., Ana-Maria T., & Emil C. (2009, 十二月 1). WAREHOUSE PERFORMANCE MEASUREMENT - A CASE STUDY. | EBSCOhost. https://openurl.ebsco.com/contentitem/gcd:48589556?sid=ebsco:plink:crawler&id=ebsco:gcd:48589556
Lou, C. X., Bonti, A., Prokofieva, M., Abdelrazek, M., & Chowdary Kari, S. M. (2020). Literature Review on Visualization in Supply Chain & Decision Making. 2020 24th International Conference Information Visualisation (IV), 746–750. https://doi.org/10.1109/IV51561.2020.00019
Mahroof, K. (2019). A human-centric perspective exploring the readiness towards smart warehousing: The case of a large retail distribution warehouse. International Journal of Information Management, 45, 176–190. https://doi.org/10.1016/j.ijinfomgt.2018.11.008
Marziali, M., Rossit, D. A., & Toncovich, A. (2021). Warehouse Management Problem and a KPI Approach: A Case Study. Management and Production Engineering Review. https://doi.org/10.24425/mper.2021.138530
McNemar, Q. (1947). Note on the Sampling Error of the Difference Between Correlated Proportions or Percentages. Psychometrika, 12(2), 153–157. https://doi.org/10.1007/BF02295996
Miettinen, K. (2014). Survey of methods to visualize alternatives in multiple criteria decision making problems. OR Spectrum, 36(1), 3–37. https://doi.org/10.1007/s00291-012-0297-0
Oleśków-Szłapka, J., & Stachowiak, A. (2013). The Use of Computer Simulation in Warehouse Automation. 收入 A. Azevedo (編輯), Advances in Sustainable and Competitive Manufacturing Systems (頁 285–293). Springer International Publishing. https://doi.org/10.1007/978-3-319-00557-7_23
Partl, C., Gratzl, S., Streit, M., Wassermann, A. M., Pfister, H., Schmalstieg, D., & Lex, A. (2016). Pathfinder: Visual Analysis of Paths in Graphs. Computer graphics forum : journal of the European Association for Computer Graphics, 35(3), 71–80. https://doi.org/10.1111/cgf.12883
Peng, J. (2010). Identification and Solution to Bottleneck of Supply Chain Based on Flexsim. 2010 International Conference on Intelligent Computation Technology and Automation, 2, 461–465. https://doi.org/10.1109/ICICTA.2010.422
Raineri, M., Perri, S., & Guarino Lo Bianco, C. (2019). Safety and efficiency management in LGV operated warehouses. Robotics and Computer-Integrated Manufacturing, 57, 73–85. https://doi.org/10.1016/j.rcim.2018.11.003
Ramaa, A., Subramanya, K. N., & Rangaswamy, T. M. (2012). Impact of Warehouse Management System in a Supply Chain. International Journal of Computer Applications, 54(1), 14–20. https://doi.org/10.5120/8530-2062
Rizqi, Z. U., Chou, S.-Y., & Khairunisa, A. (2024). Multi-objective simulation-optimization for integrated automated storage and retrieval systems planning considering energy consumption. Computers & Industrial Engineering, 189, 109979. https://doi.org/10.1016/j.cie.2024.109979
Rouwenhorst, B. (1999). Warehouse design and control: Framework and literature review. BETA Research Institute.
Roy, D., Krishnamurthy, A., Heragu, S. S., & Malmborg, C. J. (2016). A simulation framework for studying blocking effects in warehouse systems with autonomous vehicles. European Journal of Industrial Engineering, 10(1), 51–80.
Ruiz, N., Giret, A., Botti, V., & Feria, V. (2014). An intelligent simulation environment for manufacturing systems. Computers & Industrial Engineering, 76, 148–168. https://doi.org/10.1016/j.cie.2014.06.013
Spearman, C. (1961). The Proof and Measurement of Association Between Two Things (頁 58). Appleton-Century-Crofts. https://doi.org/10.1037/11491-005
Sun, D., Huang, R., Chen, Y., Wang, Y., Zeng, J., Yuan, M., Pong, T.-C., & Qu, H. (2019). PlanningVis: A Visual Analytics Approach to Production Planning in Smart Factories. IEEE Transactions on Visualization and Computer Graphics, 1–1. https://doi.org/10.1109/TVCG.2019.2934275
Suschnigg, J., Mutlu, B., Koutroulis, G., Sabol, V., Thalmann, S., & Schreck, T. (2021). Visual Exploration of Anomalies in Cyclic Time Series Data with Matrix and Glyph Representations. Big Data Research, 26, 100251. https://doi.org/10.1016/j.bdr.2021.100251
