工業化促進了經濟發展，也大幅提升生活的便利性及舒適度，然而隨著全球人口基數上升，食物、原物料等需求也隨之上漲，加上氣候變遷，天然資源的耗用以及缺水的狀況也日趨嚴重，因此環境的永續發展已成為一大重要議題。其中，製造業所帶來的大量汙染使情況變得更加嚴峻，企業應發展得以有效率降低污染的解決方案，以擔起企業的社會責任，也為產品增添永續概念所帶來的潛在價值。隨著第四次工業革命的發展，先進技術不斷推陳出新，包含人工智慧、大數據分析、雲端運算和物聯網，這些關鍵數位化分析技術若和永續發展的綠色供應鏈架構結合，可協助企業建構相關分析模型以降低資源浪費，促進企業永續發展。
本論文提出一套綠色供應鏈架構，結合紫式決策研究方法，協助業界定義綠色供應鏈問題，結合工業4.0先進數據分析技術的應用，萃取永續相關資料的潛在價值及建構相關分析優化模型，透過本研究所發展的架構，可讓企業在供應鏈各區塊導入永續發展的概念時有跡可循，逐步朝向綠色供應鏈及綠色製造邁進，為產品附加綠色價值，增強企業競爭力。本研究以半導體產業之需求預測及廢水處理作為實證研究案例，以驗證所提出的綠色供應鏈架構效度。在需求預測實證案例中，本研究應用深度強化學習結合綠色供應鏈之紫式決策研究方法，協助半導體零組件供應商根據不同需求型態，動態選擇適合的預測模型，以有效減少預測誤差造成的相關成本；在廢水處理實證案例中，以長短期記憶法類神經網路先預測過氧化氫濃度，接著應用穩健式優化模型滾動式優化調控過氧化氫酶的幫浦，降低含過氧化氫的廢水排放相關風險及成本。實證結果顯示，本研究架構確實可協助企業定義綠色供應鏈問題，以及建構數據分析技術優化資源運用及減少污染的可能風險，達到企業、社會及環境的永續發展。

While industrialization promotes economic progress and raises our standard of living in terms of comfort and convenience, the worldwide demand for natural resources encourages the depletion of the environment. The problems with water scarcity are partly a result of continuous climate change. Environmental sustainability is becoming a crucial concern for businesses and society as a whole. Manufacturing industries, in particular, are responsible for a significant portion of pollution emissions. The environment will become polluted without effective waste and pollutant management, and the release of dangerous chemicals could endanger public health. For their social obligation and to raise the sustainable value of their products, businesses must come up with effective ways to reduce contamination.
Leading nations are compelled to develop their own manufacturing strategies, such as Industry 4.0 and the Advanced Production Partnership (AMP), in order to enable digital manufacturing and maintain their manufacturing sector's competitive advantage. With the help of cutting-edge technologies from the fourth industrial revolution, like cloud computing, the Internet of Things (IoT), big data analytics, and cyber physical systems (CPS), waste could be cut down and a sustainable green production system could be made stronger.
The main contribution of this study are listed as follows: (1) This study propose a green supply chain framework combining UNISON decision-making for green supply chain management, helping to identify the critical green supply chain issues and leverage the advanced Industry 4.0 technologies to realize the empirical implementation of the green supply chain strategy. The application of digital technologies can facilitate the adoption of sustainable practices and fulfill the sustainable requirements to add additional values to products that could enhance the competitiveness of enterprises. Two empirical studies in the semiconductor industry adopted the proposed UNISON green supply chain decision framework to define and solve the problem. (2) For the empirical study of demand forecasting, deep reinforcement learning is applied along with the UNISON framework of green supply chain to assist the semiconductor component distributors to dynamically select suitable forecasting models according to demand patterns. The proposed model effectively reduces forecast errors and associated costs by 28% compared to the case company. (3) The empirical case of wastewater treatment proposed a hybrid model that includes the hydrogen peroxide forecasting model that adopted long-short-term memory and the decision model for catalase dosage pump to derive robust optimal treatments, reduce risks and costs associated with wastewater discharge. The r-square of the proposed prediction model is up to 0.854 and the MAPE is 17.495%, which is acceptable for the real case. The proposed robust optimization model has been proved to be more suitable for the data with uncertainty. The results of two empirical studies have shown its practical viability and could reduce the related risks and costs for the environment and enterprises effectively. The proposed framework can be used in the real world to help companies, the environment, and society grow in a way that is good for the long term and to boost industrial sustainability.

Table of Contents
Table of Contents i
List of Figures iv
Nomenclature viii
Chapter 1 Introduction 1
1.1 Background and motivation 1
1.2 Research aims 6
1.3 Dissertation organization 7
Chapter 2 Literature review 8
2.1 Green supply chain 8
2.2 Demand forecast 16
2.2.1 Demand forecasting in the semiconductor industry 16
2.2.2 Forecast model selection 20
2.2.3 Deep reinforcement learning (DRL) 22
2.3 Wastewater management 23
2.3.1 Wastewater treatment in the semiconductor industry 23
2.3.2 Forecasting the concentration of contamination 26
2.3.3 Pump scheduling for wastewater treatment 30
Chapter 3 The framework of green supply chain toward industrial sustainability 33
3.1 Research framework 33
3.2 Influential factors 36
3.3 Industry 4.0 technologies 37
3.4 Green supply chain practices 38
3.4.1 Plan 39
3.4.2 Green procurement 40
3.4.3 Green production 41
3.4.4 Green logistics 41
3.4.5 Waste management 42
3.5 Performance 43
3.6 UNISON framework for green supply chain management 43
Chapter 4 Demand forecasting problem for a semiconductor component distributor 48
4.1 Problem background 48
4.2 UNISON decision framework for demand forecast problem 49
4.3 Demand pattern classification 50
4.4 Demand forecast model 51
4.4.1. Naïve forecast 52
4.4.2 Simple moving average 52
4.4.3 Exponential smoothing 53
4.4.4 Syntetos–Boylan approximation (SBA) 53
4.4.5 Artificial neural network (ANN) 54
4.4.6 Recurrent neural network (RNN) 55
4.4.7 Support vector regression (SVR) 55
4.5 Deep reinforcement learning (DRL) for forecasting model selection 57
4.6 Performance evaluation 61
4.7 Data preparation for the case studies 63
4.8 Validation 65
4.9 Discussion 73
Chapter 5 Wastewater treatment problem for a semiconductor manufacturing 79
5.1 Problem background 79
5.2 Framework for wastewater treatment problem 80
5.3 Data acquisition and data preprocessing 81
5.4 LSTM model for hydrogen peroxide forecasting 82
5.5 Performance evaluation for LSTM prediction model 84
5.6 Robust optimization for catalase pump scheduling model 84
5.7 Data preparation for the case studies 88
5.8 Validation 89
5.9 Discussion 96
Chapter 6 Conclusion 99
References 102

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