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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 吳政鴻 | zh_TW |
| dc.contributor.advisor | Cheng-Hung Wu | en |
| dc.contributor.author | 楊培愉 | zh_TW |
| dc.contributor.author | Pei-Yu Yang | en |
| dc.date.accessioned | 2024-12-24T16:17:43Z | - |
| dc.date.available | 2024-12-25 | - |
| dc.date.copyright | 2024-12-24 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-12-09 | - |
| dc.identifier.citation | Afzalirad, M., & Rezaeian, J. (2016). Resource-constrained unrelated parallel machine scheduling problem with sequence dependent setup times, precedence constraints and machine eligibility restrictions. Computers & Industrial Engineering, 98, 40-52.
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/96311 | - |
| dc.description.abstract | 本研究旨在針對少量多樣生產環境下的非等效平行機台排程問題，建立一加工時間分位數預測模型，以及基於加工時間分位數的隨機性排程模型。產品於機台上的加工時間為排程問題的重要參數，除了其預測值的精準度會影響排程效能，了解加工時間的不確定性亦為決策的關鍵要素。然而，由於此生產模式具有產品多樣性高、生產批量小，以及加工時間不確定性高等特性，導致1.歷史數據觀察數不足、2.產品機台組合更複雜、3.加工時間無一固定的機率分佈，以上皆為少量多樣生產系統難以有效預測加工時間的因素，而傳統以加工時間期望值為基礎的排程方法亦無法有效捕捉生產過程的變異，使排程決策的優化更加艱難。為了克服這些問題，本研究利用集成學習方法預測不同產品機台組合的加工時間分位數，並將其納入排程最佳化模型中，提出加權分位數排程方法。與傳統的期望值方法相比，分位數方法能更全面地反映加工時間的分布範圍，有助於在不確定性高的環境下提升排程決策的穩健性。透過多次模擬實驗，本研究驗證了分位數排程方法在最小化最大完工時間上的優勢，也探討其在機台平均完工時間與產品平均完工時間的表現，並分析不同分位數權重配置對排程結果的影響。本研究不僅突破了加工時間需假設為特定機率分布的限制，也提供了一種提升生產系統穩健性的隨機性排程方法，對於動態生產環境下的決策應用具有潛在貢獻。 | zh_TW |
| dc.description.abstract | This study aims to address the unrelated parallel machine scheduling problem in a high-mix, low-volume (HMLV) production environment by developing a processing time quantile prediction model and a stochastic scheduling model based on processing time quantiles. Processing time on machines is a crucial parameter for scheduling problems, as both its prediction accuracy and consideration of its uncertainty are key factors in decision-making. However, due to the high diversity of products, small batch sizes, and significant processing time variability in HMLV production, effective processing time prediction is hindered by factors such as (1) insufficient historical data, (2) complex product-machine combinations, and (3) a lack of a fixed probability distribution for processing times. As a result, scheduling methods based on expected processing times fail to capture uncertain factors in production systems appropriately, making scheduling optimization more challenging. To address these issues, this study employs ensemble learning methods to predict processing time quantiles for various product-machine combinations, incorporating these quantiles into the scheduling optimization model and proposing a weighted quantile scheduling approach. Compared to traditional expected value methods, the quantile-based approach offers a more comprehensive reflection of processing time distribution, improving the robustness of scheduling decisions in highly uncertain environments. Through multiple simulation experiments, this study validates the advantages of the quantile scheduling method in minimizing makespan and explores its performance in terms of average machine completion time and average product completion time. The effects of different quantile weighting configurations on scheduling outcomes are also analyzed. This study not only breaks the assumption of specific probability distributions for processing times but also presents a stochastic scheduling approach that enhances the robustness of production systems, demonstrating potential contributions to decision-making in dynamic production environments. