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研究生:Amirhossein Nafei
研究生(外文):AMIRHOSSEIN NAFEI
論文名稱:利用中智集決策方法提高工廠效率和績效
論文名稱(外文):Enhancing Efficiency and Performance in Industrial Factories Utilizing Neutrosophic Decision-Making Methods
指導教授:黃乾怡黃乾怡引用關係
指導教授(外文):HUANG, CHIEN-YI
口試委員:黃乾怡應國卿吳舜堂梁書豪張志平
口試委員(外文):HUANG, CHIEN-YIYING, GUO-QINGWU, SHUN-TANGLIANG, SHU-HAOZHANG, ZHI-PING
口試日期:2024-04-08
學位類別:博士
校院名稱:國立臺北科技大學
系所名稱:工業工程與管理系
學門:工程學門
學類:工業工程學類
論文種類:學術論文
論文出版年:2024
畢業學年度:112
語文別:英文
論文頁數:106
中文關鍵詞:決策中智集機器選擇供應商選擇工業工廠TOPSIS專制多屬性決策方法
外文關鍵詞:Decision-makingNeutrosophic setsMachine selectionSupplier selectionIndustrial factoryTOPSISAutocratic methodsMADM
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決策有效性在個人與專業領域層面至關重要,這需要一種系統性的方法來應對複雜的情境。本研究深入探討多屬性決策方法(Multi-Attribute Decision-Making,MADM),MADM強調其對於提高不同領域決策過程的透明度、效率和彈性的重要性。透過對 MADM 的全面探索,本研究強調了MADM在提供結構化框架來處理模糊性、不確定性和多重標準方面的優勢,進而使決策者能夠做出更明智和更合理的選擇。此外,本研究以應對決策參數中存在的固有複雜性和不確定性,特別是在多屬性群體決策(Multi-Attribute Group Decision Making,MAGDM)的背景下探討中智集(Neutrosophic Sets, NS)和中智模糊集(Neutrosophic Fuzzy Sets, NFS)。通過引用NS和NFS理論,本研究旨在為決策者提供更強大的工具,藉以有效管理不確定性、不一致性和模糊性,從而為複雜情境中更強大且適應性更強的決策結果做出貢獻。
除了探索理論架構之外,本研究也介紹了實用的決策策略,包括專制方法和基於相似性到理想解的排序技術(Technique for Order Performance by Similarity to Ideal Solution, TOPSIS)。這些方法的使用目的在優化決策結果,特別是在工業環境下的供應商和機器選擇情境中。專制策略提供了一個結構化的決策框架,利用NS原則來有效處理不確定性並簡化決策過程。同時,TOPSIS提供了一種系統性的方法,可以根據與理想解和反理想解的接近程度對備選方案進行優先排序,進而幫助決策者取得最佳選擇。這些減少計算複雜性的方法之一,值得注意的特點是將來自不同管理觀點的決策和權重矩陣整合到統一的綜合評估矩陣中。儘管採用了旨在應對涉及團體建議的決策挑戰以及使用 NSs 和 NFSs 的策略,但分析結果表明,所提出的方法展現了令人滿意的計算簡單性水平。

Effective decision-making is crucial in personal and professional domains, requiring a systematic approach to navigating complex scenarios. This research delves into the Multi-Attribute Decision-Making (MADM) methodology, emphasizing its significance in enhancing transparency, efficiency, and resilience in decision-making processes across diverse fields. Through a comprehensive exploration of MADM, the study underscores their advantages in providing a structured framework for handling ambiguity, uncertainty, and multiple criteria, thereby enabling decision-makers to make more informed and defensible choices. Furthermore, the research extends its inquiry into the realm of Neutrosophic Sets (NS) and Neutrosophic Fuzzy Sets (NFS) to address the inherent indeterminacies and uncertainties present in decision parameters, especially in the context of Multi-Attribute Group Decision Making (MAGDM). By introducing NS and NFS theories, the study aims to provide decision-makers with enhanced tools to effectively manage indeterminacy, inconsistency, and uncertainty, thereby contributing to more robust and adaptive decision outcomes in complex scenarios.
In addition to exploring theoretical frameworks, the research introduces practical decision-making strategies, including an autocratic method and the Technique for Order Performance by Similarity to Ideal Solution (TOPSIS). These methodologies are tailored to optimize decision outcomes, particularly in supplier and machine selection scenarios within industrial factories. The autocratic strategy offers a structured framework for decision-making, leveraging NS principles to handle uncertainties and streamline the decision process effectively. Meanwhile, TOPSIS provides a systematic approach for prioritizing alternatives based on their proximity to ideal and anti-ideal solutions, thus aiding decision-makers in selecting optimal choices. One notable characteristic of these approaches to reduce computation complexity is integrating decision and weight matrices from various management viewpoints into a unified aggregated assessment matrix. Despite strategies aimed at addressing decision-making challenges involving group suggestions and using NSs and NFSs, the analytical results indicate that the proposed approaches demonstrate a desirable level of computational simplicity.

