臺灣博碩士論文加值系統

在市場競爭日趨激烈的環境下，企業過去提供大量標準化的產品已無法滿足顧客的需求。為了滿足顧客的需求及降低成本的支出，「大量客製化」的生產策略應運而生，其中接單後裝配(assemble-to-order, ATO)為著名的生產策略。ATO結合了推式與拉式生產的優點，有效減少了庫存的存量並讓製程能彈性的隨顧客的需求而變化，但產品加工一旦到了關鍵的裝配環節，企業往往面臨各種難以避免的問題，譬如：客戶需求時間變化、客戶的多樣性需求（對產品設計的變化）、裝配計劃的可執行性較弱（以經驗調度組織生產）、物料的配送模式粗放等（缺少物料的配送計劃）。基於以上所提出的困難，本研究針對裝配的執行提出創新的方法，稱為預組裝推薦系統(Pre-assembly Recommendation System, PRS)，試圖在既有的產品結構中，找出適合且可預先組裝的預組件。PRS是以Single Level tree(SLT，單階樹)結構為基礎，將產品拆解成許多SLT的結構，再應用封閉高頻項目探勘出候選預組件，然後透過組裝限制得到可組裝的預組件，再將預組件組合成推薦專案，以及計算各專案在經濟生產批量下的存貨成本，最後推薦成本最低的預組裝專案。本研究以模擬方式，探討企業使用預組裝推薦系統在經濟生產批量(EPQ)下的存貨成本。實驗結果得知預先大量生產預組件，能幫助企業節省存貨成本達50%以上。此外，透過風險共擔的觀點，預先裝配預組件能簡化整體組裝的複雜度，並且實現延遲產品差異化，進而縮短出貨時間。在一系列的實驗分析後，證實PRS推薦的專案能為企業帶來許多的益處。

Under the circumstance of intense market competition, the large amount of standardized products cannot satisfy diversified customer needs. In order to fulfil customer’s needs, the strategy of “Mass Customization” had been proposed. ATO (assemble-to-order), one of the well-known mass customization strategies, combines the advantage of push and pull supply chain and decreases the amount of stocking. However, many problems have revealed that company have been when the production processing comes to the assemble sessions. To solve the previous difficulties, this research provided a creative method, called PRS (Pre-assembly Recommendation System), for assembling process. The aims of this study want to find out components that could be pre-assembled from existing product structures to simplify the assembling complexity of, and to delay the product differentiation. In this study, PRS decomposes products into many SLT (Single Level tree) structures, and produce the possible pre-assemble components by applying closed frequent itemsets mining approach. Then, unsuitable pre-assemble components are deleted by a set of predefined assemble limitations. Lastly, the most suitable project will be suggested by cost analysis. In this study, simulation approach is used to evaluate the inventory cost of economic production quantity (EPQ) when the proposed pre-assembled recommendation system is adopted. The experiment result indicates that inventory cost can be reduced up to 50%. In addition, the pre-assembled components simplify the complexity of the whole assembly, delay product differentiation, and reduce the delivery time. After a series of experiments analysis, PRS recommended project shows that it can bring many benefits for companies.

ABSTRACT i
摘 要 iii
致 謝 v
目錄 vii
圖目錄 x
表目錄 xii
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究問題 3
1.3 研究目的 4
1.4 論文架構 4
第二章 文獻探討 6
2.1 關聯規則在生產管理的應用 6
2.1.1封閉高頻項目集演算法 9
2.2 接單後裝配(assembly-to-order, ATO) 11
2.2.1延遲差異化 12
第三章 研究方法 14
3.1 研究假設與符號定義 14
3.2 研究流程 15
3.3 產品結構定義 16
3.4 Single Level Tree(SLT)及其產生程序 17
3.5 候選預組件產生程序 21
3.6 候選預組件的組裝限制 32
3.7 預組裝專案產生 38
3.8 預組裝專案效益分析 40
第四章 實驗分析與結果 54
4.1 環境假設與PRS的執行 55
4.1.1 建立SLT 55
4.1.2 SLT相似度計算 58
4.1.3 EPQ相關成本設定 59
4.1.4 PRS執行 60
4.2 參數探討 76
4.2.1 資料處理參數 76
4.2.2 單位持有成本(H)和單位建置成本(S)的探討 80
4.2.3 生產率(p)和使用率(u)成本探討 82
4.2.4 日需求量( )探討 86
第五章 結論與未來研究 89
5.1 結論 89
5.2 未來展望 90
文獻 91
英文文獻 91
中文文獻 95
附錄A-零(組)件結構關係 96
附錄B-產品1~20的產品結構 97
附錄C-零(組)件連接關聯矩陣(CSLTv) 102
附錄D- SLT相似表( =20%) 109
附錄E-單位持有成本(H)與單位建置成本(S)不同比例下的存貨成本 113
附錄F-生產率(p)&使用率(u)不同比例下的存貨成本 117
附錄G-日需求量( )集中時的專案成本 121

