由於產品生命週期的縮短，企業在設計與製造產品時，必須更全面地考量不同的層面，因應自然資源的短缺與越來越嚴格的環保法規等限制下，迫使企業在新產品開發階段必須更加思考如何獲得更高的利潤、滿足顧客的需求與評估產品的生命週期；在過去十年中，許多的研究已將供應鏈的概念和永續性的要素結合並融入產品設計當中；然而，大多數的研究主要著重於確定性方法的探討，但企業在永續性發展的改善上將面臨環境所帶來的不確定性，而目前都還沒有適當的方法進行分析。本研究旨在提出一個整合性的方法，運用模糊集合論來處理供應鏈中所產生的不確定性因素，並且考量產品的拆解性與供應鏈網路中的成本、前置時間與碳排放量；在產品的開發階段，此永續性的觀點不僅僅運用在產品的設計上，同時也考量了整個供應鏈的設計；最後利用一個案例來驗證此方法的優點。

Enterprises have been increasingly forced to consider broader aspects while designing and manufacturing products. The scarcity of natural resources plus sterner environmental regulations have obliged enterprises to develop new products that can satisfy profit, people, and planet during their life cycles. During the last decade, numerous research efforts have incorporated supply chain and sustainability factors into product design. However, most of these studies have generated deterministic based methodologies. Enterprises intending to improve sustainability under uncertainties have been left without an appropriate method of analysis. This study presents a method based on fuzzy set theory intended to tackle uncertainty in the supply chain and to highlights sustainable design concepts involving product disassemblability, supply chain network cost, lead time, and carbon footprints. The sustainability of both the product and the supply chain are considered at the product design stage. A case study demonstrates the advantage of this methodology.

Table of Contents
Abstract 4
Table of Contents 5
List of Figures 6
List of Tables 6
1. Introduction 7
2. Literature review 9
2.1 Coordination between Product and Supply Chain Management 9
2.2 Product Architecture 10
2.3 Closed Loop Supply Chain 11
2.3.1 Green Supply Chain 13
2.3.2 Reverse Logistic 14
2.3.3 Closed Loop Supply Chain 15
2.4 Fuzzy Approach for Addressing the Uncertainties in Supply Chain 17
2.4.1 Fuzzy Set Theory 18
2.4.2 Fuzzy Set 18
2.5 Summary 20
3. Methodology 21
3.1 Generate the design concept 23
3.2 Design for disassembly (DfDA) 23
3.3 Fuzzy method 25
3.3.1 Fuzzy c-means 26
3.3.2 Triangular Fuzzy Numbers (TFNs) 28
3.4 Defuzzification 29
3.5 Mathematical Formulation 30
3.5.1 Notation 30
3.5.2 Objective Function 31
3.5.3 Constraints 33
3.6 AHP and Taguchi loss function 35
3.6.1 Taguchi Loss Function 35
3.6.2 Analytic hierarchy process (AHP) 35
3.6.3 Estimation of loss through Taguchi Loss Function and AHP 36
4. Case study 37
5. Conclusion 48
6. References 50

List of Figures
Figure 2-1 – The relationship of value recovery 13
Figure 2-2 – Traditional supply chain (Beamon, 1999) 14
Figure 3-1 – Research Scope 21
Figure 3-2 – Research Framework 22
Figure 3-3– Product Disassembly Graph 24
Figure 3-4 – Transition Matrix 24
Figure 3-5 – Supply Chain Structure 25
Figure 3-6 – Triangular Fuzzy Numbers 29
Figure 3-7 –Triangular fuzzy numbers (TFNs) 29
Figure 4-1 – Scooter Function Basis Model 37
Figure 4- 2 – Vendors’ location in TW 39
Figure 4-3 – FCM Clustering Results 40
Figure 4-4 – Cost Interval 40
Figure 4-5 – Distributor A in Satisfied Level α = 0.6 (Unit: min) 43
Figure 4-6 – Three indicators (Cost, Time, and Carbon footprint) 44
Figure 4-7 – Design Concept Selection 45
Figure 4-8 – Simapro 7.3 assess the impact at the recycled component 46
Figure 4-9 – PD and Closed Loop SC Decisions 47

List of Tables
Table 4-1 – Eight & Six Modules of a Scooter 38
Table 4-2 – Components of a Scooter 38
Table 4-3 – Estimated Lead Time of Component Suppliers 41
Table 4-4 – Estimated Lead Time of Module Suppliers 41
Table 4-5 – Estimated Lead Time of Focal Company to all distributors 42
Table 4-6 – Characteristic and relative values of each Concept 44
Table 4-7 – Concept characteristic Taguchi loss 45
Table 4-8 – Pairwise comparison matrix of the three indicators 45

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