Tang, J., Zhou, Y., Tang, T., Weng, D., Xie, B., Yu, L., Zhang, H., & Wu, Y. (2022). A Visualization Approach for Monitoring Order Processing in E-Commerce Warehouse. IEEE Transactions on Visualization and Computer Graphics, 28(1), 857–867. https://doi.org/10.1109/TVCG.2021.3114878
Tulli, S. krishna C. (2020). Simulation-based Approaches for Warehouse Layout Optimization in Logistics | International Journal of Scientific Research and Management (IJSRM). https://ijsrm.net/index.php/ijsrm/article/view/2476
van Geest, M., Tekinerdogan, B., & Catal, C. (2022). Smart Warehouses: Rationale, Challenges and Solution Directions. Applied Sciences, 12(1), Article 1. https://doi.org/10.3390/app12010219
Vieira, A. A. C., Dias, L. M. S., Pereira, G. A. B., Oliveira, J. A., Carvalho, M. D. S., & Martins, P. (2018). Simulation model generation for warehouse management: Case study to test different storage strategies. International Journal of Simulation and Process Modelling, 13(4), 324–336. https://doi.org/10.1504/IJSPM.2018.093761
Vis, I. F. A. (2006). Survey of research in the design and control of automated guided vehicle systems. European Journal of Operational Research, 170(3), 677–709. https://doi.org/10.1016/j.ejor.2004.09.020
Wang, J., & Mueller, K. (2023). DOMINO: Visual Causal Reasoning with Time-Dependent Phenomena. IEEE Transactions on Visualization and Computer Graphics, 29(12), 5342–5356. https://doi.org/10.1109/TVCG.2022.3207929
Wang, W., Zhen, H., & Gao, J. (2009). Simulation and optimization on inventory of distribution system using theory of constraints. Jiaotong Yunshu Xitong Gongcheng Yu Xinxi/ Journal of Transportation Systems Engineering and Information Technology, 9, 115–121.
Woolson, R. F. (2005). Wilcoxon Signed-Rank Test. 收入 Encyclopedia of Biostatistics. John Wiley & Sons, Ltd. https://doi.org/10.1002/0470011815.b2a15177
Wu, G., Yao, L., & Yu, S. (2018). Simulation and optimization of production line based on FlexSim. 2018 Chinese Control And Decision Conference (CCDC), 3358–3363. https://doi.org/10.1109/CCDC.2018.8407704
Xie, X., Du, F., & Wu, Y. (2020). A Visual Analytics Approach for Exploratory Causal Analysis: Exploration, Validation, and Applications (No. arXiv:2009.02458). arXiv. https://doi.org/10.48550/arXiv.2009.02458
Xu, P., Mei, H., Ren, L., & Chen, W. (2017). ViDX: Visual Diagnostics of Assembly Line Performance in Smart Factories. IEEE Transactions on Visualization and Computer Graphics, 23(1), 291–300. https://doi.org/10.1109/TVCG.2016.2598664
Yafei, L., Qingming, W., & Peng, G. (2018). Research on simulation and optimization of warehouse logistics based on flexsim-take C company as an example. 2018 7th International Conference on Industrial Technology and Management (ICITM), 288–293. https://doi.org/10.1109/ICITM.2018.8333963
Zhong, Z., Fan, Q., Zhang, J., Ma, M., Zhang, S., Sun, Y., Lin, Q., Zhang, Y., & Pei, D. (2023). A Survey of Time Series Anomaly Detection Methods in the AIOps Domain (No. arXiv:2308.00393). arXiv. https://doi.org/10.48550/arXiv.2308.00393
Živičnjak, M., Rogić, K., & Bajor, I. (2022). Case-study analysis of warehouse process optimization. Transportation Research Procedia, 64, 215–223. https://doi.org/10.1016/j.trpro.2022.09.026
電子全文 Fulltext
本電子全文僅授權使用者為學術研究之目的,進行個人非營利性質之檢索、閱讀、列印。請遵守中華民國著作權法之相關規定,切勿任意重製、散佈、改作、轉貼、播送,以免觸法。
論文使用權限 Thesis access permission:自定論文開放時間 user define
開放時間 Available:
校內 Campus:開放下載的時間 available 2028-08-25
校外 Off-campus:開放下載的時間 available 2028-08-25

您的 IP(校外) 位址是 18.97.14.87
現在時間是 2026-09-15
論文校外開放下載的時間是 2028-08-25

Your IP address is 18.97.14.87
The current date is 2026-09-15
This thesis will be available to you on 2028-08-25.

紙本論文 Printed copies
紙本論文的公開資訊在102學年度以後相對較為完整。如果需要查詢101學年度以前的紙本論文公開資訊,請聯繫圖資處紙本論文服務櫃台。如有不便之處敬請見諒。
開放時間 available 2028-08-25

QR Code