A Novel Reinforcement Learning Model for Intelligent Investigation on Supply Chain Market

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姓名

鄭景州(Ching-Chou Cheng) 查詢紙本館藏

畢業系所

資訊管理學系在職專班

論文名稱

(A Novel Reinforcement Learning Model for Intelligent Investigation on Supply Chain Market)

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至系統瀏覽論文 (2025-7-1以後開放)

摘要(中)

隨著現在技術迅速創新和全球化的時代，公司和客戶在市場上帶來巨大的機會與選擇，但也面臨勞動力逐漸減少、物料成本持續上漲、產品生命週期縮短，以及各種客製化少量多樣服務的需求變化。這些原因導致市場變動快速，產業間相關企業的競爭轉變成供應鏈與供應鏈間的競爭，為穩定且提高供應鏈運作效率對整個供應鏈體系的相關企業而言，是保持競爭力的重要條件之一。要讓工廠變為智慧製造業，就需藉由人工智慧的幫助，讓管理與生產更加有彈性，創造新一代製造革命新工程。
當今AI人工智慧的發展已經快速且全面性的滲入製造業與非製造業，企業除了可以讓流程自動化更加有效率，提升對顧客服務的品質與一致性外，人工智慧更改變了原本管理方式。此外，AI人工智慧已經應用於供應鏈管理的各個環節，讓企業在營運上變得更迅速、更聰明、更精簡。本研究旨在探究企業如何在這股浪潮下，將人工智慧系統應用於供應鏈管理系統中，如何有效率地整合與管理供應商、製造商，並保持供應鏈中各階段之生產量、存貨量及倉儲配送等階段於穩定之狀態，以面對下游客戶的各種需求變化，進而提升企業評估供應商風險之依據。
這些外在科技所帶來的變化，將採購人員從一個「執行者」轉變為一個「管理者」的角色，未來執行工作的將會是眾多形式的機器人，而人員則將扮演一個運用科技，規劃並整合系統的管理者。科技發展日新月異，採購人員是否能因應外在環境的變化趨勢，迎接AI時代的來臨。
在交易市場中，原物料價格與產業新聞確實互相影響，當出現正面新聞時，價格通常會高漲，相反的，若出現負面新聞時，價格則呈現下跌狀態。本研究針對供應商必需使用到的原物料進行價格與新聞的預測，透過強化學習方式，在與環境做互動的同時，由模型自行學習市場交易的買賣情況，再藉由正評或負評的產業新聞來重新驅動模型的行動。
本研究也提出一個新型態的強化學習演算法，藉由新聞的情感來重新執行模型的買賣行為。藉由模型來預測市場趨勢，使專業的採購人員在進行採購作業時，能夠避免採購風險。希望藉由本研究確實能有效顯示出供應商的潛在高風險值，進而提供下單前評估供應商有無缺料或財務風險之依據。

摘要(英)

With the trend of economic globalization and the innovative technology, corporations and customers can obtain tremendous opportunities and selections in the market. However, they are also facing some problems, such as a lack of labor force, rising cost of materials, shorter product life cycle, and the needs of mass customized products. Therefore, stabilizing and improving the operational efficiency of the supply chain is one of the most important conditions for these enterprises to maintain competitiveness in the overall supply chain ecosystem. To make the factories become smart-manufacturing industries, we need Artificial Intelligence (AI) to achieve more flexible management and production, and even to create a new industrial revolution. In addition to achieving the efficiency of process automation and improving the quality and consistency of customer service, AI can also change the original management mode. Furthermore, AI can apply to all aspects of supply chain management (SCM) and can make the operation of enterprises faster, smarter, and more streamlined. The purpose of this research is to explore that how the enterprises can utilize AI in the SCM to efficiently integrate and manage suppliers and manufacturers.
In the trading market, the crude oil price and the industry news may affect each other. When the positive news appears, the oil price usually rises. On the contrary, the price decreases when the negative news occurs. In this research, we propose a novel Reinforcement Learning (RL) algorithm, which utilizes a multi-learning network to predict oil prices and classify emotional news, and then fuses two networks into the RL model to investigate market trends. While interacting with the environment, the RL model can learn the market transactions by itself and employs positive or negative industry news to react to the buying, selling, or holding trading behaviors. By using this model to predict market trends, the professional purchasers can avoid purchasing risks when making purchasing operations. So, this research hopes to effectively present the potential supply risks and provide a fundamental of evaluating the suppliers’ shortage or financial risks before ordering.

