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研究生:梁佳玲
研究生(外文):Jia-Ling Liang
論文名稱:模糊多準則決策分析方法在各種產業上的應用
論文名稱(外文):Fuzzy Multi-Criteria Decision Making Methods on Various Applications
指導教授:吳昭彥吳昭彥引用關係
指導教授(外文):Chao-Yen Wu
學位類別:博士
校院名稱:義守大學
系所名稱:資訊工程學系博士班
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2011
畢業學年度:99
語文別:英文
論文頁數:165
中文關鍵詞:理想解類似度順序偏好法模糊最佳化折衷排序法優勢排序結構評估法一致性模糊偏好關係不完全語意偏好關係分析層級程序法
外文關鍵詞:Fuzzy VlseKriterijumska Optimizacija I Kompromisno Resenje(Fuzzy VIKOR)Consistent Fuzzy Preference RelationsTechnique For Order Preference By Similarity To Ideal Solution(TOPSIS)Analytic Hierarchy Process(AHP)Incomplete Linguistic Preference RelationsPreference Ranking Organization METHod For Enrichment Evaluation(PROMETHEE)
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在日常生活中,我們常會遇到許多決策問題,而這些決策問題,很多具有複雜性及不確定性,造成在評估的過程中,很難權衡取捨,或僅能單靠個人的主觀判斷來決定,常常造成結果不如預期的情況,如何改善與精進決策過程,則必須由決策者與規劃者密切的配合,方能符合實際問題的需要。
因此,本研究針對多準則決策評估模式,探討多準則決策分析方法及模糊多準則決策分析方法在各種產業上的應用,如:分析層級程序法(Analytic Hierarchy Process, AHP)、理想解類似度順序偏好法(Technique for Order Preference by Similarity to Ideal Solution, TOPSIS)、優勢排序結構評估法(Preference Ranking Organization METHod for Enrichment Evaluation, PROMETHEE)、一致性模糊偏好關係(Consistent Fuzzy Preference Relations)、不完全語意偏好關係(Incomplete Linguistic Preference Relations)以及模糊最佳化折衷排序法(Fuzzy VlseKriterijumska Optimizacija I Kompromisno Resenje, Fuzzy VIKOR)等七種分析方法,在入口網站及醫療院所上的應用,佐以實證研究問卷資料加以分析及討論,並比較其差異與優缺點,以調和、折衷決策過程中的衝突與矛盾,提出客觀之評估建議,有效解決傳統決策方法對處理模糊問題的彈性欠缺、計算複雜與不一致性的問題,俾利各領域之廣泛應用,並作為後續學術研究之參考依據。
Decision or selection making is a vital part of daily life; of which the major concern is that almost all issues requiring decisions have multiple, often conflicting, criteria. In reality, there is no avoidance of the coexistence of qualitative and quantitative data, and the data are often full of complex, fuzziness and uncertainty. Due to this, it is difficult to make a proper selection or decision based on individual subjective judgment. With closer cooperation between the decision-makers and schemers, the actual demands of problems can be realized.
Therefore, the thesis proposes multi-criteria decision making methods and fuzzy multi-criteria decision making methods to evaluate decision problems in various applications. The methodologies in the thesis include Analytic Hierarchy Process (AHP), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE), Consistent Fuzzy Preference Relations, Incomplete Linguistic Preference Relations and Fuzzy VlseKriterijumska Optimizacija I Kompromisno Resenje (Fuzzy VIKOR). The methodologies have be analyzed and compared each other with two empirical cases in web portals and healthcare organizations and several authenticated examples.
These methodologies can properly mediate the conflicts and contradictions during the decision-making processes. The thesis proposes an objective evaluation suggestions to slove the traditional decision-making problems effectively, such as: the lack of flexibility, computational complexity and inconsistent. The results provide useful suggestions for both researchers and decision-makers in every fields.
