1. NCCU Academic Hub
  2. 學術產出
  3. College of Commerce
  4. Theses
  5. Item

學術產出-Theses

Article View/Open

Publication Export

Google ScholarTM

政大圖書館

Citation Infomation

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 &quot;predicting future trends&quot;. 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. "Decision Model for Country Site Selection of Overseas Clothing Plants," The International Journal of Advanced Manufacturing Technology (29:3), pp. 408-417.
Babu, C. N., and Reddy, B. E. 2014. "A Moving-Average Filter Based Hybrid Arima–Ann Model for Forecasting Time Series Data," Applied Soft Computing (23), pp. 27-38.
Banjac, G., Vašak, M., and Baotić, M. 2015. "Adaptable Urban Water Demand Prediction System," Water Science and Technology: Water Supply (15:5), pp. 958-964.
Barros, L. Y., Poletto, J. C., Neis, P. D., Ferreira, N. F., and Pereira, C. H. 2019. "Influence of Copper on Automotive Brake Performance," Wear (426), pp. 741-749.
Belz, R., and Mertens, P. 1996. "Combining Knowledge-Based Systems and Simulation to Solve Rescheduling Problems," Decision Support Systems (17:2), pp. 141-157.
Benardos, P., and Vosniakos, G.-C. 2007. "Optimizing Feedforward Artificial Neural Network Architecture," Engineering applications of artificial intelligence (20:3), pp. 365-382.
Bengio, Y. 2012. "Practical Recommendations for Gradient-Based Training of Deep Architectures," in Neural Networks: Tricks of the Trade. Springer, pp. 437-478.
Bergerson, K., and Wunsch, D. C. 1991. "A Commodity Trading Model Based on a Neural Network-Expert System Hybrid," Ijcnn-91-seattle international joint conference on neural networks: IEEE, pp. 289-293.
Berson, A., and Thearling, K. 1999. Building Data Mining Applications for Crm. McGraw-Hill, Inc.
Bonczek, R. H., Holsapple, C. W., and Whinston, A. B. 2014. Foundations of Decision Support Systems. Academic Press.
Çelik, U., and Başarır, Ç. 2017. "The Prediction of Precious Metal Prices Via Artificial Neural Network by Using Rapidminer," Alphanumeric Journal (5:1), pp. 45-54.
Charlot, P., and Marimoutou, V. 2014. "On the Relationship between the Prices of Oil and the Precious Metals: Revisiting with a Multivariate Regime-Switching Decision Tree," Energy Economics (44), pp. 456-467.
Chi, R. H., and Kiang, M. Y. 1991. "An Integrated Approach of Rule-Based and Case-Based Reasoning for Decision Support," Proceedings of the 19th annual conference on Computer Science, pp. 255-267.
Chirodea, M. C., Novac, O. C., Novac, C. M., Bizon, N., Oproescu, M., and Gordan, C. E. 2021. "Comparison of Tensorflow and Pytorch in Convolutional Neural Network-Based Applications," 2021 13th International Conference on Electronics, Computers and Artificial Intelligence (ECAI): IEEE, pp. 1-6.
Chuang, T.-T., and Yadav, S. B. 1998. "The Development of an Adaptive Decision Support System," Decision support systems (24:2), pp. 73-87.
Confalonieri, M., Barni, A., Valente, A., Cinus, M., and Pedrazzoli, P. 2015. "An Ai Based Decision Support System for Preventive Maintenance and Production Optimization in Energy Intensive Manufacturing Plants," 2015 IEEE International Conference on Engineering, Technology and Innovation/International Technology Management Conference (ICE/ITMC): IEEE, pp. 1-8.
Cunha, A. L., Santos, M. O., Morabito, R., and Barbosa-Póvoa, A. 2018. "An Integrated Approach for Production Lot Sizing and Raw Material Purchasing," European Journal of Operational Research (269:3), pp. 923-938.
de Oliveira, L. S., Gruetzmacher, S. B., and Teixeira, J. P. 2021. "Covid-19 Time Series Prediction," Procedia Computer Science (181), pp. 973-980.
Deb, M., Kaur, P., and Sarma, K. K. 2019. "Fuzzy Approach to Decision Support System Design for Inventory Control and Management," Journal of Intelligent Systems (28:4), pp. 549-557.