關鍵字(中)

★ 供應鏈管理
★ 市場情報
★ 強化學習
★ 語意分析
★ 長短期記憶

關鍵字(英)

★ Supply Chain Management
★ Market Intelligence
★ Reinforcement Learning
★ Sentiment Analysis
★ Long Short-Term Memory

論文目次

ABSTRACT..................................................I
CHINESE ABSTRACT..........................................II
ACKNOWLEDGMENTS...........................................III
TABLE OF CONTENTS.........................................IV
LIST OF FIGURES...........................................VI
LIST OF TABLES............................................VII
CHAPTER1 INTRODUCTION....................................1
1.1 BACKGROUND............................................1
1.2 MOTIVATION............................................2
1.3 PROBLEMS..............................................3
1.4 CONTRIBUTIONS.........................................4
1.5 ORGANIZATION..........................................5
CHAPTER2 RELATED WORK....................................6
2.1 SENTIMENT CLASSIFICATION OF FINANCIAL NEWS............6
2.2 PREDICTION OF TRADING MARKET..........................7
CHAPTER3 PRELIMINARY.....................................9
3.1 PROBLEM FORMULATION FOR MATERIAL TRADING..............9
3.2 MAXIMIZED THE INVESTMENT PROFIT.......................10
CHAPTER4 PROPOSED METHOD.................................11
4.1 SYSTEM ARCHITECTURE...................................11
4.2 SENTIMENT ANALYSIS FROM FINANCIAL NEWS ARTICLES.......12
4.2.1 Sentiment Analysis..................................12
4.2.2 Long Short-Term Memory Algorithm....................13
4.2.3 Representation of News Information..................15
4.3 DEEP DETERMINISTIC POLICY GRADIENT ALGORITHM..........16
4.4 COMBINING SENTIMENTAL ANALYSIS INTO DDPG ALGORITHM....18
CHAPTER5 EXPERIMENTS AND RESULTS.........................20
5.1 DATASET...............................................20
5.2 COMPARED WITH DIFFERENT METHODS.......................21
5.3 CASE ANALYSIS.........................................23
5.4 EXPERIMENTS ON SENTIMENTAL CLASSIFICATION OF NEWS.....24
5.5 EXPERIMENTS TO PREDICT MARKET TRENDS..................26
CHAPTER6 CONCLUSION......................................29
REFERENCE.................................................30

