臺灣博碩士論文加值系統

過去PROMETHEE於模糊環境的研究當中，有關語意的資料表示方式，皆藉由語意尺度(linguistic scale)轉換為三角模糊數(TFN)。不同於三角模糊數，區間值直覺模糊數的區間表示方式，更能完整的表現數值本身的特性。此外，使用區間值直覺模糊數亦能考量到一些不確定性因素。
本研究的資料表示方式以區間值直覺模糊數為主，並應用於PROMETHEE架構當中，建立一個較能應用於現實活中，並能表達不確定性的排序模型。首先，決策者需先評估方案在各準則下的表現，並藉由語意尺度轉換成區間值直覺模糊數。而各準則的權重則同樣藉由語意尺度對應出其屬於的區間值直覺模糊數值。而本文所採取的語意尺度皆為先前一些研究文獻當中所衡量的結果，並加以修改而成。PROMETHEE的主要精神即藉由方案間的兩兩比較，來進行方案排序上的演算。接著，利用偏好函數當中的閾值(threshold)對兩兩比較的方案給予參數。最後再加入權重，獲得到方案兩兩比較後的部分排序(PROMOTHEE I)以及完整排序(PROMOTHEE II)。
本研究中，各準則下的方案資料皆由決策者所評估。而在區間值直覺模糊的資料型態表示下，可較能貼切的表達決策者的觀點。為了能使得各方案做排序的動作，區間值直覺模糊概似函數(IVIF score function)是用來解決區間值直覺模糊數難以做比較的問題。
本研究例舉了兩個案例，皆能在區間值直覺模糊環境中做有效的應用。兩個案例皆分別對區間值直覺模糊權重以及區間值直覺模糊PROMETHEE的可行性得到了應證。因此，本研究證實區間值直覺模糊權重以及區間值直覺模糊PROMETHEE的結合運用是可行的。

In the past, research pertaining to the fuzzy preference ranking organization method for enrichment evaluation F-PROMETHEE, linguistic data has been chiefly transformed into triangular fuzzy numbers (TFNs). Different from a TFN, the interval-valued intuitionistic fuzzy (IVIF) number is able to more completely present its natural features. In addition, in specific contexts, we can also consider some uncertainty elements by using IVIF numbers.
This study expresses the input data as IVIF sets which are applied within the framework of PROMETHEE. First, decision makers (DMs) need to evaluate the alternatives with respect to each criterion; then, these alternatives are switched from linguistic numbers into IVIF numbers. The weights of the criteria are also expressed as IVIF sets switched from a linguistic scale. The linguistic scales used in our study are modified from earlier works in the literature. Notably, the main spirit of PROMETHEE involves deviations among the alternatives. The parameters are taken from a pair-wise comparison of alternatives corresponding to the thresholds of the preference function. After integrating the weights of the criteria, we can obtain the final partial outranking by PROMETHEE I and a complete outranking by PROMOTHEE II.
This study considers the opinions of the DMs, who provide data with respect to each alternative. With the use of IVIF linguistic scales, the intensities of the alternative weights can be described. In order to outrank the alternatives, an IVIF score function is used to avoid specific difficulties when comparing IVIF numbers.
This study exemplifies two cases which both can be applied within the IVIF environment. Both cases investigate IVIF-weight and IVIF-PROMETHEE, respectively. Therefore, this study confirms that the cooperation of IVIF-weight and IVIF-PROMETHEE is feasible in this context.

論文指導教授推薦書
論文口試委員審定書
長庚大學博碩士論文著作授權書 III
誌謝 IV
摘要 V
Abstract VI
Contents VIII
List of Tables X
List of Figures XI
Chapter I. Introduction 1
1.1 Background 1
1.2 Research Motivation 2
1.3 Research Purpose 3
1.4 Research Flow 4
Chapter II. Literature Review 5
2.1 The PROMETHEE Family 5
2.2 The Fuzzy PROMETHEE 8
2.3 The Applications of F-PROMETHEE 11
2.4 PROMETHEE I &; PROMETHEE II 19
2.5 The IVIF-PROMETHEE Method 20
Chapter Ⅲ. Methodology 23
3.1 Related Definitions of IVIF Set 23
3.2 Preference Function 25
3.3 The Proposed Method 28
3.4 Algorithm 31
Chapter Ⅳ. Illustrative example 34
4.1 An Application for a Manufacturing Company Finding Outsourcing Suppliers 34
4.2 A Case of Finding Outsourcing Construction for New Building 44
4.3 Discussion 50
Chapter V. Conclusions and Suggestions 52
5.1 Conclusions 52
5.2 Suggestions 53
References 55

List of Tables
Table 2.1 Application of Different PROMETHEE Methods 8
Table 2.2 The Application Area of F-PROMETHEE 17
Table 4.1 Linguistic Scale for Alternatives Evaluation 35
Table4.2 Alternatives Linguistic Evaluations by DMs 36
Table 4.3 Importance Weight as Linguistic Variables 36
Table 4.4 IVIF Set Decision Matrix (Example 1) 37
Table 4.5 Parameters for PROMETHEE Analysis 38
Table 4.6 Positive, Negative Preference Flows, and Net Flows (Example 1) 40
Table 4.7 Alternatives’ Leaving and Entering Flows (Example 1) 42
Table 4.8 The aggregated IVIF set matrix (Example 2) 45
Table 4.9 The Weights of Criteria (Example 2) 47
Table 4.10 Parameters for PROMETHEE Analysis (Example 2) 47
Table 4.11 Positive, Negative Preference Flows, and Net Flows (Example 2) 48
Table 4.12 Alternatives’ Leaving and Entering Flows (Example 2) 49
Table 4.13 Research Comparisons 51

List of Figures
Figure 1.1 Research Flow Chart 4
Figure 3.1 Types of Preference Functions P(d) 26
Figure 4.1 Partial Preorder—Value Outranking Graph (Example 1) 40
Figure 4.2 Complete Preorder—Value Outranking Graph (Example 1) 41
Figure 4.3 Partial Preorder—Value Outranking Graph (Example 2) 48
Figure 4.4 Complete Preorder—Value Outranking Graph (Example 2) 48

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