臺灣博碩士論文加值系統

隨著高科技進展、資訊化時代的來臨，現代化的戰爭型態亦日新月異不斷改變，高科技武器設計漸趨精密，造價也日趨昂貴，以往藉由實兵實彈進行訓練的方式，不但成本高且易產生人員傷亡。因此，許多國家的軍事單位也逐漸以訓練模擬系統取代部分實兵演訓，達成戰備訓練之目標。然而，雖然已有相當多類型的訓練模擬系統應用在軍事環境，但各式模擬器的訓練績效卻從未完整評估，再加上各訓練模擬系統建置時間、功能、環境與實際操作人員素質等變動因素，以及部分資訊的不完整，都將增加訓練模擬系統績效評估之困難。為解決上述問題，避免各項模擬訓練系統使用率過低或閒置，造成訓練資源浪費，本研究整合層級分析法、軟集合、模糊語意模型及重要績效度分析進行訓練模擬系統整體績效評估，並運用重要績效度分析之概念，同時考量訓練模擬系統所節省之成本與訓練安全之影響，期望藉由此研究發現促進訓練模擬系統使用效益，避免浪費資源，並為後續應用和分析提供數據，為了驗證本研究提出的方法，採用訓練模擬系統績效評估的數值範例，並與AHP及novel-AHP排序技術進行數值結果的比較。結果證實，本研究提出的方法不僅可以充分考慮專家提供的問卷資訊，降低訓練模擬系統績效排序的重複率，並且透過二維圖示提供管理者作為訓練模擬系統有限資源分配的決策參考，進而提高整體投資效益和訓練成效。

The advancement of high technologies and the arrival of the information age have caused changes to the modern warfare; more technologically exquisite weapons are designed, increasing in cost and complexity. Training maneuvers with live ammunition are expensive and are prone to accidental casualties. Thus, the military forces of many countries have replaced partially real training drills with training simulation systems to achieve combat readiness. Although considerable types of training simulation systems are used in military settings, the training effectiveness has never been evaluated completely. In addition, differences in system set up time, functions, the environment, and the competency of system operators, as well as incomplete information have made it difficult to evaluate the performance of training simulation systems. To address the aforementioned problems, this study integrated analytic hierarchy process, soft set, and the fuzzy linguistic representation model to evaluate the performance of various training simulation systems. Furthermore, importance–performance analysis was adopted to examine the influence of saving costs and training safety of training simulation systems. The findings of this study are expected to facilitate applying military training simulation systems, avoiding wasting of resources (e.g., low utility and idle time), and providing data for subsequent applications and analysis. To verify the method proposed in this study, the numerical examples of the performance evaluation of training simulation systems were adopted and compared with the numerical results of an AHP and a novel AHP-based ranking technique. The results verified that not only could expert-provided questionnaire information be fully considered to lower the repetition rate of performance ranking, but a two-dimensional graph could also be used to help managers allocate limited resources, thereby enhancing the investment benefits and training effectiveness of a training simulation system.

Contents
摘要 ii
ABSTRACT iv
誌謝 vi
List of Tables x
List of Figures xi
Chapter 1 Introduction 1
1.1 Research background and motivation 1
1.2 Research objectives 5
1.3 Thesis organization 6
Chapter 2 Literature review 7
2.1 Analytical hierarchy process 7
2.1.1 Outline of AHP 7
2.1.2 Operations procedure of AHP 10
2.2 Important-performance analysis method 13
2.2.1 Outline of IPA 13
2.2.2 Determination of IPA 15
2.3 2-tuple fuzzy linguistic representation model 16
2.3.1 Outline of 2-tuple fuzzy linguistic representation model 16
2.3.2 The procedure of 2-tuple fuzzy linguistic representation model 18
2.4 Soft set 20
2.4.1 Outline of soft set 20
2.4.2 Determination of soft set 21
Chapter 3 Proposed 2-tuple AHP-based ranking technique 23
3.1 The reason for using the 2-tuple AHP, soft set, and IPA 23
3.2 The procedure of the proposed approach 25
Chapter 4 Numerical verification 31
4.1 Overview 31
4.2 Solution based on the traditional AHP method 40
4.2.1 Weighting calculation 40
4.2.2 Simulator benefit analysis 42
4.3 Solution based on the novel AHP-based ranking technique by Chang, et al. 44
4.3.1 Weighting calculation 44
4.3.2 Obtaining the performance evaluation scores 44
4.3.3 Simulator performance analysis 47
4.4 Solution based on the proposed method 49
4.4.1 Solving procedure 49
4.4.2 Research results analysis 57
4.5 Comparisons and discussion 58
4.5.1 Comparison of the main differences in special attributes among the research methods 58
4.5.2 Comparison of the results of the research methods used in this study 59
Chapter 5 Conclusions and further direction 64
5.1 Conclusions 64
5.2 Contributions 65
5.3 Further direction 66
References 67

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