參考文獻

[1] U. Jüttner, H. Peck, and M. Christopher, “Supply chain risk management: outlining an agenda for future research,” International Journal of Logistics: Research and Applications, pp. 197-210, 2003.
[2] I. Manuj, J. T. Mentzer, “Global supply chain risk management strategies,” International Journal of Physical Distribution & Logistics Management, Vol. 38, No. 3, pp. 192-223, 2008.
[3] G. Baryannis, S. Validi, S. Dani, “Supply chain risk management and artificial intelligence: state of the art and future research directions,” International Journal of Production Research, Vol. 57, No.7, pp. 2179-2202, 2019.
[4] S. Paajanen, K. Valkokari, “The opportunities of big data analytics in supply market intelligence,” In Working Conference on Virtual Enterprises. Springer, Cham., pp. 194-205, 2017.
[5] R. Handfield, “Organizational Structure and Application of Supply Market Intelligence: Current Trends and Best Practice,” In Proceedings of the 2014 Workshop on Human Centered Big Data Research, pp. 36-40, 2014.
[6] M. R. Amin-Naseri, E. A. Gharacheh, “A hybrid artificial intelligence approach to monthly forecasting of crude oil price time series,” In the Proceedings of the 10th International Conference on Engineering Applications of Neural Networks, CEUR-WS284, pp. 160-167, 2007.
[7] K. Chen, Y. Zhou, F. Dai, “A LSTM-based method for stock returns prediction: A case study of China stock market,” IEEE international conference on big data (big data), pp. 2823-2824, 2015.
[8] T. Fischer, C. Krauss, “Deep learning with long short-term memory networks for financial market predictions,” European Journal of Operational Research, Vol. 270, No. 2, pp. 654-669, 2018.
[9] Z. Cen, J. Wang, “Crude oil price prediction model with long short term memory deep learning based on prior knowledge data transfer,” Energy 169, pp.160-171, 2019.
[10] Please refer to the newspaper on investing.com web site. https://hk.investing.com/news/commodities-news/article-69886
[11] Please refer to the newspaper on investing.com web site. https://hk.investing.com/news/commodities-news/article-75593
[12] R. B., Kumar, B. S., Kumar, C. S. S. Prasad, “Financial news classification using SVM,” International Journal of Scientific and Research Publications, Vol. 2, No.3, pp. 1-6, 2012.
[13] S. Kogan, D. Levin, B. R., Routledge, J. S., Sagi, N. A. Smith, “Predicting risk from financial reports with regression,” In Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pp. 272-280, 2009.
[14] R. P., Schumaker, H. Chen, “Textual analysis of stock market prediction using breaking financial news: The AZFin text system,” ACM Transactions on Information Systems (TOIS), Vol. 27, No.2, pp.1-19, 2009.
[15] K. Mizumoto, H. Yanagimoto, M. Yoshioka, “Sentiment analysis of stock market news with semi-supervised learning,” In 2012 IEEE/ACIS 11th International Conference on Computer and Information Science (IEEE), pp. 325-328, 2012.
[16] M. Makrehchi, S. Shah, W. Liao, “Stock prediction using event-based sentiment analysis,” In 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT) (IEEE), Vol. 1, pp. 337-342, 2013.
[17] X. Wang, Y. Liu, C. J. Sun, B. Wang, X. Wang, “Predicting polarities of tweets by composing word embeddings with long short-term memory,” In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, Vol. 1: Long Papers, pp. 1343-1353, 2015.
[18] K. Mizumoto, H. Yanagimoto, M. Yoshioka, “Sentiment analysis of stock market news with semi-supervised learning,” In 2012 IEEE/ACIS 11th International Conference on Computer and Information Science (IEEE), pp. 325-328, 2012.
[19] Y. Kim, S. R. Jeong, I. Ghani, “Text opinion mining to analyze news for stock market prediction,” Int. J. Advance. Soft Comput. Appl, Vol. 6, No.1, pp. 2074-8523, 2014.
[20] R. Levine, S. Zervos, “Stock markets, banks, and economic growth,” JOURNAL ARTICLE: The American economic review, Vol. 88, No.3, pp. 537-558, 1998.
[21] M. L. Barnes, A. T. W. Hughes, “A quantile regression analysis of the cross section of stock market returns,” Federal Reserve Bank of Boston, 2002.
[22] A. Dutta, G. Bandopadhyay, S. Sengupta, “Prediction of stock performance in indian stock market using logistic regression,” International Journal of Business and Information, Vol. 7, No.1, 2012.
[23] H. Yang, L. Chan, I. King, “Support vector machine regression for volatile stock market prediction,” In International Conference on Intelligent Data Engineering and Automated Learning, pp. 391-396, 2002.
[24] P. D. Yoo, M. H. Kim, T. Jan, “Machine learning techniques and use of event information for stock market prediction: A survey and evaluation,” In International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC′06) (IEEE), Vol. 2, pp. 835-841, 2005.
[25] A. F. Sheta, S. E. M. Ahmed, H. Faris, “A comparison between regression, artificial neural networks and support vector machines for predicting stock market index,” Soft Computing, Vol. 7, No. 8, 2015.
