臺灣博碩士論文加值系統

科技的迅速發展使得產品生命週期逐漸縮短。為滿足市場需求之快速變化與強化競爭力，供應鏈必須不斷地提升作業效率。倉儲作業在其中更是關鍵的角色。由於揀貨作業在倉儲活動中最為耗時與費力，管理者無不尋求快速且精確的方法，來提高訂單揀貨效率與降低倉儲營運成本。因此，本研究旨在發展最佳化儲位規劃與合併揀貨策略，以改善揀貨績效。由於儲位規劃是影響揀貨績效的關鍵，故本研究所提出之方法論，結合了儲位規劃與揀貨單分群。首先以顧客訂單預測資料計算零組件需求數量，來預估零組件儲存空間需求，再結合過去大數據資料計算零組件在倉儲平均停留時間，提出最佳化儲位規劃建議。同時，以此儲位規劃為基礎，分別採用階層式分群與K-means分群法，來將相似度高的訂單進行合併。最後，以H公司為案例驗證本論文所提出的方法論。在本研究結果中顯示，優化儲位規劃可以顯著地改善揀貨員檢索零組件在貨架位置的時間。再者，藉由將相似度高的揀貨單合併，可以減少來回揀貨的次數與距離，以達到提升揀貨效率之目標。本研究所提出的改善方法不僅限於電子零組件，此方法之概念亦可應用於其他物料或產品的揀貨作業中。

Rapid technological evolution has led to a decrease in product life cycles, and the efficiency of supply chains must be continually improved in satisfying the rapidly changing market demand and reinforcing company competitiveness. Warehouse management plays a key role in this process. Because order picking is a particularly time-consuming and labor-intensive warehouse operations, managers have sought rapid and accurate methods to improve order picking efficiency and to decrease warehouse operation cost. The goal of this study is to develop an optimized strategy for storage location planning and consolidated picking to improve the order-picking performance. Because storage location planning critically affects order picking, the proposed methodology combines the storage location planning and picking-orders clustering. First, the number of components required is calculated using the customer order prediction data to determine the space required to store these components. Big data are then employed to calculate the average storage time of the components and then to propose the suggestions for the optimized storage locations. Based on the optimized storage location planning, hierarchical clustering and K-means clustering are separately employed to consolidate picking orders with high similarity. Finally, a case study is conducted on Company H to verify the feasibility of the methodology proposed in this study. The results revealed that the optimized storage location planning effectively shortened the picking times for pickers in searching the storage locations of components. Moreover, consolidating the picking orders with high similarity reduced the frequency and travel distances in order picking so as to boost order picking efficiency. The methodology proposed in this study is applicable to not only the order picking for electronic components, but also that for other components or products.

目 錄

中文摘要 II
英文摘要 III
誌謝 III
目錄 V
表目錄 VI
圖目錄 VII
第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的 3
第三節 研究流程 4
第二章 文獻探討 5
第一節 儲位規劃策略 5
第二節 分批揀貨策略 11
第三節 大數據在倉儲管理相關應用 18
第三章 研究方法 21
第一節 問題描述 21
第二節 研究架構 24
第四章 個案研討 37
第一節 儲位規劃分析之個案研討 37
第二節 揀貨單合併分析之個案研討 45
第五章 結論與建議 65
參考文獻 67

參考文獻
簡禛富、許嘉裕（2018）。大數據分析與資料挖礦。新北：前程文化事業股份有限公司。
Arai, K., & Barakbah, A.R. (2007). Hierarchical K-means: An algorithm for centroids initialization for K-means. Reports of the Faculty of Science and Engineering, 36, 25-31.
Aristidis, L., Nikos, V., Jakob J. V. (2003). The global k-means clustering algorithm. Pattern Recognition, 36(2), 451-461.
Bahrami, B., Piri, H., & Aghezza, E. (2019). Class-based Storage Location Assignment: An Overview of the Literature. International Conference on Informatics in Control, Automation and Robotics, 1, 390-397.
