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A semiconductor distributor that plays a third-party role in the supply chain will buy diverse components from different
suppliers, warehouse and resell them to a number of electronics manufacturers with vendor-managed inventories, while
suffering both risks of oversupply and shortage due to demand uncertainty. However, demand fluctuation and supply chain
complexity are increasing due to shortening product life cycle in the consumer electronics era and long lead time for capacity
expansion for high-tech manufacturing. Focusing realistic needs of a leading distributor for semiconductor components and
modules, this study aims to construct a UNISON framework based on deep reinforcement learning (RL) for dynamically
selecting the optimal demand forecast model for each of the products with the corresponding demand patterns to empower
smart production for Industry 3.5. Deep RL that integrates deep learning architecture and RL algorithm can learn successful policies from the dynamic and complex real world. The reward function mechanism of deep RL can reduce negative
impact of demand uncertainty. An empirical study was conducted for validation showing practical viability of the proposed
approach. Indeed, the developed solution has been in real settings.

Deep reinforcement learning for selecting demand f

Data engineer

林聖凱

約兩年多的資料分析與機器學習專案經驗，且有將專案平台化及跨部門合作經驗，喜歡自我學習與分享交流，在專案執行與學習的過程中總結出「資料是行為與過程的紀錄，演算法是分析與歸納的工具，結果解釋與應用才是數據分析的根本價值」的心得



- 決策分析研究室 (指導教授: 簡禎富 講座教授)
- 助教經歷: 決策分析(EMBA, 107上), 電子商務網站管理(107下), 大數據與決策分析(AIMS在職班, 108上), AIMS 課程總助教 (108下)

清華大學

政治大學

台積電

Data Engineer

1. 建立廠務資料流
從InSQL pump資料進HDFS 並利用 spark將資料整裡並放入Hbase

實習於QE, Big data team, 執行以下專案, 並獲得2020預聘資格
- 預測性維修: 廠務CVD管的維修預測模型, 採用氣壓感測器資料, 提取時間序列特徵建立 boosting model, 可提前3~5天偵測到氣壓下降之問題
- 虛擬量測: 晶圓線寬的虛擬量測模型, 採用FDC資料, 利用 embedding 和 convolution layer 建立 Neural Network model (自建loss function)


台灣美光記憶體股份有限公司

Intern

實習於生產處, 執行以下專案, 實習期間專案衍生為產學合作主題, 成果獲得科技部2019未來科技突破獎
- 參數優化: CNC工具機效率優化模型, 採用主軸電流及震動資料, 利用ensemble model 建立製程預測模型用於優化製程參數，提升銑銷效率8~20%

漢翔航空工業

主要負責中心合作廠商的分析專案，過去執行過的專案包含需求預測、參數優化及機台設備的預測性保養, 其中需求預測專案是針對半導體元件供應商建立需求預測模型, 利用庫存及歷史需求等建立7個預測模型, 並採用DQN model作動態模型選擇, 並於2020將成果發表成期刊論文

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