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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 黃奎隆 | |
dc.contributor.author | Huang-Yu Chen | en |
dc.contributor.author | 陳皇宇 | zh_TW |
dc.date.accessioned | 2021-06-17T06:03:15Z | - |
dc.date.available | 2024-01-30 | |
dc.date.copyright | 2019-01-30 | |
dc.date.issued | 2019 | |
dc.date.submitted | 2019-01-28 | |
dc.identifier.citation | [1] 江堉旻,「應用灰色預測法與時間序列法於連鎖藥局需求預測系統之研究」,國立臺灣大學工業工程學研究所碩士論文,2015。
[2] 孫智麗,「醫藥生技產業分析與市場趨勢」,2016。 [3] 陳彥臻,「考慮病患行為應用於連鎖藥局需求預測模型之研究」,國立臺灣大學工業工程學研究所碩士論文,2017。 [4] 張哲倫,「專利連結之歷史、緣由及其政策功能」,智慧財產月刊,2015,196期:5-19。 [5] 虞成全,「2018年生技醫療產業展望」,2018。 [6] 鄧哲明,「新藥的研發流程概論」,科學月刊,2013,44(2):188-206。 [7] Abonyi, J., Feil, B. (2007). Cluster Analysis for Data Mining and System Identification. [8] Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, AC-19(6), 716-723. [9] Amaldoss, W., He, C. (2009). Direct-to-consumer advertising of prescription drugs: A strategic analysis. Marketing Science, 28(3), 472-487. [10] Anusha, S. L., Alok, S., Shaik, A. (2014). Demand forecasting for the Indian pharmaceutical retail: A case study. Journal of Supply Chain Management Systems, 3(2), 1-8. [11] Arsham, H., Shao, Jr. S. P. (1985). Seasonal and cyclical forecasting for the small firm. American Journal of Small Business, 9(4), 46-57. [12] Ashouri, M., Cai, K., Lin, F., Shmueli, G. (2018). Assessing the value of an information system for developing predictive analytics: The case of forecasting school-level demand in Taiwan. Service Science, 10(1), 58-75. [13] Bala, R., Bhardwaj, P. (2010). Detailing vs. direct-to-consumer advertising in the prescription pharmaceutical industry. Management Science, 56(1), 148-160. [14] Box, G. E. P., Jenkins, G. M. (1976). Time Series Analysis: Forecasting and Control, 2nd ed. San Francisco: Holden Day. [15] Breiman, L., Friedman, J. H., Olshen, R. A., Stone, C. J. (1984). Classification and Regression Trees. Monterey, CA: Wadsworth & Brooks/Cole Advanced Books & Software. [16] Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32. [17] Chang, N., Liu Sheng, O. R. (2008). Decision-tree-based knowledge discovery: Single- vs. multi-decision-tree induction. INFORMS Journal on Computing, 20(1), 46-54. [18] Chu, C. P., Shie, S. Y. (2014). The study of forecasting and stock management for different categories of medicines consumed. Journal of Healthcare Management, 15(1), 55-72. [19] Dia, Q. Long-term load forecast using decision tree method. 2006 IEEE PES Power Systems Conference and Exposition. [20] Eick, C. F., Zeidat, N., Zhao, Z. (2004). Supervised clustering - algorithms and benefits. 16th IEEE International Conference on Tools with Artificial Intelligence. [21] Ferreira, K. J., Lee, B. H. A., Simchi-Levi, D. (2016). Analytics for an online retailer: Demand forecasting and price optimization. Manufacturing & Service Operations Management, 18(1), 69-88. [22] Fu, Z., Golden, B. L., Lele, S., Raghavan, S., Wasil, E.A. (2003). A genetic algorithm-based approach for building accurate decision trees. INFORMS Journal on Computing, 15(1), 3-22. [23] Holland, J. H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, MI. [24] Holt, C. E. (1957). Forecasting seasonals and trends by exponentially weighted averages (O.N.R. Memorandum No. 52). Carnegie Institute