Dehghani, H., and Bogdanovic, D. 2018. "Copper Price Estimation Using Bat Algorithm," Resources Policy (55), pp. 55-61.
Dumitru, C., and Maria, V. 2013. "Advantages and Disadvantages of Using Neural Networks for Predictions," Ovidius University Annals, Series Economic Sciences (13:1).
Elguindi, J., Hao, X., Lin, Y., Alwathnani, H. A., Wei, G., and Rensing, C. 2011. "Advantages and Challenges of Increased Antimicrobial Copper Use and Copper Mining," Applied microbiology and biotechnology (91:2), pp. 237-249.
Eom, H. B., and Lee, S. M. 1990. "A Survey of Decision Support System Applications (1971–April 1988)," Interfaces (20:3), pp. 65-79.
Fabian, T., Fisher, J., Sasieni, M., and Yardeni, A. 1959. "Purchasing Raw Material on a Fluctuating Market," Operations Research (7:1), pp. 107-122.
Gao, C., Huang, J.-b., Chen, J.-y., Tang, W.-y., Wang, Z.-p., and Liu, J.-x. 2018. "Industrial Transmission Effect of International Metal Price Shocks in Perspective of Industry Chain," Journal of Central South University (25:12), pp. 2929-2943.
Gao, Z., and Tang, L. 2003. "A Multi-Objective Model for Purchasing of Bulk Raw Materials of a Large-Scale Integrated Steel Plant," International Journal of Production Economics (83:3), pp. 325-334.
Ghumman, A. R., Ahmad, S., and Hashmi, H. N. 2018. "Performance Assessment of Artificial Neural Networks and Support Vector Regression Models for Stream Flow Predictions," Environmental monitoring and assessment (190:12), pp. 1-20.
Ginzberg, M. J., and Stohr, E. A. 1982. "Decision Support Systems: Issues and Perspectives,".
Gomez, F., Guzman, J. I., and Tilton, J. E. 2007. "Copper Recycling and Scrap Availability," Resources Policy (32:4), pp. 183-190.
Gorry, G. A., and Scott Morton, M. S. 1971. "A Framework for Management Information Systems,".
Han, Y., Yang, Y., and Zhou, X. 2013. "Co-Regularized Ensemble for Feature Selection," IJCAI International Joint Conference on Artificial Intelligence.
Hao, J., Li, J., Wu, D., and Sun, X. 2020. "Portfolio Optimisation of Material Purchase Considering Supply Risk–a Multi-Objective Programming Model," International Journal of Production Economics (230), p. 107803.
Keen, P. G. 1980. "Adaptive Design for Decision Support Systems," Acm Sigoa Newsletter (1:4-5), pp. 15-25.
Khoshalan, H. A., Shakeri, J., Najmoddini, I., and Asadizadeh, M. 2021. "Forecasting Copper Price by Application of Robust Artificial Intelligence Techniques," Resources Policy (73), p. 102239.
Kingsman, B. G. 1986. "Purchasing Raw Materials with Uncertain Fluctuating Prices," European Journal of Operational Research (25:3), pp. 358-372.
Kolodner, J. L. 1992. "An Introduction to Case-Based Reasoning," Artificial intelligence review (6:1), pp. 3-34.
Kundig, K. J., and Joseph, G. 1998. "Copper: Its Trade Manufacture, Use, and Environmental Status,".
Kwok, T.-Y., and Yeung, D.-Y. 1997. "Constructive Algorithms for Structure Learning in Feedforward Neural Networks for Regression Problems," IEEE transactions on neural networks (8:3), pp. 630-645.
Lasheras, F. S., de Cos Juez, F. J., Sánchez, A. S., Krzemień, A., and Fernández, P. R. 2015. "Forecasting the Comex Copper Spot Price by Means of Neural Networks and Arima Models," Resources Policy (45), pp. 37-43.
Lee, C.-Y., Chou, B.-J., and Huang, C.-F. 2022. "Data Science and Reinforcement Learning for Price Forecasting and Raw Material Procurement in Petrochemical Industry," Advanced Engineering Informatics (51), p. 101443.
Lee, C. K., Lin, D., and Pasari, R. 2014. "Strategic Procurement from Forward Contract and Spot Market," Industrial Management & Data Systems).
