供應鏈管理是企業最近相當重視的一個領域。就供應鏈管理而言，一向被是認為從原物料的供應，透過生產製造，到最終消費者手中的一個網路架構，其間也包含了各項相關資訊的傳遞。因此，為了去提升整體供應鏈的競爭力，企業不僅要整合內部的資源，也要減少外部不確定性因素的來源。然而在大部分存貨模式中，牽涉到環境中不確定性因素時，許多的注意力仍集中在顧客需求的機率模式上，直到最近的幾年，外部供應的不確定性才逐漸被重視。所以在供應鏈管理的外部環境中主要兩個不確定性來源，一個是來自顧客的需求，另一個則是來自外部原物料的供應。
要根除推行供應鏈管理時的不確定性因素是不可能的，但是適當策略的採行則可以降低受到不確定性因素的波及。所以本研究目的是解決供應鏈存貨在不確定性因素下的採購問題，並使建構出來之採購決策支援系統在不確定性因素發生時，能決定在供應鏈管理所規劃的時間範圍裡每次存貨管制的採購量。並期望透過公司在以往物料採購和管理的經驗，建構出一套結合模糊理論與類神經網路的採購決策支援系統。
經由實例的研究，實驗的結果顯示模糊類神經網路能有效的學習已存在的採購管理規則。除此之外，模糊類神經網路也能夠快速的處理大量的資料，所以採購人員可以定期的評核採購項目和採購決策，使決策在採購件的數量或品質發生變化時能做必要的修正，以讓企業能快速的反應市場的變化。並提供給較無經驗的採購人員一個快速的依循準則。

Supply chain management is an area that has recently received a great deal of attention in the business. Supply chain management is generally viewed as a network of actions linked by a material flow from suppliers through production to end customers and containing an information flow. To raise the competitiveness of a supply chain, enterprises not only integrate the internal resources but also decrease the sources of external uncertainty. In most of the inventory models that involve uncertainties in the environment, the attention has been focused on the probabilistic modeling of the customer demand side. Up until the recent years the uncertainties in the supply side have not received the amount of treatment they deserved. Two sources of uncertainty inherent in the external environment in which the supply chain operates were identified: customer demand and external supply of raw material.
It is impossible to root the sources of uncertainties out, while supply chain management is carried out. But adopting the suitable tactics can reduce the influences of uncertainties. The objective of this research is to solve the procurement problems with the uncertain factors of supply chain inventory, and to develop a decision support system, that would determine the order quantities of each inventory in the presence of uncertainty for the whole supply chain management during a finite time horizon. A decision support system integrating fuzzy theory and neural network by past experiences of material purchasing and managing is developed.
Through a real world case study, the experimental result shows that the fuzzy neural networks can learn the existing procurement management rules effectively. Besides, fuzzy neural network is capable of processing a lot of data quickly. A buyer can periodically review purchasing items as well as their procurement strategies and make necessary adjustments if quantity or quality of products altered. Therefore, the enterprises can react to market fluctuations faster. For those who do not have much experience in procurement, this model can provide a guideline to make the decisions.

中文摘要.......................................................i
Abstract......................................................ii
誌 謝.........................................................iv
目 次..........................................................v
表目錄.......................................................vii
圖目錄......................................................viii
第一章 緒論....................................................1
1.1 研究背景與動機............................................1
1.2 研究目的...................................................2
1.3 研究範圍與限制............................................3
1.4 研究流程...................................................4
第二章 文獻探討................................................6
2.1供應鏈管理..................................................6
2.1.1 供應鏈管理的定義.........................................6
2.1.2 供應鏈管理整體目標.......................................7
2.1.3 供應鏈管理中不確定性因素之探討...........................8
2.2存貨管理...................................................13
2.2.1 存貨的意義..............................................13
2.2.2 傳統存貨管理系統的理論..................................14
2.2.3 存貨政策................................................14
2.3模糊理論...................................................16
2.3.1 模糊集合................................................16
2.3.2 語意變數................................................18
2.3.3 模糊化與解模糊化........................................19
2.3.4 模糊邏輯推論模式........................................23
2.4類神經網路.................................................25
2.4.1 類神經網路的基本架構....................................26
2.4.2 類神經網路的分類........................................28
2.4.3 類神經網路的特性........................................29
2.4.4 倒傳遞神經網路..........................................29
2.5模糊類神經網路.............................................33
2.5.1 模糊類神經網路發展......................................33
2.5.2 模糊類神經網路基本架構..................................35
2.6決策支援系統...............................................35
2.7模糊德爾菲法...............................................37
第三章 研究方法...............................................38
3.1模式決策因子之建立.........................................38
3.2資料整理與建構.............................................40
3.3建構模糊類神經網路模型.....................................41
3.3.1 模糊類神經網路架構......................................42
3.3.2 模糊類神經網路演算法....................................45
3.3.3 網路學習過程............................................48
3.4決策支援系統之建構.........................................50
第四章 實例探討...............................................51
4.1資料收集...................................................51
4.1.1 採購作業因素的選取......................................51
4.1.2 問卷資料的進行方式......................................53
4.2模糊類神經網路模型建構.....................................54
4.2.1網路模型.................................................54
4.2.2網路架構初始加權值設定...................................56
4.3網路參數分析與設定.........................................56
4.4測試與評估.................................................59
4.4.1模糊類神經網路模式.......................................59
4.4.2複迴歸分析模式...........................................60
4.4.3模糊類神經網路與複迴歸模式的比較.........................62
4.5決策支援系統操作與功能.....................................63
第五章 結論與建議.............................................68
5.1結論.......................................................68
5.2研究貢獻...................................................69
5.3建議.......................................................69
參考文獻......................................................71
附 錄.........................................................76

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