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題名 銅採購決策支援系統
A copper procurement decision support system作者 商肯豪
Shang, Ken-Hao貢獻者 蔡瑞煌<br>林怡伶
Tsaih, Rua-Huan<br>Lin, Yi-Ling
商肯豪
Shang, Ken-Hao關鍵詞 銅採購議題
混合學習型決策支援系統
單隱藏層前饋神經網路
趨勢預測
copper procurement issue
decision support system
single-hidden layer feedforward neural network
trend forecasting日期 2022 上傳時間 1-Aug-2022 17:22:36 (UTC+8) 摘要 有色金屬對於經濟發展的影響隨著社會的進步日趨變得重要，其中銅有著多元的特性更是有色金屬中十分重要的角色，許多相關於銅的研究也應運而生。其中關於銅採購的研究也是十分稀少，儘管原料採購議題對於製造業是如此的重要，至今也尚未有完善的決策支援系統可以幫助企業做銅採購上的建議。因此本研究旨在提出一個整合性銅採購決策支援系統。本決策支援系統將許多會影響採購的因素納入到系統內，包含未來銅價、未來需求、未來碎銅價、碎銅庫存量、庫存極限存放量等等……而在系統建構方式的選上考量到本系統是以「預測未來趨勢」為導向的決策支援系統，因此在模型I到模型III主要採用learning-based為模型的建構基礎，並在最後模型IV結合換銅成本轉換公式和DINKLE公司的採購規則來給予銅採購決策支援。在模型演算法的選擇上，考量到ANN、改良ANN在許多具有時間序列、多為度特性的資料集上有著優秀的表現，本研究採取了改良ANN演算法中的單隱藏層前饋神經網路(SLFN)作為前三個模型的主體，並添加了一些機制來改善原生ANN所具有的缺點，提出SOTRS機制。資料來源方面，銅價及碎銅價資料取自了銅道網及長江有色網，銅需求及庫存量則使用了DINKLE公司2015年至2020年共六年的歷史資料。SOTRS機制的驗證會以模型III碎銅價的資料集作為驗證，並和市面上常見的演算法做比較(包含 ANN、SVR 和 DST)。CPDSS的驗證會根據DINKLE公司專家對於2015 – 2020年採購的理想決策情形做為檢測標竿，並使用上述4種方法(包含 ANN, SVR, DST, SOTRS)的預測結果根據模型IV的推理結果來給予決策建議，以此來檢驗CPDSS的成效。
The impact of non-ferrous metals on economic development has become increasingly important with the progress of society. Among them, copper with multiple characteristics plays a very important role in non-ferrous metals. Furthermore, the research on copper procurement is very rare although the issue of raw material procurement is so important to the manufacturing industry, there is still no a related decision support system to help enterprises make copper procurement suggestions. Therefore, the purpose of this study is to put forward an integrated copper procurement decision support system.This proposed CPDSS incorporates many factors that will affect copper procurement including future copper and shredded copper price, future demand, and inventory information. Regarding the choice of system construction method, consider CPDSS is a DSS oriented to "predicting future trends". Therefore, in model I, model II, and model III, learning-based is mainly used as the basis for the construction of the model, and finally model IV incorporates the procurement rules recommended by DINKLE company to summarize the output of the first three models and then outputs decision support. Regarding the choice of model algorithm, consider that ANN and improved ANN have great performances on many data sets with time series and highdimensional characteristics, this research adopts the single-hidden layer feedforward neural network (SLFN) which is one of the improved ANN as the main body of the first three models, and adds some additional modules to improve the shortcomings of ANN, the SOTRS mechanism is proposed. In terms of data sources, copper price and shredded copper price data are collected from ccmn.cn and https://www.tongdow.com/, and the copper demand and inventory are offered by the historical data of DINKLE company from 2015 to 2020. This study first verifies the SOTRS mechanism through the dataset of model III, and compares with the common algorithms in the researches including ANN, SVR, and DST. Then using the ideal 2015-2020 copper procurement decision offered by DINKLE company as the benchmark to measure the decision recommendation from the above four methods (CPDSS, ANN, SVR, and DST) based on the results of the previous experiment.參考文獻 Aamodt, A., and Plaza, E. 1994. "Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches," AI communications (7:1), pp. 39-59.Angehrn, A. A., and Lüthi, H.-J. 1990. "Intelligent Decision Support Systems: A Visual Interactive Approach," Interfaces (20:6), pp. 17-28.Astudillo, G., Carrasco, R., Fernández-Campusano, C., and Chacón, M. 2020. "Copper Price Prediction Using Support Vector Regression Technique," Applied Sciences (10:19), p. 6648.Au, K., Wong, W. K., and Zeng, X. 2006. 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國立政治大學
資訊管理學系