Li, H., and Love, P. E. 1999. "Combining Rule-Based Expert Systems and Artificial Neural Networks for Mark-up Estimation," Construction Management & Economics (17:2), pp. 169-176.
Li, M., Lian, S., Wang, F., Zhou, Y., Chen, B., Guan, L., and Wu, Y. 2020a. "A Decision Support System Using Hybrid Ai Based on Multi-Image Quality Model and Its Application in Color Design," Future Generation Computer Systems (113), pp. 70-77.
Li, S.-T., Chiu, K.-C., and Chiu, T.-H. 2020b. "An Application on Building Information Model to Procurement Strategy of Copper Raw Material with Big Data Analytics," 2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM): IEEE, pp. 696-700.
Liang, J., and Liu, R. 2015. "Stacked Denoising Autoencoder and Dropout Together to Prevent Overfitting in Deep Neural Network," 2015 8th international congress on image and signal processing (CISP): IEEE, pp. 697-701.
Liu, C., Hu, Z., Li, Y., and Liu, S. 2017. "Forecasting Copper Prices by Decision Tree Learning," Resources Policy (52), pp. 427-434.
Liu, S., Duffy, A. H., Whitfield, R. I., and Boyle, I. M. 2010. "Integration of Decision Support Systems to Improve Decision Support Performance," Knowledge and Information Systems (22:3), pp. 261-286.
Liu, S., Zhang, Y., Su, Z., Lu, M., Gu, F., Liu, J., and Jiang, T. 2020a. "Recycling the Domestic Copper Scrap to Address the China’s Copper Sustainability," Journal of Materials Research and Technology (9:3), pp. 2846-2855.
Liu, Y., Yang, C., Huang, K., and Gui, W. 2020b. "Non-Ferrous Metals Price Forecasting Based on Variational Mode Decomposition and Lstm Network," Knowledge-Based Systems (188), p. 105006.
Méndez-Suárez, M., García-Fernández, F., and Gallardo, F. 2019. "Artificial Intelligence Modelling Framework for Financial Automated Advising in the Copper Market," Journal of Open Innovation: Technology, Market, and Complexity (5:4), p. 81.
Ma, C., Liu, Z., Cao, Z., Song, W., Zhang, J., and Zeng, W. 2020. "Cost-Sensitive Deep Forest for Price Prediction," Pattern Recognition (107), p. 107499.
Ma, L., and Khorasani, K. 2003. "A New Strategy for Adaptively Constructing Multilayer Feedforward Neural Networks," Neurocomputing (51), pp. 361-385.
Morales, L., and Andreosso-O’Callaghan, B. 2011. "Comparative Analysis on the Effects of the Asian and Global Financial Crises on Precious Metal Markets," Research in International Business and Finance (25:2), pp. 203-227.
Navin, G. V. 2015. "Big Data Analytics for Gold Price Forecasting Based on Decision Tree Algorithm and Support Vector Regression (Svr)," International Journal of Science and Research (IJSR) (4:3), pp. 2026-2030.
O`Keefe, R. M., and Roach, J. W. 1987. "Artificial Intelligence Approaches to Simulation," Journal of the Operational Research Society (38:8), pp. 713-722.
Power, D. J. 2002. Decision Support Systems: Concepts and Resources for Managers. Greenwood Publishing Group.
Prentzas, J., and Hatzilygeroudis, I. 2007. "Categorizing Approaches Combining Rule‐Based and Case‐Based Reasoning," Expert Systems (24:2), pp. 97-122.
Rao, N., Brownee, S. M., and Sarma, P. 2004. "Gis-Based Decision Support System for Real Time Water Demand Estimation in Canal Irrigation Systems," Current Science), pp. 628-636.
Russo, F., and Chilà, G. 2009. "Safety of Users in Road Evacuation: Modelling and Dss for Demand," WIT Transactions on Ecology and the Environment (120), pp. 465-474.
Sachan, S., Yang, J.-B., Xu, D.-L., Benavides, D. E., and Li, Y. 2020. "An Explainable Ai Decision-Support-System to Automate Loan Underwriting," Expert Systems with Applications (144), p. 113100.