Bindi, F., Riccardo, M., Arrigo, P., & Alberto, R. (2008). Similarity-based Storage Allocation Rules in an Order Picking System: An Application to the Food Service Industry. International Journal of Logistics Research and Applications, 12(4), 233-247.
Bradley, P.S.; Fayyad, U. M. (1998). Refining initial points for k-means Clustering. International Conference on Machine Learning, 98, 91-99.
Cergibozan, Ç., & Tasan, A. S. (2019). Order batching operations: an overview of classification, solution techniques, and future research. Journal of Intelligent Manufacturing, 30(1), 355-349.
Chan, H. L., Pang, A., & Li, K. W. (2011). Association Rule Based Approach for Improving Operation Efficiency in a Randomized Warehouse. International Conference on Industrial Engineering and Operations Management. Kuala Lumpur, Malaysia, January 2011: 22-24.
Chen, B., Harrison, R., Pan, Y., & Tai, C. P. (2005). Novel Hybrid Hierarchical-K-means Clustering Method (H-K-means) for Microarray Analysis. IEEE Computational Systems Bioinformatics Conference-Workshops, 105-108.
Chen L., Langevin A., & Riopel, D. (2010). The storage location assignment and interleaving problem in an automated storage/retrieval system with shared storage. International Journal of Production Research, 48(4), 991-1011.
Chen, M., & Wu, H. (2005). An association-based clustering approach to order batching considering customer demand patterns, Omega, 33(4), 333-343.
Chiang, D. M. H., Lin, C. P., & Chen, M. C. (2011). The Adaptive Approach for Storage Assignment by Mining Data of Warehouse Management System for Distribution Centres. Enterprise Information Systems, 5(2), 219-234.
Chiang, M. H. D., Lin, C. P., & Chen, M. C. (2014). Data mining based storage assignment heuristics for travel distance reduction. Expert Systems, 31(1), 81-90.
Chuang, Y. F., Lee, H. T., & Lai, Y. C. (2012). Item-associated Cluster Assignment Model on Storage Allocation Problems. Computers & Industrial Engineering, 63(4), 1171-1177.
Davarzani, H., & Norrman, A. (2015). Toward a relevant agenda for warehousing research: literature review and practitioners’ input. Logistics Research, 8(1).
De Koster, R., Le-Duc, T., & Roodbergen, K. J. (2007). Design and control of warehouse order picking: A literature review. European Journal of Operational Research, 182(2), 481-501.
Elbert, R. M., Franzke, T., Glock, C. H., & Grosse, E. H. (2017). The effects of human behavior on the efficiency of routing policies in order picking: the case of route deviations. Computers & Industrial Engineering, 111, 537-551.
Fabrizio, D., Gino, M., & Marco, M. (2009). Design of order picking system. The International Journal of Advanced Manufacturing Technology, 42, 1-12.
Franzke, T., Grosse, E. H., Glock, C. H., & Elbert, R. (2017). An investigation of the effects of storage assignment and picker routing on the occurrence of picker blocking in manual picker-to-parts warehouses. The International Journal of Logistics Management, 28(3), 841-863.
Frazelle, E. (2002). World-class Warehousing and Material Handling. McGraw-Hill.
Frazelle, E. A., & Sharp, G. P. (1989). Correlated Assignment Strategy Can Improve Any Order-picking Operation. Industrial Engineering, 21(4), 33-37.
Frazelle, E. H. (1990). Stock Location Assignment and Order Batching Productivity. PhD Thesis, Georgia Institute of Technology, Atlanta, 1-105.
Giannikas, V., Lu, W., Robertson, B., & McFarlane, D. (2017). An interventionist strategy for warehouse order picking: Evidence from two case studies. International Journal of Production Economics, Elsevier, 189(C), 63-76.
Goetschalckx, M., & Ashayeri, J. (1989). Classification And Design Of Order Picking. Logistics World, 2(2), 99-106.
Goetschalckx, M., & Ratliff, H. D. (1990). Shared Storage Policies Based on the Duration Stay of Unit Loads. Management Science, 36(9), 1120-1132.
Gong, Y., & de Koster, R. B. M. (2011). A review on stochastic models and analysis of warehouse operations. Logistics Research, 3(4), 191-205.