摘要 i
ABSTRACT iii
Acknowledgments v
Table of Contents vi
List of Tables x
List of Figures xi
Chapter 1 Introduction 1
1.1 Insights from MCDM and MADM Methodologies 1
1.1.1 Advantages of MCDM and MADM 2
1.1.2 Applications in Diverse Fields 2
1.1.3 Challenges and Considerations 3
1.2 Motivation for Using NSs in Decision-Making 3
1.3 The Motivation for Using Neutrosophic Fuzzy Sets 4
1.4 The Motivation for Using an Autocratic Strategy 6
1.5 The Motivation for Using TOPSIS 7
1.6 Enhancing Efficiency by Strategic Supplier and Machine Selection 9
1.7 The Objectives of This Study 12
1.8 The Contributions of This Study 13
Chapter 2 Literature Review and Preliminaries 16
2.1 Literature Review 16
2.2 Essential Concepts in Neutrosophic Theory 20
Chapter 3 The Proposed Strategy for Autocratic Decision-Making 24
3.1 Autocratic Decision-Making under Neutrosophic Environment 25
3.2 Example: A Case Study for Supplier Selection 28
3.3 Sensitivity Analysis 31
3.4 Enhancing Efficiency through Supplier Selection 36
3.4.1 Contextual Significance of Supplier Selection 37
3.4.2 Quality Assurance and Product Excellence 37
3.4.3 Timely Delivery and Production Schedules 37
3.4.4 Cost Optimization and Financial Efficiency 38
3.4.5 Competitiveness in the Market 38
Chapter 4 Advanced Techniques for MAGDM with Neutrosophic Fuzzy Sets 40
4.1 Score Function and Similarity Measure 41
4.2 Suggested Score Function 42
4.3 Suggested Distance and Similarity Measures 43
4.4 The Significance of the Proposed Distance Measure 48
4.5 Decision-Making Based on SVNFSs 49
4.5.1 The Proposed Autocratic Method 50
4.5.2 The Proposed TOPSIS Method 53
4.6 Application Example 57
4.6.1 Autocratic Method 59
4.6.2 TOPSIS Method 62
4.7 Sensitivity Analysis 70
4.8 Evaluation Processes 73
4.8.1 Comparative Analysis 74
4.8.2 Complexity Analysis 75
4.9 Elevating Performance by Strategic Machine Selection 79
4.9.1 Precision in Production: The Electronic Parts Factory 79
4.9.2 Automated Excellence: The Automotive Manufacturing Plant 79
4.9.3 Strategic Impact on Factory Performance 80
Chapter 5 Enhancing Performance through Decision-Making in Industrial Factories 81
5.1 Enhancing Efficiency through Supplier Selection 82
5.1.1 Contextual Significance of Supplier Selection 84
5.1.2 Quality Assurance and Product Excellence 84
5.1.3 Timely Delivery and Production Schedules 85
5.1.4 Cost Optimization and Financial Efficiency 86
5.1.5 Competitiveness in the Market 87
5.2 Elevating Efficiency by Strategic Machine Selection 88
5.2.1 Precision in Production: The Electronic Parts Factory 90
5.2.2 The Automotive Manufacturing Plant 91
5.3 Strategic Impact on Factory Performance 92
Chapter 6 Conclusion 94
References 97
Appendix 103
List of Symbols 106


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