Samuelsson, C., and Björkman, B. 2014. "Copper Recycling," in Handbook of Recycling. Elsevier, pp. 85-94.
Seguel, F., Carrasco, R., Adasme, P., Alfaro, M., and Soto, I. 2015. "A Meta-Heuristic Approach for Copper Price Forecasting," International Conference on Informatics and Semiotics in Organisations: Springer, pp. 156-165.
Shao, Y., and Wang, S. 2016. "Productivity Growth and Environmental Efficiency of the Nonferrous Metals Industry: An Empirical Study of China," Journal of Cleaner Production (137), pp. 1663-1671.
Sprague Jr, R. H., and Carlson, E. D. 1982. Building Effective Decision Support Systems. Prentice Hall Professional Technical Reference.
Sun, G., Liu, Y., and Lan, Y. 2011. "Fuzzy Two-Stage Material Procurement Planning Problem," Journal of Intelligent Manufacturing (22:2), pp. 319-331.
Tan, C. L., Quah, T. S., and Teh, H. H. 1996. "An Artificial Neural Network That Models Human Decision Making," Computer (29:3), pp. 64-70.
Tsaih, R. 1993. "The Softening Learning Procedure," Mathematical and computer modelling (18:8), pp. 61-64.
Tsaih, R., Hsu, Y., and Lai, C. C. 1998. "Forecasting S&P 500 Stock Index Futures with a Hybrid Ai System," Decision support systems (23:2), pp. 161-174.
Turban, E., and Watkins, P. R. 1986. "Integrating Expert Systems and Decision Support Systems," Mis Quarterly), pp. 121-136.
Wang, D., Wang, L., Zhang, Z., Wang, D., Zhu, H., Gao, Y., Fan, X., and Tian, F. 2021. "“Brilliant Ai Doctor” in Rural Clinics: Challenges in Ai-Powered Clinical Decision Support System Deployment," Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, pp. 1-18.
Wang, M., Chen, W., Zhou, Y., and Li, X. 2017. "Assessment of Potential Copper Scrap in China and Policy Recommendation," Resources policy (52), pp. 235-244.
Wang, S.-T., and Lin, W.-T. 2010. "Research on Integrating Different Methods of Neural Networks with Case-Based Reasoning and Rule-Based System to Infer Causes of Notebook Computer Breakdown," Expert Systems with Applications (37:6), pp. 4544-4555.
Wu, C., and Chau, K.-W. 2010. "Data-Driven Models for Monthly Streamflow Time Series Prediction," Engineering Applications of Artificial Intelligence (23:8), pp. 1350-1367.
Xiarchos, I. M., and Fletcher, J. J. 2009. "Price and Volatility Transmission between Primary and Scrap Metal Markets," Resources, Conservation and Recycling (53:12), pp. 664-673.
Yalcintas, M., and Akkurt, S. 2005. "Artificial Neural Networks Applications in Building Energy Predictions and a Case Study for Tropical Climates," International journal of energy research (29:10), pp. 891-901.
Yang, R.-H. 2022. An Adaptive Learning-Based Model for Copper Price Forecasting, Master thesis, National Chengchi University, Taipei, Taiwan, pp. 1-78.an Adaptive Learning-Based Model for Copper Price Forecasting."
Yong-quan, L., Shuang-mei, Z., and Kang, Y. 2017. "Concurrent Project Optimal Steel Procurement Strategy in Construction Engineering," Commercial Research (59:2), p. 143.
Zhang, J., Cui, S., Xu, Y., Li, Q., and Li, T. 2018. "A Novel Data-Driven Stock Price Trend Prediction System," Expert Systems with Applications (97), pp. 60-69.
