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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/84037

    Title: A Novel Reinforcement Learning Model for Intelligent Investigation on Supply Chain Market
    Authors: 鄭景州;Cheng, Ching-Chou
    Contributors: 資訊管理學系在職專班
    Keywords: 供應鏈管理;市場情報;強化學習;語意分析;長短期記憶;Supply Chain Management;Market Intelligence;Reinforcement Learning;Sentiment Analysis;Long Short-Term Memory
    Date: 2020-06-29
    Issue Date: 2020-09-02 17:57:43 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 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。
    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。;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.
    Appears in Collections:[資訊管理學系碩士在職專班 ] 博碩士論文

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