臺灣博碩士論文加值系統

在本論文中，主要探討如何求解單目標及多目標模糊最適化問題，並詳細地介紹其原理與執行步驟。
於單目標問題中，我們藉助模糊邏輯，將不明確參數及限制條件轉換成一具有隸屬度函數之模糊集合，
如此，目標函數即被視為一模糊目標。再藉由數學規劃原理之分析得知，原目標值位於最樂觀及最悲觀
條件之間，由上述兩目標值得以建立對目標函數之模糊化敘述。當問題完成模糊化後，即可依據模糊決
策原理，完成對單目標模糊最適化問題之數學描述，對問題求解。
於多目標問題中，我們依然藉助模糊邏輯，基於各單目標最適解所建立之理想解償付表，將所獲致
各目標之不可接受水準值與最適值，由數學規劃原理，得以建立各目標之模糊化敘述。同樣的，當
完成模糊化後，即可依據原湖決策對多目標最適化問題完成數學描述，對問題求解。最後，依據本
研究所提出之模糊最適化方法與步驟，分別對批次程序實作單目標及多目標模糊最適化設計。

In this thesis,we mainly discuss how to solve the single objective and multi-objective
fuzzy optimization problems and ,moreover,introduce the methodology in detail.
In single objective optimization problem,by means of fuzzy logic,we transfer uncertain
parameters and constraints into fuzzy sets with membership functions.Therefore,objective
function is considered as a fuzzy one.Through the analysis of mathematical programming,
we know that original objective value locates between the condition of pessimistic view
and the condition of optimistic view.According to above two objective values, the fuzzy
description of objective function could be constructed.After fuzzification ,one can
reformulate the original single objective optimization into a new mathematical model by principle of fuzzy decision and solve it.We again apply the concept of fuzzy decision in multi-objective optimization problem.
Base on the optimum of each objective we can establish a payoff table . And using the
value of unacceptable level and optimal value of each objective fuzzification can be
achieved by means of mathematical programming.Similarly,the problem of muti-objective
optimization problem can be reformulated and solved.Finally,we apply methodology and
procedures for single objective and multi-objective fuzzy optimization on batch process
design.

1 緒論
1.1 前言
1.2 研究動機與目的
1.3 組織章節
2 單目標模糊最適化
2.1 模糊資源最適化問題
2.1.1 限制條件的模糊化
2.1.2 目標函數的模糊化
2.1.3 模糊資源最適化
2.1.4 模糊資源解題流程
2.2 模糊目標之最適化問題
2.2.1 目標函數中參數的模糊化
2.2.2 目標函數的模糊化
2.2.3 模糊目標最適化問題
2.2.4 目標函數之解題流程
2.3 模糊限制條件之最適化問題
2.3.1 限制條件中參數的模糊化
2.3.2 目標函數的模糊化
2.3.3 模糊限制條件最適化問題
2.3.4 模糊限制條件參數之解題流程
2.4 全模糊最適化問題
2.4.1 參數及資源的模糊化
2.4.2 目標函數的模糊化
2.4.3 全模糊最適化問題
2.4.4 全模糊最適化解題流程
2.5 演算範例
2.5.1 模糊資源
2.5.2 模糊目標
2.5.3 模糊限制條件
2.5.4 全模糊
3 多目標模糊最適化
3.1 多目標規劃法之回顧
3.2 模糊多目標規劃法
3.2.1 模糊多目標規劃的性質
3.2.2 模糊滿意度法
3.3 多目標函數模糊資源最適化問題
3.4 多目標函數目標最適化問題
3.5 多目標函數模糊限制條件最適化問題
3.6 多目標函數全模糊最適化問題
3.7 演算範例
3.7.1 模糊資源
3.7.2 模糊目標
3.7.3 模糊限制條件
3.7.4 全模糊
4 批次程序之工廠設計
4.1 單目標批次工廠設計
4.2 單目標模糊最適化設計
4.2.1 模糊資源最適化設計
4.2.2 模糊目標最適化設計
4.2.3 模糊限制條件最適化設計
4.2.4 全模糊最適化設計
4.3 多目標批次工廠設計
4.4 多目標模糊最適化設計
4.4.1 模糊資源最適化設計
4.4.2 模糊目標最適化設計
4.4.3 模糊限制條件最適化設計
5 結論與未來展望
A 模糊環境下的決策
B 批次程序
B.1 單一產物批次程序
B.2 多種產物批次程序
B.3 平行設備

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