描述 碩士
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-Lingen_US
dc.contributor.author (Authors) 商肯豪zh_TW
dc.contributor.author (Authors) Shang, Ken-Haoen_US
dc.creator (作者) 商肯豪zh_TW
dc.creator (作者) Shang, Ken-Haoen_US
dc.date (日期) 2022en_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) G0109356023en_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) 109356023zh_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 &quot;predicting future trends&quot;. 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 II
Abstract III
Tables V
Figures VI
1. Introduction 1
2. Literature review 4
2.1 Decision support systems (DSS) 4
2.2 Material procurement problem (MPP) 5
2.3 Learning-based method approaches 8
2.4 Improved ANN mechanism approaches 9
2.5 Pytorch 10
3. Copper procurement decision support system (CPDSS) 11
3.1 CPDSS outline 11
3.2 SOTRS mechanism 14
3.3 Reasoning mechanism 21
4. Experiment data 24
4.1 Variable description 24
4.2 Data preprocessing 28
5. Experiment results 29
5.1 SOTRS mechanism evaluation 30
5.2 CPDSS evaluation 35
6. Conclusion and future work 49
6.1 Conclusion 49
6.2 Limitation and future work 52
Reference 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/#G0109356023en_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 issueen_US
dc.subject (關鍵詞) decision support systemen_US
dc.subject (關鍵詞) single-hidden layer feedforward neural networken_US
dc.subject (關鍵詞) trend forecastingen_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 systemen_US
dc.type (資料類型) thesisen_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. "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. "Decision Model for Country Site Selection of Overseas Clothing Plants," The International Journal of Advanced Manufacturing Technology (29:3), pp. 408-417.
Babu, C. N., and Reddy, B. E. 2014. "A Moving-Average Filter Based Hybrid Arima–Ann Model for Forecasting Time Series Data," Applied Soft Computing (23), pp. 27-38.
Banjac, G., Vašak, M., and Baotić, M. 2015. "Adaptable Urban Water Demand Prediction System," Water Science and Technology: Water Supply (15:5), pp. 958-964.
Barros, L. Y., Poletto, J. C., Neis, P. D., Ferreira, N. F., and Pereira, C. H. 2019. "Influence of Copper on Automotive Brake Performance," Wear (426), pp. 741-749.
Belz, R., and Mertens, P. 1996. "Combining Knowledge-Based Systems and Simulation to Solve Rescheduling Problems," Decision Support Systems (17:2), pp. 141-157.
Benardos, P., and Vosniakos, G.-C. 2007. "Optimizing Feedforward Artificial Neural Network Architecture," Engineering applications of artificial intelligence (20:3), pp. 365-382.
Bengio, Y. 2012. "Practical Recommendations for Gradient-Based Training of Deep Architectures," in Neural Networks: Tricks of the Trade. Springer, pp. 437-478.
Bergerson, K., and Wunsch, D. C. 1991. "A Commodity Trading Model Based on a Neural Network-Expert System Hybrid," Ijcnn-91-seattle international joint conference on neural networks: IEEE, pp. 289-293.
Berson, A., and Thearling, K. 1999. Building Data Mining Applications for Crm. McGraw-Hill, Inc.
Bonczek, R. H., Holsapple, C. W., and Whinston, A. B. 2014. Foundations of Decision Support Systems. Academic Press.
Çelik, U., and Başarır, Ç. 2017. "The Prediction of Precious Metal Prices Via Artificial Neural Network by Using Rapidminer," Alphanumeric Journal (5:1), pp. 45-54.
Charlot, P., and Marimoutou, V. 2014. "On the Relationship between the Prices of Oil and the Precious Metals: Revisiting with a Multivariate Regime-Switching Decision Tree," Energy Economics (44), pp. 456-467.
Chi, R. H., and Kiang, M. Y. 1991. "An Integrated Approach of Rule-Based and Case-Based Reasoning for Decision Support," Proceedings of the 19th annual conference on Computer Science, pp. 255-267.
Chirodea, M. C., Novac, O. C., Novac, C. M., Bizon, N., Oproescu, M., and Gordan, C. E. 2021. "Comparison of Tensorflow and Pytorch in Convolutional Neural Network-Based Applications," 2021 13th International Conference on Electronics, Computers and Artificial Intelligence (ECAI): IEEE, pp. 1-6.
Chuang, T.-T., and Yadav, S. B. 1998. "The Development of an Adaptive Decision Support System," Decision support systems (24:2), pp. 73-87.
Confalonieri, M., Barni, A., Valente, A., Cinus, M., and Pedrazzoli, P. 2015. "An Ai Based Decision Support System for Preventive Maintenance and Production Optimization in Energy Intensive Manufacturing Plants," 2015 IEEE International Conference on Engineering, Technology and Innovation/International Technology Management Conference (ICE/ITMC): IEEE, pp. 1-8.