Gu, J., Goetschalckx, M., & Leon, F. M. (2007). Research on warehouse operation: A comprehensive review. European Journal of Operational Research, 177(1), 1-21.
Guo, X., Yu, Y., & De Koster, R. B. M.. (2015). Impact of required storage space on storage policy performance in a unit-load warehouse. International Journal of Production Research, 54(8), 2405-2418.
Huang, Anna. (2008). Similarity Measures for Text Document Clustering. New Zealand Computer Science Research Student Conference, 49-56.
Jain, A. K., Murty, M. N., & Flynn, P. J. (1999). Data clustering: a review. ACM Computing Surveys, 31(3), 264-323.
Jeroen P. Van den Berg (1999). A literature survey on planning and control of warehousing systems. IIE Transactions, 31(8), 751-762.
Kalkstein, L.S., Tan, G., & Skindlov, J.A. (1987). An evaluation of three clustering procedures for use in synoptic climatological classification. Journal of Climate and Applied Meteorology, 26(6), 717-730.
Kantardzic, M. (2003). Data Mining: Concepts, Models, Methods, and Algorithms. Wiley-Interscience:IEEE Press. Hoboken, New Jersey.
Khan, S.S., & Ahmad, A. (2004). Cluster center initialization algorithm for K-means clustering. Pattern Recognit. Lett, 25, 1293-1302.
Kim, K. H. (1993). A Joint Determination of Storage Locations and Space Requirements for Correlated Items in a Miniload Automated Storage-Retrieval System. International Journal of Production Research, 31(11), 2649-2659.
Kovács, A. (2011). Optimizing the storage assignment in a warehouse served by milkrun logistics. International Journal of Production Economics, 133(1), 312-318.
Krista Rizman Žalik (2008). An efficient k-means clustering algorithm. Pattern Recognition Letters, 29(9), 1385-1391.
Lee, M. K. (1992). A Storage Assignment Policy in a Man-on-board Automated Storage/Retrieval System. International Journal of Production Research, 30(10), 2281-2292.
Li, J., Moghaddam, M., & Nof, S. Y. (2016). Dynamic Storage Assignment with Product Affinity and ABC Classification- A Case Study. International Journal of Advanced Manufacturing Technology, 84(9), 2179-2194.
Li, M. L., Wolf, E., & Wintz, D. (2020). Duration-of-Stay Storage Assignment Under Uncertainty. International Conference on Learning Representations, Addis Ababa, Ethiopia.
Liu, C. M. (1999). Clustering Techniques for Stock Location and Order-picking in a Distribution Center. Computers and Operations Research, 26(10-11), 989-1002.
Liu, Z., Li, T., Li, P., Jia, C., & Li, J., (2017). Verifiable searchable encryption with aggregate keys for data sharing system. Future Generation Computer Systems, 78(2), 778-788.
Masae, M., Glock, C. H., & Grosse, E. H. (2019). Order picker routing in warehouses: A systematic literature review. International Journal of Production Economics, 224(C).
Montulet, P., Langevin, A., & Riopel, D. (1997). Le proble`me de l’optimisation de l’entreposage partage´: me´thodes exacte et heuristique. Information Systems and Operational Research, 35(2), 138-153.
Moseley, B., & Wang, J. (2017) Approximation Bounds for Hierarchical Clustering: Average Linkage, Bisecting K-means, and Local Search. Conference on Neural Information Processing Systems. Long Beach, CA, USA.
Muppani, V. R., & Adil, G. K. (2008). Class-based storage-location assignment to minimize pick travel distance. International Journal of Logistics Research and Applications, 11(4), 247-265.
Muppani, V. R., & Adil, G. K. (2008). Efficient formation of storage classes for warehouse storage location assignment: A simulated annealing approach. Omega, 36(4), 609-618.
Ömer Öztürkoğlu & Deniz Hoser. (2019). A discrete cross aisle design model for order-picking warehouses. European Journal of Operational Research, 275(2), 411-430.