of Technology, Pittsburgh USA. [25] Hyndman, R. J., Athanasopoulos, G. (2013). Forecasting: Principles and Practice, accessed October 14, 2014, available at http://otexts.org/fpp/. [26] Hyndman, R. J. Khandakar, Y. (2008). Automatic time series forecasting: The forecast package for R. Journal of Statistical Software, 27(3). [27] Hyndman, R. J., Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22, 679-688. [28] Johnson, R. A., Wichern, D. W. (2007). Applied Multivariate Statistical Analysis, sixth edition. [29] Kappe, E., Stremersch, S. (2016). Drug detailing and doctors’ prescription decisions: The role of information content in the dace of competitive entry. Marketing Science, 28(6), 915-933. [30] Kelle, P., Woosley, J., Schneider, H. (2012). Pharmaceutical supply chain specifics and inventory solutions for a hospital case. Operations Research for Health Care, 1, 54-63. [31] Kwiatkowski, D., Phillips, P.C.B., Schmidt, P., Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root. Journal of Econometrics, 54, 159-178. [32] Lai, R. K., Fan, C. Y., Huang, W. H., Chang, P. C. (2009). Evolving and clustering fuzzy decision tree for financial time series data forecasting. Expert Systems with Applications, 36, 3761-3773. [33] Landram, F. G., Abdullat, A., Shah, V. (2004).Using seasonal and cyclical components in least squares forecasting models. Southwestern Economic Review, 31, 189-196. [34] Lee, J. M., Chang, Y. F. (2003). An interactive decision support using multiple models: The hospital drugs management for inventory level. MIS Review, 12, 139-158. [35] Osborn, D. R., Chui, A. P. L., Smith, J. P., Birchenhall, C. R. (1988). Seasonality and the order of integration for consumption. Oxford Bulletin of Economics and Statistics, 50(4), 361-377. [36] Qia, D. (2006). Long-term load forecast using decision tree method. 2006 IEEE PES Power Systems Conference and Exposition. [37] Quinlan, J. R. (1992). Learning with continuous classes. Proceedings Fifth Australian Joint Conference Artificial Intelligence, 343-348. [38] Quinlan, J. R. (1993). C4.5: programs for machine learning. Morgan Kaufmann Publishers Inc., San Francisco, CA. [39] Regan, T. L. (2008). Generic entry, price competition, and market segmentation in the prescription drug market. International Journal of Industrial Organization, 26, 930-948. [40] Scott Morton, F. M. (2000). Barriers to entry, brand advertising, and generic entry in the US pharmaceutical industry. International Journal of Industrial Organization, 18, 1085-1104. [41] Smith, J., Yadav, S. (1994). Forecasting costs incurred from unit differencing fractionally integrated process. International Journal of Forecasting, 10(4), 507-514. [42] Strehl A., Ghosh, J. (2003). Relationship-based clustering and visualization for high-dimensional data mining. INFORMS Journal on Computing, 15(2), 208-230. [43] Thomas, D. W., Burns, J., Audette, J., Carroll, A., Dow-Hygelund, C., Hay, M. (2016). Clinical Development Success Rates 2006-2015. BIO Industry Analysis. [44] Vapnik, V., Lerner A. (1963). Pattern recognition using generalized portrait method. Automation and Remote Control, 24, 774-780. [45] Vigo, D., Caremi, C., Gordini, A., Bosso, S., D’Aleo, G., Beleggia, B. (2014). SPRINT: Optimization of staff management for desk