Cunha, A. L., Santos, M. O., Morabito, R., and Barbosa-Póvoa, A. 2018. "An Integrated Approach for Production Lot Sizing and Raw Material Purchasing," European Journal of Operational Research (269:3), pp. 923-938.
de Oliveira, L. S., Gruetzmacher, S. B., and Teixeira, J. P. 2021. "Covid-19 Time Series Prediction," Procedia Computer Science (181), pp. 973-980.
Deb, M., Kaur, P., and Sarma, K. K. 2019. "Fuzzy Approach to Decision Support System Design for Inventory Control and Management," Journal of Intelligent Systems (28:4), pp. 549-557.
Dehghani, H., and Bogdanovic, D. 2018. "Copper Price Estimation Using Bat Algorithm," Resources Policy (55), pp. 55-61.
Dumitru, C., and Maria, V. 2013. "Advantages and Disadvantages of Using Neural Networks for Predictions," Ovidius University Annals, Series Economic Sciences (13:1).
Elguindi, J., Hao, X., Lin, Y., Alwathnani, H. A., Wei, G., and Rensing, C. 2011. "Advantages and Challenges of Increased Antimicrobial Copper Use and Copper Mining," Applied microbiology and biotechnology (91:2), pp. 237-249.
Eom, H. B., and Lee, S. M. 1990. "A Survey of Decision Support System Applications (1971–April 1988)," Interfaces (20:3), pp. 65-79.
Fabian, T., Fisher, J., Sasieni, M., and Yardeni, A. 1959. "Purchasing Raw Material on a Fluctuating Market," Operations Research (7:1), pp. 107-122.
Gao, C., Huang, J.-b., Chen, J.-y., Tang, W.-y., Wang, Z.-p., and Liu, J.-x. 2018. "Industrial Transmission Effect of International Metal Price Shocks in Perspective of Industry Chain," Journal of Central South University (25:12), pp. 2929-2943.
Gao, Z., and Tang, L. 2003. "A Multi-Objective Model for Purchasing of Bulk Raw Materials of a Large-Scale Integrated Steel Plant," International Journal of Production Economics (83:3), pp. 325-334.
Ghumman, A. R., Ahmad, S., and Hashmi, H. N. 2018. "Performance Assessment of Artificial Neural Networks and Support Vector Regression Models for Stream Flow Predictions," Environmental monitoring and assessment (190:12), pp. 1-20.
Ginzberg, M. J., and Stohr, E. A. 1982. "Decision Support Systems: Issues and Perspectives,".
Gomez, F., Guzman, J. I., and Tilton, J. E. 2007. "Copper Recycling and Scrap Availability," Resources Policy (32:4), pp. 183-190.
Gorry, G. A., and Scott Morton, M. S. 1971. "A Framework for Management Information Systems,".
Han, Y., Yang, Y., and Zhou, X. 2013. "Co-Regularized Ensemble for Feature Selection," IJCAI International Joint Conference on Artificial Intelligence.
Hao, J., Li, J., Wu, D., and Sun, X. 2020. "Portfolio Optimisation of Material Purchase Considering Supply Risk–a Multi-Objective Programming Model," International Journal of Production Economics (230), p. 107803.
Keen, P. G. 1980. "Adaptive Design for Decision Support Systems," Acm Sigoa Newsletter (1:4-5), pp. 15-25.
Khoshalan, H. A., Shakeri, J., Najmoddini, I., and Asadizadeh, M. 2021. "Forecasting Copper Price by Application of Robust Artificial Intelligence Techniques," Resources Policy (73), p. 102239.
Kingsman, B. G. 1986. "Purchasing Raw Materials with Uncertain Fluctuating Prices," European Journal of Operational Research (25:3), pp. 358-372.
Kolodner, J. L. 1992. "An Introduction to Case-Based Reasoning," Artificial intelligence review (6:1), pp. 3-34.
Kundig, K. J., and Joseph, G. 1998. "Copper: Its Trade Manufacture, Use, and Environmental Status,".
Kwok, T.-Y., and Yeung, D.-Y. 1997. "Constructive Algorithms for Structure Learning in Feedforward Neural Networks for Regression Problems," IEEE transactions on neural networks (8:3), pp. 630-645.