TABLE OF CONTENTS
論文口試委員會審定書III
中文摘要V
ABSTRACT VII
誌謝IX
TABLE OF CONTENTS X
LIST OF TABLES XIV
LIST OF FIGURES XVII
CHAPTER ONE:INTRODUCTION 1
1.1 Background 1
1.2 Definition of Terms 6
1.3 Objective of the Thesis 7
1.4 Organization 7
CHAPTER TWO:LITERATURE REVIEW 10
2.1 Quality Consciousness 10
2.2 Service Quality 12
2.3 Electronic Commerce (EC) 14
2.4 Balanced Scorecard (BSC) 14
2.5 Fuzzy Set Theory 17
2.6 Consistent Fuzzy Preference Relations 20
CHAPTER THREE:METHODOLOGY 27
3.1 Analytic Hierarchy Process (AHP) 27
3.2 Consistent Fuzzy Preference Relations (Fuzzy PreRa) 32
3.3 Incomplete Linguistic Preference Relations 37
3.4 Fuzzy VIKOR 40
3.5 TOPSIS 47
3.6 ELECTRE I 49
3.7 PROMETHEE 55
CHAPTER FOUR:EMPIRICAL CASE ONE 64
4.1 The Scale of User Perceived Service Quality of Information Presentation of Web Portals 64
4.2 Determining the Priority Weights of Influential Factors 66
4.2.1 Linguistic variables 66
4.2.2 Prioritizing the influential factors 67
4.3 Questionnaire Design 70
4.4 Data Collection 71
4.5 Sample Profile 71
4.6 Data Analysis 73
4.7 Discussion 77
CHAPTER FIVE:EMPIRICAL CASE TWO 79
5.1 Case Study 79
5.2 Data Analysis and Results 80
CHAPTER SIX:AUTHENTICATED EXAMPLE ONE 89
6.1 Fuzzy VIKOR Model 89
6.2 A Numerical Example 91
CHAPTER SEVEN:AUTHENTICATED EXAMPLE TWO 98
7.1 Incomplete Linguistic Preference Relations 98
7.2 Compared Incomplete Linguistic Preference Relations and Fuzzy Preference Relation 104
CHAPTER EIGHT:AUTHENTICATED EXAMPLE THREE 108
8.1 Illustration Example 108
8.2 A Comparative Analysis of VIKOR and TOPSIS 112
CHAPTER NINE:CONCLUSIONS AND CONTRIBUTIONS 116
9.1 Conclusions 116
9.2 Contributions 118
9.3 Future Works 119
BIBLIOGRAPHY 121
APPENDIX:QUESTIONNAIRE 141
PUBLICATION 147
LIST OF TABLES
Table 2.1. Major service quality dimensions 13
Table 3.1. Linguistic variables for the weight of criteria 41
Table 3.2. Linguistic variables for the rating of alternative 42
Table 3.3. The level of four criteria in this example 52
Table 3.4. The results after assessment 52
Table 4.1. The scale of user perceived service quality of information presenting 65
Table 4.2. Linguistic terms for priority weights of influential factors 67
Table 4.3. Demographics 72
Table 4.4. Five-dimensional transformed fuzzy preference values 74
Table 4.5. Preference values transformed by linear solution (five-dimensional) 75
Table 4.6. Normalized matrix of priority weight and rank of influential factors (five-dimensional) 75
Table 4.7. The whole rank of the perceived service quality of information-presenting web portals (index by dimensions) 76
Table 4.8. The whole rank of the perceived service quality of information-presenting web portals (index by rank) 78
Table 5.1. The balanced scorecards of healthcare organizations 81
Table 5.2. The original data of the pairwise comparison matrices with respect to the criteria 82
Table 5.3. The original data of the pairwise comparison matrices with respect to the PS criteria 82
Table 5.4. The original data of the pairwise comparison matrices with respect to the CUO criteria 83
Table 5.5. The original data of the pairwise comparison matrices with respect to the FPC criteria 83
Table 5.6. The original data of the pairwise comparison matrices with respect to the SIC criteria 83
Table 5.7. Preference relation matrix for pairwise comparison of criteria (Fuzzy PreRa) 84
Table 5.8. The weights and ranking of each dimension 88
Table 5. 9. The weights of each attribute 88
Table 6.1. The importance weight of the criteria 93
Table 6.2. The rating of candidates under five criteria 93
Table 6.3. Aggregated Fuzzy Weight of Criteria 94
Table 6.4. Aggregated Fuzzy Decision Matrix 94
Table 6.5. Fuzzy Best Value(FBV) and Fuzzy Worst Value(FWV) 95
Table 6.6. Index (S_i)~ and (R_i)~ 95
Table 6.7. (S^*)~, (S^-)~, (R^*)~ and (R^-)~ 96
Table 6.8. Index (Q_i)~, Q_i and rank for candidates 96
Table 6.9. Acceptable stability in decision making 97
Table 7.1. Linguistic variables for the preference relations 103
Table 7.2. Comparison table 104
Table 8.1 Multicriteria selection for a restaurant location in Taipei 108
Table 8.2 Normalized decision matrix 109
Table 8.3 Weighted normalized decision matrix 110
LIST OF FIGURES
Figure 3.1. The membership degree of fuzzy numbers in the weight of criteria 42
Figure 3.2. The membership degree of fuzzy numbers in the rating of alternative 42
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