[26] E. Altay, M. H. Satman, “Stock market forecasting: artificial neural network and linear regression comparison in an emerging market,” Journal of Financial Management & Analysis, Vol. 18, No.2, 2005.
[27] T. Kimoto, K. Asakawa, M., Yoda, M. Takeoka, “Stock market prediction system with modular neural networks,” In 1990 IJCNN international joint conference on neural networks (IEEE), pp. 1-6, 1990.
[28] D. M. Nelson, A. C. Pereira, R. A. de Oliveira, “Stock market′s price movement prediction with LSTM neural networks,” In 2017 International joint conference on neural networks (IJCNN) (IEEE), pp. 1419-1426, 2017.
[29] M. Roondiwala, H. Patel, S. Varma, “Predicting stock prices using LSTM,” International Journal of Science and Research (IJSR), Vol. 6, No.4, pp. 1754-1756, 2017.
[30] M. Dixon, “A high‐frequency trade execution model for supervised learning,” High Frequency, Vol. 1, No.1, pp. 32-52, 2018.
[31] Y. Deng, F. Bao, Y. Kong, Z. Ren, Q. Dai, “Deep direct reinforcement learning for financial signal representation and trading,” IEEE transactions on neural networks and learning systems, Vol. 28, No.3, pp. 653-664, 2016.
[32] H. Li, C. H. Dagli, D. Enke, “Forecasting series-based stock price data using direct reinforcement learning,” In 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No. 04CH37541) (IEEE), Vol. 2, pp. 1103-1108, 2004.
[33] Z. Xiong, X. Y. Liu, S. Zhong, H. Yang, A. Walid, “Practical deep reinforcement learning approach for stock trading,” arXiv preprint arXiv:1811.07522, 2018.
[34] Q. V. Dang, “Reinforcement Learning in Stock Trading,” In International Conference on Computer Science, Applied Mathematics and Applications, pp. 311-322, 2019.
[35] X. Li, Y. Li, Y. Zhan, X. Y. Liu, “Optimistic bull or pessimistic bear: adaptive deep reinforcement learning for stock portfolio allocation,” arXiv preprint arXiv:1907.01503, 2019.
[36] Z. Liang, H. Chen, J. Zhu, K. Jiang, Y. Li, “Adversarial deep reinforcement learning in portfolio management,” arXiv preprint arXiv:1808.09940, 2018.
[37] J. W. Lee, E. Hong, J. Park, “A Q-learning based approach to design of intelligent stock trading agents,” In 2004 IEEE International Engineering Management Conference (IEEE Cat. No. 04CH37574) (IEEE), Vol. 3, pp. 1289-1292, 2004.
[38] Z. Xiong, X. Y. Liu, S. Zhong, H. Yang, A. Walid, “Practical deep reinforcement learning approach for stock trading,” arXiv preprint arXiv:1811.07522, 2018.
[39] L. Baird, “Residual algorithms: Reinforcement learning with function approximation,” In Machine Learning Proceedings 1995, pp. 30-37, 1995.
[40] G. Barth-Maron, M. W. Hoffman, D. Budden, W. Dabney, D. Horgan, D. Tb, T. Lillicrap, “Distributed distributional deterministic policy gradients,” arXiv preprint arXiv:1804.08617, 2018.
[41] A. R. Azhikodan, A. G. Bhat, M. V. Jadhav, “Stock trading bot using deep reinforcement learning,” In Innovations in Computer Science and Engineering, pp. 41-49, 2019.
[42] S. Ioffe, C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” In Proceedings of the 32nd International Conference on Machine Learning, pp. 448-456, 2015.
[43] P. D. Turney, “Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews,” In Proceedings of the 40th annual meeting on Association for Computational Linguistics, pp. 417-424, 2002.
[44] B. Pang, L. Lee, S. Vaithyanathan, “Thumbs up?: sentiment classification using machine learning techniques,” In Proceedings of the ACL-02 conference on Empirical methods in natural language processing on Association for Computational Linguistics, Vol. 10, pp. 79-86, 2002.
[45] C. Wei-Fan, L. W. Ku, “Introduction to CSentiPackage: Tools for Chinese Sentiment Analysis,” Journal of Library & Information Science, Vol. 44, No. 1, pp. 24-41, 2018.
[46] T. Mikolov, M. Karafiát, L. Burget, J. Černocký, S. Khudanpur, “Recurrent neural network based language model,” In Eleventh annual conference of the international speech communication association, pp. 1045-1048, 2010.
[47] F. A. Gers, N. N. Schraudolph, J. Schmidhuber, “Learning precise timing with LSTM recurrent networks,” Journal of machine learning research, Vol. 3, pp. 115-143, 2002.
[48] T. Noraset, C. Liang, L. Birnbaum, D. Downey, “Definition modeling: Learning to define word embeddings in natural language,” In Thirty-First AAAI Conference on Artificial Intelligence, 2017.
[49] N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,” The journal of machine learning research, Vol. 15, No. 1, pp. 1929-1958, 2014.
[50] D. Silver, G. Lever, N. Heess, T. Degris, D. Wierstra, M. Riedmiller, “Deterministic policy gradient algorithms,” International Conference on Machine Learning, Vol. 32, 2014.

指導教授

陳以錚(Yi-Cheng Chen)

審核日期

2020-6-29

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