英文文獻
1. Agrawal, R., Imielinski, T., Swami. A., “Mining association rules between sets of items in large database.” Proceedings of the 1993 ACM SIGMOD Conference, pp. 207–216, 1993.
2. Agrawal, R., Srikant, R., “Fast algorithms for mining association rules in large database.” Proceedings of the 20th International Conference on Very Large Data Bases, pp. 487-499, 1994.
3. Altuntas, S., & Selim, H., “Facility layout using weighted association rule-based data mining algorithms: Evaluation with simulation.” International Journal of Expert Systems with Applications, 39(1), pp. 3-13, 2012.
4. Bucklin, L.-P., “Postponement speculation and the structure of distribution channels.” International Journal of Marketing Research, 2(1), pp. 26-31, 1965.
5. Bowersox, D.-J., & Closs, D.-J., “Logistical Management: The integrated supply chain process.” McGraw-Hill Series in Marketing, 1996.
6. Brin, S., Motwani, R., & Silverstein, C., “Beyond market basket: generalizing association rules to correlations.” Proceedings of the ACM SIGMOD Conference, pp. 265–276, 1997a.
7. Brin, S., Motwani, R., Ullman, J.-D., & Tsur, S., “Dynamic itemset counting and implication rules for market basket data.” Proceedings of the ACM SIGMOD Conference, pp. 255–264, 1997b.
8. Chen, M.-S., Han, J., & Yu, P.-S., “Data mining: an overview from a database perspective.” International Journal of Knowledge and Data Engineering, IEEE Transactions, 8(6), pp. 866-883, 1996.
9. Chen, W.-C., Tseng, S.-S., & Wang, C.-Y., “A novel manufacturing defect detection method using association rule mining techniques.” International Journal of Expert Systems with Applications, 29(4), pp.807-815, 2005.
10. Chen, M.-C., & Lin C.-P., “A data mining approach to product assortment and shelf space allocation.” International Journal of Management of Expert Systems with Applications, 32(4), pp. 976-986, 2007.
11. Cil, I., “Consumption universes based supermarket layout through association rule mining and multidimensional scaling.” International Journal of Expert Systems with Applications, 39(10), pp. 8611-8625, 2012.
12. Kamsu-Foguem, B., Rigal, F., & Mauget, F., “Mining association rules for the quality improvement of the production process.” International Journal of Expert Systems with Applications, 40(4), pp. 1034-1045, 2013.
13. Lee, H.-L., Billington, Corey, “Designing Products and Processes for Postponement.” International Journal of Management of Design, Springer Netherlands, pp. 105-122, 1994.
14. Lee, H.-L., & Tang, C.-S., “Modeling the cost and benefits of delayed product differentiation.” Management Science, 43(1), pp. 40-53, 1997.
15. Lee, C., Song, B., & Park, Y., “Design of convergent product concepts based on functionality: An association rule mining and decision tree approach.” International Journal of Expert Systems with Applications, 39(10), pp. 9534-9542, 2012.
16. Le, H.-Q., Arch-int, S., Nguyen, H.-X., & Arch-int, N., “Association rule hiding in risk management for retail supply chain collaboration.” International Journal of Computers in Industry, 64(7), pp.776-784, 2013.
17. Nafari, M., & Shahrabi, J., “A temporal data mining approach for shelf-space allocation with consideration of product price.” International Journal of Expert Systems with Applications, 37(6), pp. 4066-4072, 2010.
