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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 洪一薰 | zh_TW |
| dc.contributor.advisor | I-Hsuan Hong | en |
| dc.contributor.author | 羅迪納 | zh_TW |
| dc.contributor.author | Rudi Nurdiansyah | en |
| dc.date.accessioned | 2024-08-15T17:27:36Z | - |
| dc.date.available | 2024-08-16 | - |
| dc.date.copyright | 2024-08-15 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-08-10 | - |
| dc.identifier.citation | Abbassi, M., Chaabani, A., & Said, L. B. (2022). An efficient chemical reaction algorithm for multi-objective combinatorial bi-level optimization. Engineering Optimization, 54(4), 665-686.
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/94431 | - |
| dc.description.abstract | 在數位轉型時代，各行各業正利用先進的數據驅動優化技術來提高效率、降低成本並改善決策過程。這些技術結合了大數據、機器學習和人工智慧，通過創建模型和模擬來預測結果並提供最佳策略，徹底改變了傳統做法。然而，現實世界中的優化問題往往是複雜的、多目標的且資源密集型的，需要使用進化算法和基於模擬的優化等複雜的方法。替代模型等技術有助於減少計算成本，但會引入近似誤差。製造業、能源、海事和農業等行業從數據驅動優化中受益顯著，應對數據稀缺、噪聲和不平衡等挑戰。這些技術的框架包括數據收集、模型開發和計算，確保模型的穩健性和適應性。本研究探討了在製造排隊時間環生產系統、海洋渦輪模擬校準和海洋農場佈局優化中的應用，展示了性能和效率方面的顯著改進. | zh_TW |
| dc.description.abstract | In the digital transformation era, industries are leveraging advanced data-driven optimization techniques to enhance efficiency, reduce costs, and improve decision-making processes. These techniques integrate big data, machine learning, and artificial intelligence, revolutionizing traditional practices by creating models and simulations to predict outcomes and suggest optimal strategies. However, real-world optimization problems are often complex, multi-objective, and resource-intensive, requiring sophisticated approaches like evolutionary algorithms and simulation-based optimization. Techniques such as surrogate models help mitigate computational costs but introduce approximation errors. Industries like manufacturing, energy, maritime, and agriculture benefit significantly from data-driven optimization, addressing challenges like data scarcity, noise, and imbalance. A framework for these techniques involves data collection, model development, and computation, ensuring robust and adaptable models. This study explores applications in manufacturing queue time loop production systems, marine turbine simulation calibration, and marine farm layout optimization, demonstrating significant improvements in performance and efficiency. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-08-15T17:27:36Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2024-08-15T17:27:36Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | Acknowledgements ii
中文摘要 iii Abstract iv Table of Contents v List of Figures vii List of Tables ix Chapter 1 Introduction 1 Chapter 2 Real-time framework for admission control in queue-time loop production systems 7 2.1 Problem background 8 2.2 Mathematical Model 13 2.3 Solution approach 19 2.3.1 Combinatorial Benders’ cuts 19 2.3.2 Phase-step method 22 2.4 Numerical Study 30 2.5 Summaries 35 Chapter 3 Calibrating the turbulence characteristics using a surrogate model-based framework: A marine turbine simulation 36 3.1 Problem background 36 3.2 Framework and development 41 3.2.1 OpenFOAM model development 41 3.2.2 Model of vegetative canopy 42 3.2.3 Framework of surrogate model and its performance 43 3.3 Development of surrogate model 45 3.4 Optimization methods 46 3.4.1 PSO algorithm 47 3.4.2 GA 48 3.5 Results and discussion 50 3.6 Evaluating the quality of sample data 57 3.7 Summaries 59 Chapter 4 An optimization model for marine current turbines layout problem against parameters ambiguity 61 4.1 Problem background 61 4.2 BO-MCTIP model 65 4.2.1 The Analytical wake effects model 67 4.2.2 The Mathematical model for BO-MCTIP 71 4.3 Robust optimization model 75 4.4 Solution approach 76 4.4.1 Computational performance of the GHA 84 4.2.2 Robust GHA 88 4.5 Case Study 92 4.6 Summaries 104 Chapter 5 Conclusions 105 References 107 | - |
| dc.language.iso | en | - |
| dc.subject | 數據建模 | zh_TW |
| dc.subject | 優化算法 | zh_TW |
| dc.subject | 工業效率 | zh_TW |
| dc.subject | 數據驅動優化 | zh_TW |
| dc.subject | data-driven optimization | en |
| dc.subject | optimization algorithms | en |
| dc.subject | industrial efficiency | en |
| dc.subject | data modelling | en |
| dc.title | 資料驅動最佳化之產業應用 | zh_TW |
| dc.title | Industrial Application of Data-Driven Optimization Techniques | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 112-2 | - |
| dc.description.degree | 博士 | - |
| dc.contributor.coadvisor | 蘇哲平 | zh_TW |
| dc.contributor.coadvisor | Jack C.P. Su | en |
| dc.contributor.oralexamcommittee | Jakey Blue;Ying-Chao Hung;Kwei-Long Huang ;Cheng-Hung Wu;Wen-chih chen | zh_TW |
| dc.contributor.oralexamcommittee | Jakey Blue;Ying-Chao Hung;Kwei-Long Huang ;Cheng-Hung Wu;Wen-chih chen | en |
| dc.subject.keyword | 數據驅動優化,數據建模,工業效率,優化算法, | zh_TW |
| dc.subject.keyword | data-driven optimization,data modelling,industrial efficiency,optimization algorithms, | en |
| dc.relation.page | 125 | - |
| dc.identifier.doi | 10.6342/NTU202403713 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2024-08-13 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 工業工程學研究所 | - |
| 顯示於系所單位： | 工業工程學研究所 | |
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