Lasheras, F. S., de Cos Juez, F. J., Sánchez, A. S., Krzemień, A., and Fernández, P. R. 2015. "Forecasting the Comex Copper Spot Price by Means of Neural Networks and Arima Models," Resources Policy (45), pp. 37-43.
Lee, C.-Y., Chou, B.-J., and Huang, C.-F. 2022. "Data Science and Reinforcement Learning for Price Forecasting and Raw Material Procurement in Petrochemical Industry," Advanced Engineering Informatics (51), p. 101443.
Lee, C. K., Lin, D., and Pasari, R. 2014. "Strategic Procurement from Forward Contract and Spot Market," Industrial Management & Data Systems).
Li, H., and Love, P. E. 1999. "Combining Rule-Based Expert Systems and Artificial Neural Networks for Mark-up Estimation," Construction Management & Economics (17:2), pp. 169-176.
Li, M., Lian, S., Wang, F., Zhou, Y., Chen, B., Guan, L., and Wu, Y. 2020a. "A Decision Support System Using Hybrid Ai Based on Multi-Image Quality Model and Its Application in Color Design," Future Generation Computer Systems (113), pp. 70-77.
Li, S.-T., Chiu, K.-C., and Chiu, T.-H. 2020b. "An Application on Building Information Model to Procurement Strategy of Copper Raw Material with Big Data Analytics," 2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM): IEEE, pp. 696-700.
Liang, J., and Liu, R. 2015. "Stacked Denoising Autoencoder and Dropout Together to Prevent Overfitting in Deep Neural Network," 2015 8th international congress on image and signal processing (CISP): IEEE, pp. 697-701.
Liu, C., Hu, Z., Li, Y., and Liu, S. 2017. "Forecasting Copper Prices by Decision Tree Learning," Resources Policy (52), pp. 427-434.
Liu, S., Duffy, A. H., Whitfield, R. I., and Boyle, I. M. 2010. "Integration of Decision Support Systems to Improve Decision Support Performance," Knowledge and Information Systems (22:3), pp. 261-286.
Liu, S., Zhang, Y., Su, Z., Lu, M., Gu, F., Liu, J., and Jiang, T. 2020a. "Recycling the Domestic Copper Scrap to Address the China’s Copper Sustainability," Journal of Materials Research and Technology (9:3), pp. 2846-2855.
Liu, Y., Yang, C., Huang, K., and Gui, W. 2020b. "Non-Ferrous Metals Price Forecasting Based on Variational Mode Decomposition and Lstm Network," Knowledge-Based Systems (188), p. 105006.
Méndez-Suárez, M., García-Fernández, F., and Gallardo, F. 2019. "Artificial Intelligence Modelling Framework for Financial Automated Advising in the Copper Market," Journal of Open Innovation: Technology, Market, and Complexity (5:4), p. 81.
Ma, C., Liu, Z., Cao, Z., Song, W., Zhang, J., and Zeng, W. 2020. "Cost-Sensitive Deep Forest for Price Prediction," Pattern Recognition (107), p. 107499.
Ma, L., and Khorasani, K. 2003. "A New Strategy for Adaptively Constructing Multilayer Feedforward Neural Networks," Neurocomputing (51), pp. 361-385.
Morales, L., and Andreosso-O’Callaghan, B. 2011. "Comparative Analysis on the Effects of the Asian and Global Financial Crises on Precious Metal Markets," Research in International Business and Finance (25:2), pp. 203-227.
Navin, G. V. 2015. "Big Data Analytics for Gold Price Forecasting Based on Decision Tree Algorithm and Support Vector Regression (Svr)," International Journal of Science and Research (IJSR) (4:3), pp. 2026-2030.
O`Keefe, R. M., and Roach, J. W. 1987. "Artificial Intelligence Approaches to Simulation," Journal of the Operational Research Society (38:8), pp. 713-722.
Power, D. J. 2002. Decision Support Systems: Concepts and Resources for Managers. Greenwood Publishing Group.
Prentzas, J., and Hatzilygeroudis, I. 2007. "Categorizing Approaches Combining Rule‐Based and Case‐Based Reasoning," Expert Systems (24:2), pp. 97-122.
Rao, N., Brownee, S. M., and Sarma, P. 2004. "Gis-Based Decision Support System for Real Time Water Demand Estimation in Canal Irrigation Systems," Current Science), pp. 628-636.
