IC 電路設計是藉由電路的設計與整理安排，建構電子產品運作的核心，其
中IC 設計實踐的手法則是透過數百家不同的運營商業模式而成型，IC 設計服務
及晶圓製造代工在產業鏈中更是舉足輕重。IC 設計服務業本身並無產品，經由
協助客戶解決IC 設計中所遭遇的技術困難為主要營收；此外，耳熟能詳的晶圓
製造代工產業亦沒有產品，而是協助IC 設計實體化，免除IC 設計公司在設計之
餘需要投入過多資本且須克服製程技術的躓礙，兩者看似截然不同的產業皆可以
幫助IC 設計以更精良的品質，更低廉的成本內嵌入消費者所使用的電子用品中。
本研究旨在發展一套決策支援系統預測每一個晶圓量產服務專案所需之人力，給
予積體電路設計服務公司人力派遣之準則，此決策支援系統應用機器學習及基因
演算法之技術，結合領域專家現有之經驗法則，並比較數種機器學習演算法之優
劣及其對基因演算法之適應程度，擇優建立一個隨時間遞進隨之優化之人力需求
模型，減少人為估計之偏誤，提升決策品質同時增進產能、降低成本，達成智慧
管理的營運模式。

IC Design has been an industry which provides flexible application-specific integrated
circuit (ASIC) services enabling semiconductor manufacturing companies for flexible
decision. Although the industry influences semiconductor supply chain significantly,
capacity portfolio and planning issues of IC design industry is seldom mentioned in the
past studies. For IC design service industry, the main productivity denotes to IC design
which is influenced by the performance of project management from workforce
allocation. The purpose of this study is to develop an intelligent agent to predict the
workforce required for each wafer production service project, and thus based on the
prediction, the intelligent agent is able to provide an IC design service company with
workforce allocation strategy. Featuring learning algorithms and analyzing from the
existing data, the study trains a XGBoost model combining Genetic Algorithm based
parameter optimization mechanism. The proposed intelligent agent contributes to Total
Resource Management (TRM) to enhance productivity, reduce costs and become
intelligence management.

Chapter 1 Introduction 1
1.1 Background and Motivation 1
1.2 Research Objectives 3
1.3 Thesis Organization 3
Chapter 2 Literature Review 5
2.1 Human Resource Demand Forecasting 5
2.1.1 Demand Forecasting 5
2.1.2 Human Resource Forecasting 6
2.2 Extreme Gradient Boosting 7
2.2.1 Gradient boosting Machine 7
2.2.2 Extreme Gradient Boosting 8
2.2.3 LightGBM 9
2.3 Genetic Algorithm 9
2.4 Intelligent Agents & Artificial Intelligence 11
2.4.1 Categories of Intelligent Agent 13
Chapter 3 Proposed Research Framework 14
3.1 Problem Definition 15
3.2 Data Acquisition 15
3.3 Feature Engineering 16
3.4 Model Construction for Intelligent Agent 17
3.4.1 Model Selection 17
3.4.2 Model Construction 18
Chapter 4 An Empirical Study 21
4.1 Problem Definition 21
4.2 Data Acquisition 21
4.2.1 Data preparation 21
4.2.2 Data Cleaning 22
4.3 Feature Engineering 24
4.3.1 Feature Selection 24
4.4 Model Construction for Intelligent Agent 26
4.4.1 Model Selection 26
4.4.2 Sparse matrix 26
4.4.3 Model Construction 26
4.4.4 Intelligent Agent 29
4.5 Efficiency Improvement 31
4.5.1 Compare with cooperate company use domain knowledge 31
4.5.2 Compare with grid search method 32
4.5.3 Benefit 33
Chapter 5 Conclusion and future direction 36
5.1 Conclusion 36
5.2 Future direction 37
References 38

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