18. Park, J.-S., Chen, M.-S., & Yu, P.-S., “An effective hash-based algorithm for mining association rules.” Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 175–186, 1995a.
19. Park, J.-S., Chen, M.-S., & Yu, P.-S., “Efficient parallel data mining for association rules.” Proceedings of the Fourth International Conference on Information and Knowledge Management, pp. 31–36, 1995b.
20. Park, J.-S., Chen, M.-S., & Yu, P.-S., “Mining association rules with adjustable accuracy.” Proceedings of the Sixth International Conference on Information and Knowledge Management, pp. 151-160, 1997.
21. Pagh, J.-D., & Cooper, M.-C. “Supply chain postponement and speculation structures: how to choose the right structure?” International Journal of Business Logistics, 19(2), pp. 13-34, 1998.
22. Pasquier, N., Bastide, Y., Taouil, R. & Lakhal, L., “Discovering frequent closed itemsets for association rules.” Proceedings of the 7th International Conference on Database Theory, Springer Berlin Heidelberg, pp. 398-416, 1999.
23. Pei, J., Han, J. & Mao, R., “Closet: An efficient algorithm for mining frequent closed itemsets.” Proceedings of the SIGMOD Int’l Workshop on Data Mining and Knowledge Discovery, 2000.
24. Su, J.-C.-P., Chang, Y.-L., & Ferguson, M., “Evaluation of postponement structures to accommodate mass customization,” International Journal of Operations Management, 23(3-4), pp. 305-318, 2005.
25. Su, J.-C.-P., Chang, Y.-L., & Ferguson, M., “Evaluation of postponement structures to accommodate mass customization.” International Journal of Operations Management, 23(3-4), pp. 305-318, 2005.
26. Stevenson, W.J., “Operations management, 9e” Publish from McGraw-Hill, 2007.
27. Simchi-Levi, D., Kaminsky, P., & Simchi-Levi, E., “Designing and managing the supply chain” Publish of McGraw-Hill, 2008.
28. Tan, P.-N., Steinbach, M. & Kumar, V., “Introduction to data mining.” Publish from Pearson Addison Wesley, 2006.
29. Tsai, C.-Y., Chen, C.-J., Lo, Y.-T., “A cost-based module mining method for the assemble-to-order” International Journal of Intelligent Manufacturing, 2013.
30. Van Hoek, R.-I., Vos, B., & Commandeur, H.-R., “Restructuring European supply chains by implementing postponement strategies.” International Journal of Strategic Management in Long Range Planning, 32(5), pp. 505-518, 1999.
31. Wur, S.-Y., & Leu, Y. “An effective Boolean algorithm for mining association rules in large databases.” Proceedings of the 6th International Conference on Database Systems for Advanced Applications, pp. 179-186, 1999.
32. Wang J., & Pei, J., “CLOSET+: Searching for the Best Strategies for Mining Frequent Closed Itemsets.” Proceedings of the 12th ACM International Conference on Information and Knowledge Management, 2003.
33. Wu, C., “Applying frequent itemset mining to identify a small itemset that satisfies a large percentage of orders in a warehouse.” International Journal of Computer and Operations Research, 33(11), pp. 3161-3170, 2006.
34. Zinn, W., & Bowersox, D.-J., “Planning physical distribution with the principle of postponement.” Journal of Business Logistics, 9(2), pp. 117-136, 1988
35. Zaki, M.-J., & Hsiao, C.-j., “CHARM: An efficient algorithm for closed itemset mining.” Proceedings of the SDM’02, 2002.

中文文獻
1. 李友錚, 「作業管理 Operations Management」, 前程文化, 2011。
2. 黃欽足, 「序列聯結產品整合式組裝規劃系統之研發」, 博士論文, 國立成功大學, 2004。
3. 許本諧, 「CTO模式在半導體設備產業應用」, 碩士論文, 國立清華大學, 2009。
4. 蘇雄義, 「物流與運籌管理」, 華泰文化, 2000。