customer relations services at Hera. Interfaces, 44(5), 461-479. [46] Wagstaff, K., Cardie, C., Rogers, S., Schroedl, S. (2001). Constrained K-means clustering with background knowledge. Eighteenth International Conference on Machine Learning, 577-584. [47] Wang, X., Smith, K. A., Hyndman, R. J. (2006). Characteristic-based clustering for time series data. Data Mining and Knowledge Discovery, 13(3), 335-364. [48] Winters, P. R. (1960). Forecasting sales by exponentially weighted moving averages. Management Science, 6, 324–342. [49] Witten, I. H., Frank, E., Hall, M. A. (2005). Data Mining, third edition. [50] Xiao, H., Xiao, Z., Wang, Y. (2016). Ensemble classification based on supervised clustering for credits coring. Applied Soft Computing, 43, 73-86. [51] Yu, Z., Haghighat, F., C. M. Fung, B., Yoshino, H. (2010). A decision tree method for building energy demand modeling. Energy and Buildings, 42(10), 1637-1646. [52] Zadeh, N. K., Sepehri, M. M., Farvaresh, H. (2014). Intelligent sales prediction for pharmaceutical distribution companies: A data mining based approach. Mathematical Problems in Engineering, 2014, 15 pages. [53] Zhang, G. P. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159-175. [54] Zhang, G. P., Qi, M. (2005). Neural network forecasting for seasonal and trend time series. European Journal of Operational Research, 160, 501-514. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/71559 | - |
dc.description.abstract | 近年來隨著政府醫藥分業的政策,連鎖藥局和社區藥局的數量逐年增加,藥局已成為日常民眾最主要的拿藥途徑。在藥局經營競爭壓力日趨增加下,對於藥局經營者來說為了提高利潤與降低庫存成本,如何準確地預測需求並達成穩定的存貨水平是一個關鍵課題。然而其中在一些特殊的情況下,要精確地預測藥品需求將會非常困難,例如專利藥在專利權到期後的需求預測。要處理此類問題的困難點在於專利權到期對於專利藥而言是一個重大事件的發生,專利權到期意味著將會有許多學名藥進入同樣的市場與之競爭,因此專利藥品需求歷史資料的參考價值便大幅降低,代表我們必須被迫在有限的資料下進行預測。針對此種特殊情況下,本研究首先採用集群分析對於資料集進行初步的探索,試圖把專利藥品依據需求、價格等特徵先做分類並加上標籤,以作為之後分析的因子;同時採用時間序列的方法試圖描述專利藥在專利權過期前的需求趨勢;最後利用決策樹中的分類與迴歸樹演算法預測專利權到期後專利藥的需求變化情形。 | zh_TW |
dc.description.provenance | Made available in DSpace on 2021-06-17T06:03:15Z (GMT). No. of bitstreams: 1 ntu-108-R05546051-1.pdf: 2381666 bytes, checksum: 66e03a7d7a6281988acd91ba54d21068 (MD5) Previous issue date: 2019 | en |
dc.description.tableofcontents | 摘要 I
ABSTRACT II 目錄 III 圖目錄 V 表目錄 VI 第一章 緒論 1 1.1 研究背景 1 1.2 研究動機 3 1.3 研究目的 4 1.4 研究架構 5 第二章 文獻探討 6 2.1 製藥產業特色 6 2.2 集群分析 7 2.3 時間序列 9 2.4 決策樹 12 第三章 研究方法與預測系統 14 3.1 需求預測系統主要架構 14 3.2 藥品分群 16 3.2.1 階層式分群法 17 3.2.2 切割式分群法 19 3.3 藥品需求的時間序列模型 20 3.3.1 自我迴歸整合移動平均模型 21 3.3.2 指數平滑法 24 3.4 決策樹需求預測模型 25 第四章 實例驗證與結果分析 29 4.1 資料描述 29 4.2 資料集分析 30 4.3 模型建置及參數設定 34 4.3.1 集群分析 34 4.3.2 時間序列法 40 4.3.3 決策樹需求預測模型 49 4.4 模型預測結果比較 53 第五章 結論 58 5.1 結論與建議 58 5.2 未來研究方向 59 參考文獻 60 | |
dc.language.iso | zh-TW | |
dc.title | 專利權到期後專利藥之需求預測 | zh_TW |
dc.title | Demand Forecasting for the Patent Expiration Drug | en |
dc.type | Thesis | |
dc.date.schoolyear | 107-1 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 余峻瑜,藍俊宏,范治民 | |
dc.subject.keyword | 需求預測,集群分析,時間序列,決策樹, | zh_TW |
dc.subject.keyword | demand forecasting,cluster analysis,time series method,decision tree, | en |
dc.relation.page | 64 | |
dc.identifier.doi | 10.6342/NTU201900003 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2019-01-28 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 工業工程學研究所 | zh_TW |
顯示於系所單位: | 工業工程學研究所 |
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