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-12-24T16:17:43Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2024-12-24T16:17:43Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 謝辭 i
中文摘要 ii Abstract iii 目次 v 圖次 viii 表次 ix 第一章 緒論 1 1.1 研究背景與動機 1 1.1.1 少量多樣化生產的成因與挑戰 1 1.1.2 工廠作業生產排程之不確定性 2 1.1.3 加工時間分佈之不確定性與複雜性 3 1.1.4 研究背景與動機小結 4 1.2 研究目的 4 1.2.1 突破加工時間須為特定機率分佈假設的限制 4 1.2.2 提升加工時間預測的準確性 5 1.3 研究方法與流程 5 第二章 文獻回顧與探討 9 2.1 少量多樣化生產模式 9 2.2 生產排程問題的分類 9 2.3 非等效平行機台之排程 11 2.3.1 確定性排程問題 12 2.3.2 隨機性排程問題 13 2.4 加工時間不確定性之量化 14 2.5 分位數迴歸 15 2.6 分位數迴歸於不確定性問題之應用 16 2.7 集成學習預測方法 17 2.7.1 提升方法(Boosting) 18 2.7.2 引導聚集法(Bagging) 18 2.7.3 堆疊法(Stacking) 19 2.7.4 XGBoost演算法 19 2.8 文獻回顧小結 21 第三章 加工時間預測模型之建構與驗證 22 3.1資料集描述 22 3.1.1訓練資料集與測試資料集概述 22 3.1.2資料集定義 23 3.2建立XGBoost預測模型 24 3.2.1 XGBoost期望值預測模型之訓練 24 3.2.2 XGBoost期望值預測模型之超參數調整 25 3.2.3 XGBoost分位數預測模型之訓練 27 3.2.4 XGBoost分位數預測模型之超參數調整 28 3.3 XGBoost預測模型之驗證 28 3.3.1 虛擬產品機台組合之加工時間統計真實值 29 3.3.2 XGBoost期望值預測模型之驗證 29 3.3.3 XGBoost分位數預測模型之驗證 32 第四章 非等效平行機台之排程 36 4.1 研究問題描述與假設 36 4.1.1 研究問題描述 36 4.1.2 研究問題假設 36 4.2 排程最佳化數學模型建構與符號定義 37 4.2.1加權分位數排程方法之最佳化模型 38 4.2.2期望值排程方法之最佳化模型 39 4.3 排程結果之驗證指標 40 4.3.1 正規化前之驗證指標 40 4.3.2 正規化後之驗證指標 43 第五章 虛擬生產系統與模擬生產資料集之建構 45 5.1 建置虛擬生產系統 45 5.1.1虛擬產品與虛擬機台之特徵介紹 46 5.1.2虛擬生產系統介紹 52 5.1.3真值模型之建立 53 5.2 歷史生產模擬資料 55 5.2.1 歷史生產模擬資料之屬性 56 5.2.2 歷史生產模擬資料之生成個數 57 5.2.3 虛擬產品機台組合之加工時間與資料觀察筆數分布 58 第六章 數值分析與驗證 60 6.1 實驗設計 60 6.2 二十產品與六非等效平行機台之生產系統案例 63 6.3 不同產品機台數量之生產系統案例 67 6.3.1 50%分位數排程之實驗結果分析 67 6.3.2 不同分位數權重之實驗結果分析 70 第七章 結論與未來研究方向 93 7.1 結論 93 7.2 未來研究方向 94 參考文獻 96 附錄 101 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 集成學習 | zh_TW |
| dc.subject | 分位數排程 | zh_TW |
| dc.subject | 加工時間分位數 | zh_TW |
| dc.subject | 加工時間不確定性 | zh_TW |
| dc.subject | 少量多樣生產系統 | zh_TW |
| dc.subject | quantile scheduling | en |
| dc.subject | high-mix low-volume production | en |
| dc.subject | uncertainty of processing time | en |
| dc.subject | processing time quantile | en |
| dc.subject | ensemble learning | en |
| dc.title | 考量加工時間不確定之加權分位數排程方法初探 | zh_TW |
| dc.title | Preliminary Study of Weighted Quantile Scheduling under Processing Time Uncertainties | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-1 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 黃奎隆;藍俊宏;陳文智 | zh_TW |
| dc.contributor.oralexamcommittee | Kwei-Long Huang;Jakey Blue;Wen-Chih Chen | en |
| dc.subject.keyword | 少量多樣生產系統,加工時間不確定性,加工時間分位數,集成學習,分位數排程, | zh_TW |
| dc.subject.keyword | high-mix low-volume production,uncertainty of processing time,processing time quantile,ensemble learning,quantile scheduling, | en |
| dc.relation.page | 127 | - |
| dc.identifier.doi | 10.6342/NTU202404688 | - |
| dc.rights.note | 未授權 | - |
| dc.date.accepted | 2024-12-10 | - |
| 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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