Russo, F., and Chilà, G. 2009. "Safety of Users in Road Evacuation: Modelling and Dss for Demand," WIT Transactions on Ecology and the Environment (120), pp. 465-474.
Sachan, S., Yang, J.-B., Xu, D.-L., Benavides, D. E., and Li, Y. 2020. "An Explainable Ai Decision-Support-System to Automate Loan Underwriting," Expert Systems with Applications (144), p. 113100.
Samuelsson, C., and Björkman, B. 2014. "Copper Recycling," in Handbook of Recycling. Elsevier, pp. 85-94.
Seguel, F., Carrasco, R., Adasme, P., Alfaro, M., and Soto, I. 2015. "A Meta-Heuristic Approach for Copper Price Forecasting," International Conference on Informatics and Semiotics in Organisations: Springer, pp. 156-165.
Shao, Y., and Wang, S. 2016. "Productivity Growth and Environmental Efficiency of the Nonferrous Metals Industry: An Empirical Study of China," Journal of Cleaner Production (137), pp. 1663-1671.
Sprague Jr, R. H., and Carlson, E. D. 1982. Building Effective Decision Support Systems. Prentice Hall Professional Technical Reference.
Sun, G., Liu, Y., and Lan, Y. 2011. "Fuzzy Two-Stage Material Procurement Planning Problem," Journal of Intelligent Manufacturing (22:2), pp. 319-331.
Tan, C. L., Quah, T. S., and Teh, H. H. 1996. "An Artificial Neural Network That Models Human Decision Making," Computer (29:3), pp. 64-70.
Tsaih, R. 1993. "The Softening Learning Procedure," Mathematical and computer modelling (18:8), pp. 61-64.
Tsaih, R., Hsu, Y., and Lai, C. C. 1998. "Forecasting S&P 500 Stock Index Futures with a Hybrid Ai System," Decision support systems (23:2), pp. 161-174.
Turban, E., and Watkins, P. R. 1986. "Integrating Expert Systems and Decision Support Systems," Mis Quarterly), pp. 121-136.
Wang, D., Wang, L., Zhang, Z., Wang, D., Zhu, H., Gao, Y., Fan, X., and Tian, F. 2021. "“Brilliant Ai Doctor” in Rural Clinics: Challenges in Ai-Powered Clinical Decision Support System Deployment," Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, pp. 1-18.
Wang, M., Chen, W., Zhou, Y., and Li, X. 2017. "Assessment of Potential Copper Scrap in China and Policy Recommendation," Resources policy (52), pp. 235-244.
Wang, S.-T., and Lin, W.-T. 2010. "Research on Integrating Different Methods of Neural Networks with Case-Based Reasoning and Rule-Based System to Infer Causes of Notebook Computer Breakdown," Expert Systems with Applications (37:6), pp. 4544-4555.
Wu, C., and Chau, K.-W. 2010. "Data-Driven Models for Monthly Streamflow Time Series Prediction," Engineering Applications of Artificial Intelligence (23:8), pp. 1350-1367.
Xiarchos, I. M., and Fletcher, J. J. 2009. "Price and Volatility Transmission between Primary and Scrap Metal Markets," Resources, Conservation and Recycling (53:12), pp. 664-673.
Yalcintas, M., and Akkurt, S. 2005. "Artificial Neural Networks Applications in Building Energy Predictions and a Case Study for Tropical Climates," International journal of energy research (29:10), pp. 891-901.
Yang, R.-H. 2022. An Adaptive Learning-Based Model for Copper Price Forecasting, Master thesis, National Chengchi University, Taipei, Taiwan, pp. 1-78.an Adaptive Learning-Based Model for Copper Price Forecasting."
Yong-quan, L., Shuang-mei, Z., and Kang, Y. 2017. "Concurrent Project Optimal Steel Procurement Strategy in Construction Engineering," Commercial Research (59:2), p. 143.
Zhang, J., Cui, S., Xu, Y., Li, Q., and Li, T. 2018. "A Novel Data-Driven Stock Price Trend Prediction System," Expert Systems with Applications (97), pp. 60-69.		zh_TW
dc.identifier.doi (DOI) 10.6814/NCCU202200877en_US