臺灣博碩士論文加值系統

本研究在於發展同步工程環境的產品開發整合性決策評估模式。整合模式係涵蓋產品開發之多層次、多目標的需求，考量產品「生命週期」各項功能之整合，解決多目標需求的資源分配衝突。提出包括：目標層次，決策層次-含目標決策層次及作業決策層次，功能層次，設計發展層次，作業層次等多層次的整合模式。
經由產品開發的整合模式，進而發展一適用於「初步設計」之決策評估模式，採用模糊理論來建構決策評估模式，建立包括：目標決策層次以「模糊邏輯」作決策，而在作業決策層次則以「模糊多準則」作決策之模式。
本研究的主要貢獻為:
(1)發展產品開發的「模糊邏輯」決策模式：將顧客模糊性、主觀性、不夠明確性的需求轉換成產品的特性，建構「模糊邏輯」決策IF-THEN規則，作為產品開發的目標及決策的依據，建立先期決策的能力。
(2)結合「模糊邏輯」決策模式與類神經網路發展電腦輔助決策的架構：將「模糊邏輯」決策模式建立成可供類神經網路作訓練的學習樣本，再利用類神經網路來協助決策，本項發展係基於同步工程環境中以電腦輔助作決策的趨勢。
(3)發展一更具效率且實用性的「模糊多準則」決策模式：可有效而迅速處理方案(n)、準則(m)數目甚多時的決策問題(例如:m≦350, n為任意數目) 【Chen and Hwang,1992】。且可綜合處理「各方案」相對於「各評估準則」的評分、權重可以是明確數據、模糊數(三角、梯形…模糊數)、或模糊語意變數等不同資料的實用性決策問題。本項發展係考量同步工程環境中的實際決策問題，當方案、準則數目甚多時需要有更具效率的決策模式。
(4)以案例分析驗證本研究建立之產品開發的決策模式：結合實際產品開發的案例，以印證本研究建構的決策評估模式。

　This research is to develop an integrated decision model for product development under concurrent engineering environments. The integrated decision model involves the multi-level, multi-goal requirements of product. Also, It considers the function integration of the total life cycle of product. It can resolve the conflict of insufficient resource. The multi-level integrated model involves：Goal Space, Decision Space - Goal Decision Space and Activity Decision Space, Function Space, Product ＆ Process Design Space, and Activity Space. The model considers the top-down and bottom-up integration.
Following the integration model, a decision model for preliminary design is developed. The model considers the integration of tangible factors and intangible factors; that is constructed by fuzzy theory. The Goal Decision Space is constructed by fuzzy logic decision model. The Activity Decision Space is constructed by fuzzy multiple-attribute decision model.
The main contributions of this research are:
(1) To develop a fuzzy logic decision model for product development：Convert the customer requirement as the product parameters. The customer requirement may full of vagueness, subjective, or uncertainty. The fuzzy logic decision model is using IF-THEN rules, it is the decision baseline for product development; and thus suffer the early capability for decision.
(2) To combine the fuzzy logic decision model with neural networks as a computer-aided decision framework：To convert the fuzzy logic decision model as the training parameters of neural network; then use the trained neural network to support decision. The idea is based on computer-aided decision that is the trend of concurrent engineering environments in the future.
(3) To develop an efficient fuzzy multiple-attribute decision model for practical use. It can efficiently and rapidly solve large scale decision problem (i.e. attributes m≦350, and any number of alternatives, n) 【Chen and Hwang, 1992】. It can also solve any type of input weights and rating data, whether crisp or fuzzy data. Fuzzy data can be in any form: triangular, trapezoidal, normal distribution or convex form''s fuzzy numbers, or as fuzzy linguistic variables. The idea is based on the real-world decision problem that is complex, and there needs an efficient method to solve the practical problems.
(4) To demonstrate the constructed decision model by using a case study：Using the example of product development, to illustrate and to verify the procedure of the constructed decision model.

第一章　緒論 …………………………………………… 1
1.1 研究動機 .................................................................................1
1.2 研究目的 .................................................................................3
1.3 研究架構及流程 ................................................................4
第二章 文獻回顧 ....................................................................................6
2.1 同步工程 ..................................................................................6
2.2 模糊理論及模糊邏輯 ................................................................9
2.3 模糊多準則決策 .............................................................. 11
2.4 類神經網路之演算 .............................................................. 19
2.5 作業為基的成本制度 ...............................................................22
第三章 研究方法及模式架構建立 ............................................ 28
3.1 研究範圍及問題描述 ............................................................. 28
3.2 研究模式之方法架構建立 ........................................... 30
3.3 目標決策層次－模糊邏輯決策模式 ............................................37
3.4 作業決策層次－模糊多準則決策模式 ...........................45
.
第四章 案例應用及分析. ............................................................. 57
4.1 產品階層架構之建立 ............................................................. 57
4.2 目標決策層次－模糊邏輯決策模式結合類神經網路之應用… 59
4.3 整合作業決策層次及目標決策層次之應用................................ 69
第五章 結論及未來展望 ............................................................. 91
5.1 本研究之結論與貢獻 ..............................................................91
5.2 未來展望 ............................................................................... 93
參考文獻 .....................................................……………………… 95

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