109356023資料來源 http://thesis.lib.nccu.edu.tw/record/#G0109356023 資料類型 thesis dc.contributor.advisor 蔡瑞煌<br>林怡伶 zh_TW dc.contributor.advisor Tsaih, Rua-Huan<br>Lin, Yi-Ling en_US dc.contributor.author (Authors) 商肯豪 zh_TW dc.contributor.author (Authors) Shang, Ken-Hao en_US dc.creator (作者) 商肯豪 zh_TW dc.creator (作者) Shang, Ken-Hao en_US dc.date (日期) 2022 en_US dc.date.accessioned 1-Aug-2022 17:22:36 (UTC+8) - dc.date.available 1-Aug-2022 17:22:36 (UTC+8) - dc.date.issued (上傳時間) 1-Aug-2022 17:22:36 (UTC+8) - dc.identifier (Other Identifiers) G0109356023 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/141037 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊管理學系 zh_TW dc.description (描述) 109356023 zh_TW dc.description.abstract (摘要) 有色金屬對於經濟發展的影響隨著社會的進步日趨變得重要，其中銅有著多元的特性更是有色金屬中十分重要的角色，許多相關於銅的研究也應運而生。其中關於銅採購的研究也是十分稀少，儘管原料採購議題對於製造業是如此的重要，至今也尚未有完善的決策支援系統可以幫助企業做銅採購上的建議。因此本研究旨在提出一個整合性銅採購決策支援系統。本決策支援系統將許多會影響採購的因素納入到系統內，包含未來銅價、未來需求、未來碎銅價、碎銅庫存量、庫存極限存放量等等……而在系統建構方式的選上考量到本系統是以「預測未來趨勢」為導向的決策支援系統，因此在模型I到模型III主要採用learning-based為模型的建構基礎，並在最後模型IV結合換銅成本轉換公式和DINKLE公司的採購規則來給予銅採購決策支援。在模型演算法的選擇上，考量到ANN、改良ANN在許多具有時間序列、多為度特性的資料集上有著優秀的表現，本研究採取了改良ANN演算法中的單隱藏層前饋神經網路(SLFN)作為前三個模型的主體，並添加了一些機制來改善原生ANN所具有的缺點，提出SOTRS機制。資料來源方面，銅價及碎銅價資料取自了銅道網及長江有色網，銅需求及庫存量則使用了DINKLE公司2015年至2020年共六年的歷史資料。SOTRS機制的驗證會以模型III碎銅價的資料集作為驗證，並和市面上常見的演算法做比較(包含 ANN、SVR 和 DST)。CPDSS的驗證會根據DINKLE公司專家對於2015 – 2020年採購的理想決策情形做為檢測標竿，並使用上述4種方法(包含 ANN, SVR, DST, SOTRS)的預測結果根據模型IV的推理結果來給予決策建議，以此來檢驗CPDSS的成效。 zh_TW dc.description.abstract (摘要) The impact of non-ferrous metals on economic development has become increasingly important with the progress of society. Among them, copper with multiple characteristics plays a very important role in non-ferrous metals. Furthermore, the research on copper procurement is very rare although the issue of raw material procurement is so important to the manufacturing industry, there is still no a related decision support system to help enterprises make copper procurement suggestions. Therefore, the purpose of this study is to put forward an integrated copper procurement decision support system.This proposed CPDSS incorporates many factors that will affect copper procurement including future copper and shredded copper price, future demand, and inventory information. Regarding the choice of system construction method, consider CPDSS is a DSS oriented to "predicting future trends". Therefore, in model I, model II, and model III, learning-based is mainly used as the basis for the construction of the model, and finally model IV incorporates the procurement rules recommended by DINKLE company to summarize the output of the first three models and then outputs decision support. Regarding the choice of model algorithm, consider that ANN and improved ANN have great performances on many data sets with time series and highdimensional characteristics, this research adopts the single-hidden layer feedforward neural network (SLFN) which is one of the improved ANN as the main body of the first three models, and adds some additional modules to improve the shortcomings of ANN, the SOTRS mechanism is proposed. In terms of data sources, copper price and shredded copper price data are collected from ccmn.cn and https://www.tongdow.com/, and the copper demand and inventory are offered by the historical data of DINKLE company from 2015 to 2020. This study first verifies the SOTRS mechanism through the dataset of model III, and compares with the common algorithms in the researches including ANN, SVR, and DST. Then using the ideal 2015-2020 copper procurement decision offered by DINKLE company as the benchmark to measure the decision recommendation from the above four methods (CPDSS, ANN, SVR, and DST) based on the results of the previous experiment. en_US dc.description.tableofcontents Acknowledgement I摘要 IIAbstract IIITables VFigures VI1. Introduction 12. Literature review 42.1 Decision support systems (DSS) 42.2 Material procurement problem (MPP) 52.3 Learning-based method approaches 82.4 Improved ANN mechanism approaches 92.5 Pytorch 103. Copper procurement decision support system (CPDSS) 113.1 CPDSS outline 113.2 SOTRS mechanism 143.3 Reasoning mechanism 214. Experiment data 244.1 Variable description 244.2 Data preprocessing 285. Experiment results 295.1 SOTRS mechanism evaluation 305.2 CPDSS evaluation 356. Conclusion and future work 496.1 Conclusion 496.2 Limitation and future work 52Reference 53 zh_TW dc.format.extent 2753246 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0109356023 en_US dc.subject (關鍵詞) 銅採購議題 zh_TW dc.subject (關鍵詞) 混合學習型決策支援系統 zh_TW dc.subject (關鍵詞) 單隱藏層前饋神經網路 zh_TW dc.subject (關鍵詞) 趨勢預測 zh_TW dc.subject (關鍵詞) copper procurement issue en_US dc.subject (關鍵詞) decision support system en_US dc.subject (關鍵詞) single-hidden layer feedforward neural network en_US dc.subject (關鍵詞) trend forecasting en_US dc.title (題名) 銅採購決策支援系統 zh_TW dc.title (題名) A copper procurement decision support system en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) Aamodt, A., and Plaza, E. 1994. "Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches," AI communications (7:1), pp. 39-59.Angehrn, A. A., and Lüthi, H.-J. 1990. 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