Pang, K. W., & Chan, H. L. (2016). Data mining-based algorithm for storage location assignment in a randomised warehouse. International Journal of Production Research, 55(14), 1-18.
Penã, J. M., Lozano, J.A., & Larrañaga, P. (1999). An empirical comparison of four initialization methods for the K-means algorithm. Pattern Recognition Lett, 20, 1027-1040.
Petersen, C. G., & Aase, G. R. (2004). A comparison of picking, storage, and routing policies in manual order picking. International Journal of Production Economics, 92(1), 11-19.
Pferschy, U., & Schauer, J. (2018). Order batching and routing in a non-standard warehouse. Electronic Notes in Discrete Mathematics, 69, 125-132.
Rao, S. S., & Adil, G. K. (2013). Optimal class boundaries, number of aisles, and pick list size for low-level order picking systems. IIE Transactions, 45(12), 1309-1321.
Roodbergen, K. J., & De Koster, R. (2001). Routing order pickers in a warehouse with a middle aisle. European Journal of Operational Research, 133(1), 32-43.
Roodbergen, K. J., & Vis, I. F.A. (2009). A survey of literature on automated storage and retrieval systems. European Journal of Operational Research, 194(2), 343-362.
Shantakumar, B. P. & Kumaraswamy, Y. S. (2009). Extraction of Significant Patterns from Heart Disease Warehouses for Heart Attack Prediction. International Journal of Computer Science and Network Security, 9(2), 228-235.
Sharp, G. P., II-Choe, K., & Yoon, C. S. (1991). Small Parts Order Picking: Analysis Framework and Selected Results. Material Handling and Logistics, Material Handling’90, 317-341.
Shehroz, S. K., & Ahmad, A. (2004). Cluster center initialization algorithm for K-means clustering. Pattern Recognition Letters , 25(11), 1293-1302.
Tompkins, J. A., White, J. A., Bozer, Y. A., & Tanchoco, J. M. A. (2010). Facilities planning. John Wiley & Sons.
Van Gils, T., Ramaekers, K., Caris, A., & de Koster, R. B. M.(2018). Designing Efficient Order Picking Systems by Combining Planning Problems: State-of-the-art Classification and Review. European Journal of Operational Research, 267(1), 1-15.
Vincent C. A., Varun, K., Frederik M. T., & Claire, M. (2019). Hierarchical Clustering. Journal of the ACM, 66(4), 1-42.
Xiao, J., & Zheng, L. (2009). A Correlated Storage Location Assignment Problem in a Singleblock-multi-aisles Warehouse Considering BOM Information. International Journal of Production Research, 48(5), 1321-1338.
Xu, R., & WunschII, D. (2005). Survey of Clustering Algorithms. IEEE Transactions on Neural Networks, 16(3), 645-678.
Yener, F., & Yazgan, H. R. Optimal warehouse design: literature review and case study application. Computers & Industrial Engineering, 129, 1-13.
Yu, H., Wen, G., Gan, J., Zheng, W., & Lei, C. (2018). Self-paced learning for k-means clustering algorithm. Pattern Recognition Letters, 132, 69-75
Yu, Yugang, de Koster, Rene ́ B.M. & Guo, Xiaolong. (2015). Class-based storage with a finite number of items: Using more classes is not always better. Production and operations management, 24(8), 1235-1247.
Zhang, G., Nishi, T., Turner, S., Oga, K., & Li, X. (2017). An integrated strategy for a production planning and warehouse layout problem: modeling and solution approaches. Omega, 68(C), 85-94.
Zhang, R. Q., Wang, M., & Pan, X. (2019). New model of the storage location assignment problem considering demand correlation pattern. Computers & Industrial Engineering, 129, 210-219.
Zhou, L., Li, Z., Shi, N., Liu, S., & Xiong, K. (2019). Performance analysis of three intelligent algorithms on route selection of fishbone layout. Sustainability, 11(4), 1148-1165.

Zhou, L., Fan, X., Wang, J., Wang, S., Cao, N., & Wu, M. (2020). A Random Classified-Storage Picking Path Model for V-Type Storage Layout. Comp