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系統識別號 U0002-1502201913192800
中文論文名稱 基於價值距離衡量之動態多準則決策方法及其應用
英文論文名稱 A dynamic multi-attributes decision making method based on value distance and its applications
校院名稱 淡江大學
系所名稱(中) 管理科學學系博士班
系所名稱(英) Doctoral Program, Department of Management Sciences
學年度 107
學期 1
出版年 108
研究生中文姓名 尹亮
研究生英文姓名 Liang Yin
學號 899620099
學位類別 博士
語文別 英文
口試日期 2019-01-16
論文頁數 108頁
口試委員 指導教授-徐煥智
共同指導教授-鄭啟斌
委員-郭人介
委員-伍台國
委員-范書愷
委員-黃承龍
委員-時序時
委員-徐煥智
委員-曹銳勤
中文關鍵字 多屬性決策  展望理論  動態決策  群體決策 
英文關鍵字 MADM  Prospect theory  Group decision  Dynamic decision making 
學科別分類
中文摘要 為取得最令人滿意之決策結果,決策者的風險態度而不僅僅是待選項目的效用價值應被納入考量中。本研究使用S形曲線價值函數來取代傳統多屬性決策方法中的期望效用函數以反映決策者的風險趨避和風險追求行為。在此基礎上,為了進一步減輕使用者在標量參考點上遇到的困難,本研究使用價值函數和權重加總方法來定義每個待選項目相對於極端可行解的心理價值距離以衡量它們的總體展望價值。該方法的效能在比較分析和敏感度分析中得到了驗證,證明其能夠幫助減少多屬性決策中的常見的問題例如排序顛倒問題.之後,該方法被擴展到群體決策領域,將多位決策者的偏好加總後得出公正的解決方案。實驗證明了該方法是適當且穩定的。最後,為了處理現實社會中存在的動態多階段決策場景.本研究將此方法發展為動態多階段決策方法並應用在一個挑選海量資料服務建構商的實際標案過程中,前一輪決策結果被以回饋機制帶入到下一輪的決策過程中。使用者接受了最終結果並認為該決策過程是易用且有幫助的。
英文摘要 To achieve the most satisfying decision results, not only the utility value of the alternatives but also the risk attitudes of the decision makers need to be considered. In this proposed model, the s-shape value function is adopted to replace the expected utility function that is often used in traditional MADM methods to reflect the risk-averse and risk-seeking behavior of decision makers. On top of that, to further reduce the user burden of identifying the reference points, the psychological value distance is defined to measure the overall prospect values of each alternative reference to extreme feasible solutions using the value function and the additive weighting method. Performance of the proposed algorithms is comparatively analyzed and sensitivity analysis is conducted, to prove that this mechanism can help reduce issues like rank reversal. After that, the method is extended to a group decision setting, and the preferences of multiple decision makers are aggregated to produce a fair result. The experiments show that it is an appropriate and robust MADM method. Finally, considering the real world dynamic decision making scenario, the model is further developed to be dynamic (can handle more than one rounds of decision making, as defined in another research of a dynamic multiple-criteria decision making framework) (Campanella and Ribeiro, 2011), and then was applied in a big data service provider selection bidding case and the results from previous decision making process were carried to the following round using a feedback mechanism. The users accepted the final results and were satisfied with the easy and helpful decision making process.
論文目次 CONTENTS
Chapter 1 Introduction 1
1.1 Research motivation 1
1.2 Research objectives 4
1.3 Research methodology 5
1.4 Research limitations 8
1.5 Dissertation structure 9
Chapter 2 Literature review 10
2.1 MADM 10
2.2 The Prospect Theory 13
2.3 The usage of the Prospect Theory in MADM 16
2.4 Dynamic decision process studies 19
Chapter 3 The proposed approach for static scenario 22
3.1 Election based on relative value distances 22
3.2 Election based on value distances 34
3.3 EBVD used in group decision scenario 38
Chapter 4 Evaluations and experiments 46
4.1 Evaluations for ERVD 46
4.2 Evaluations for EBVD 59
4.3 Comparison of ERVD and EBVD 70
Chapter 5 The proposed approach in dynamic scenarios 74
5.1 The framework of the dynamic EBVD 74
5.2 Detailed steps of the dynamic EBVD 77
Chapter 6 Illustrative example for dynamic scenario 83
6.1 Project background 83
6.2 Assessment procedures 86
6.3 The second round of the bidding process 89
Chapter 7 Conclusion and discussions 92
7.1 Advantages of the proposed methods 92
7.2 Scenarios to apply the proposed methods 95
7.3 Management implications and future studies 96
I. Appendix I: call center support system evaluation form – first round 98
II. Appendix II: call center support system evaluation form – second round 99
References 100


