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研究生： 陳星豪
Hsing-hao Chen
論文名稱： 以模糊層級分析法發展多準則供應商選擇之研究
A Research Based on Fuzzy AHP for Multi-criteria Supplier Selection in Supply Chain
指導教授： 周碩彥
Shuo-Yan Chou
口試委員: 張聖麟
Sheng-Lin Chang
楊文鐸
Wen-Dwo Yang
學位類別： 碩士
Master
系所名稱： 管理學院 - 工業管理系
Department of Industrial Management
論文出版年： 2005
畢業學年度： 93
語文別： 英文
論文頁數： 84
中文關鍵詞： 多準則決策 、模糊理論 、供應商選擇 、層級分析法 、供應鏈管理 、決策支援系統
外文關鍵詞： multi-criteria decision-making
相關次數： 點閱：281  下載：20 
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    • 一家企業必須從不同的上游供應商中，購買許多不同類型的零組件或原物料來製造或是裝配它的產品。在這樣的情形下，採購部門負責的供應商選擇問題成為一個十分關鍵的議題。對策略採購的決策者來說，選擇傑出的供應商不只是一個複雜且擾人的工作，而且經常反覆發生。因此，策略採購決策者極需要一套有效率、有根據且客觀的工具來協助他們即時選擇合適的供應商。
    為了滿足上述的需求，我們應用EXCEL VBA建構一套以模糊層級分析法為基礎的決策支援系統。此系統將模糊理論與群組決策結合層級分析法來減低決策者主觀偏好的影響，並且評估過程中控制不精確與不精準的變異。
    Cheng et al. (1999b)為每個可量化的準則建立各自的歸屬函數。為了更容易衡量可量化的準則，在文中我們改善其方法。此外，經由整合所有決策者主觀的意見，形成群體決策來不可量化準則和準則權重。文中提出的方法，同時加入決策者的信心水準與風險指標，使得決策者可以衡量真實情境來適當調整此兩參數。最後，以一個真實的供應商選擇案例來模擬此系統。我們的決策支援系統協助案例公司L從三家提供USB線的候選供應商中選擇出績效最好的兩家。

    A company must purchase a lot of diverse components and raw materials from different upstream suppliers to manufacture or assemble its products. Thus, the supplier selection problem which is the responsibility of the purchasing department becomes a critical issue in this situation. However, it is not only a very complicated and perplexing task to select outstanding suppliers for decision makers of strategic purchasing, but also it arises rapidly. For this reason, the decision makers of strategic purchasing greatly require an efficient, valid and fair tool to assist them in selecting appropriate suppliers forthwith.
    A decision support system is developed to address foregoing demand. This system combines fuzzy set theory and group decision with the AHP to decrease the influence of decision makers’ subjective preferences and control the uncertain and imprecise variations during evaluation process. In order to measure tangible criteria more easily, we improve the method of Cheng et al. (1999b) which is to establish individual membership function for each tangible criterion. Besides, the intangible criteria and criteria weights are determined by group decision which can integrate all decision makers’ subjective opinions. The degree of confidence and risk index are also joined, so that decision makers can adjust them to match real context. Finally, a case of supplier selection about USB cables is simulated by our approach. This system helps company L select two preferred suppliers from candidate suppliers.

    Abstract I Acknowledgements II Content III List of Figures VI List of Tables VII Chapter 1 Introduction 1 1.1 Motivation and Background 1 1.2 Objective 3 1.3 Methodology 3 1.4 Organization of Thesis 6 Chapter 2 Literature Review 7 2.1 Supply Chain Structure 7 2.2 Supplier Selection Problem 10 2.3 MCDM on Supplier Selection Problem 14 2.4 Analytic Hierarchy Process 15 2.5 Fuzzy Set Theory 19 2.5.1 Fuzzy System 20 2.5.1 Triangular Fuzzy Numbers 21 2.5.2 Arithmetic Operations on Triangular Fuzzy Numbers 22 2.5.3 Linguistic Variable 23 2.6 Fuzzy AHP 23 2.7 TOPSIS 25 Chapter 3 Model Development 28 3.1 Assumptions and Notations 29 3.2 Determine the Fuzzy Judgement Matrix 32 3.2.1 Construct Hierarchical Structure 32 3.2.2 Evaluate the Tangible Sub-criteria 33 3.2.3 Evaluate the Intangible Sub-criteria 34 3.2.4 Obtain the Fuzzy Judgement Matrix 35 3.3 Determine the Fuzzy Performance Matrix 37 3.3.1 Determine the Fuzzy Weight Vector 37 3.3.2 Synthesize 39 3.4 Determine the Crisp Performance Matrix 40 3.4.1 Determine the Interval Performance Matrix 41 3.4.2 Consider Risk Index and Defuzzification 43 3.5 Rank the Alternatives Using TOPSIS 44 Chapter 4 An Empirical Study: Supplier Selection 47 4.1 Scenario 48 4.2 Define the Problem 48 4.2.1 Set up Criteria for Supplier Selection 49 4.2.2 Construct Hierarchical Structure 52 4.3 Calculate the Fuzzy Judgement Matrix 53 4.3.1 Measure the Tangible Sub-criteria 53 4.3.2 Measure the Intangible Sub-criteria 54 4.3.3 Attain the Fuzzy Judgement Matrix 55 4.4 Calculate the Fuzzy Performance Matrix 57 4.4.1 Construct the Fuzzy Weight Vector 57 4.4.2 Attain the Fuzzy Performance Matrix 59 4.5 Calculate the Crisp Performance Matrix 60 4.5.1 Join α-cut 60 4.5.2 Join Risk Issue 61 4.6 Rank Suppliers 62 4.7 Summary 63 Chapter 5 Conclusion and Future Directions 65 5.1 Conclusion 65 5.2 Future Directions 66 Reference 67

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