LIST OF TABLES

Table 2 1 Recent MADM methods based on prospect theory 19
Table 2 2 Dynamic MADM methods 21
Table 3 1 Decision matrix of human resource selection (Opricovic, 1998) 29
Table 3 2 Weights on criteria (Opricovic, 1998) 30
Table 3 3 Normalized decision matrix 31
Table 3 4 Normalized value based decision matrix 32
Table 3 5 The relative closeness and rank by ERVD 33
Table 3 6 Decision matrix defined by decision maker 1 (Shih et al., 2007) 42
Table 3 7 Separation measures 44
Table 3 8 The aggregated relative closeness and rank by group EBVD 45
Table 4 1 The decision matrix and results obtained by TOPSIS and ERVD of Experiment 1 48
Table 4 2 The decision matrix and results obtained by TOPSIS and ERVD of Experiment 2 50
Table 4 3 Results obtained by ERVD with parameter α and β varied 52
Table 4 4 Results obtained by ERVD with parameter λ varied 56
Table 4 5 Results obtained by ERVD with reference point of C2 varied 58
Table 4 6 The decision matrix results obtained by TOPSIS and EBVD of Example 1 60
Table 4 7 The final values and rank obtained by TODIM 62
Table 4 8 Decision matrix of Example 2 (García-Cascales and Lamata, 2012) 63
Table 4 9 Results obtained by TOPSIS and EBVD of Example 2 63
Table 4 10 Results obtained by TOPSIS and EBVD of Example 2 with the incorporation of a new alternative 64
Table 4 11 Results obtained by EBVD with parameter α = β varied 66
Table 4 12 Results obtained by EBVD with attenuation factor of the losses varied 70
Table 4 13 EBVD and ERVD comparison using 4.1 experiment 1 71
Table 4 14 Subsection 4.2 Example 2 original scenario 72
Table 4 15 Subsection 4.2 Example 2 add new alternative 72
Table 6 1 The sample call-in phone call counts for short (<10 seconds) and longer (10-50 seconds) calls 84
Table 6 2 Sample report 85
Table 6 3 Assessment criteria for the first round of decision-making 87
Table 6 4 The parameters for the first round decision 88
Table 6 5 The decision matrix for the first round 89
Table 6 6 Ranking results for the first round 89
Table 6 7 Parameters used in the second round of decision-making 91
Table 6 8 Decision matrix for the second round 91
Table 6 9 Results for the second round 91



LIST OF FIGURES
Figure 2 1 Value Function (Kahneman and Tversky, 1979) 14
Figure 4 1 Experiment on the range of α = β 51
Figure 4 2 Experiment on the range of α 53
Figure 4 3 Experiment on the range of β 54
Figure 4 4 Experiment on the range of λ (0.25 - 7) 55
Figure 4 5 Experiment on the range of λ (2.0 - 2.55) 57
Figure 4 6 The values according to various reference points. 59
Figure 4 7 Experiment on the range of α = β 65
Figure 4 8 Experiment on the range of α 67
Figure 4 9 Experiment on the range of β 68
Figure 4 10 Experiment on the range of λ (0.25 - 7) 69
Figure 5 1 The framework for a Dynamic EBVD 76
Figure 7 1 Different scenario to apply the corresponding proposed methods 95
Figure I 1 Call center support system evaluation form round 1 98
Figure II 1 Call center support system evaluation form round 2 99

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