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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 吳政鴻 | |
dc.contributor.author | Chi-Kang Tsai | en |
dc.contributor.author | 蔡季剛 | zh_TW |
dc.date.accessioned | 2021-07-10T21:46:41Z | - |
dc.date.available | 2021-07-10T21:46:41Z | - |
dc.date.copyright | 2020-05-21 | |
dc.date.issued | 2020 | |
dc.date.submitted | 2020-04-28 | |
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/77101 | - |
dc.description.abstract | 本篇論文研究開放式列隊網路 (open Queueing networks)的指派問題。通過利用結合數個動態規劃結果,達到近似最佳生產系統控制模型,用以精進控制系統結果。動態規劃由於受制於維度詛咒及重建模型問題,在求解較大規模系統的最佳控制策略時往往會花費很長時間,而且每當新設置或製程出現就必須重新建構數學模型。然而,動態最佳控制策略存在著一定規律,零工問題本身也存在一些特性。若利用這些性質於方法之中,將小規模系統的最佳控制策略結合,用來預測大規模系統的最佳控制策略,將可以克服因為利用動態規劃求解最佳策略亦或是重新建模等所花費的時間成本。 | zh_TW |
dc.description.abstract | This study presents a dynamic approach method for Multiple stage job shop manufacturing systems. Due to the computational complexity and memory requirement, dynamic programming cannot efficiently find optimal control policies for realistic operating systems which usually contains large number of machines and productions. It often takes a long time to solve the optimal control strategy of a large-scale system, and the mathematical model must be reconstructed whenever a new setting or process occurs. However, with some properties of the problem we can find an easy way to cope with these problems. | en |
dc.description.provenance | Made available in DSpace on 2021-07-10T21:46:41Z (GMT). No. of bitstreams: 1 ntu-109-R06546050-1.pdf: 6772361 bytes, checksum: ac5cc3538fafc8229fa35144d2c9e12e (MD5) Previous issue date: 2020 | en |
dc.description.tableofcontents | 目 錄 圖目錄 III 表目錄 IV 第一章 緒論 1 1.1研究背景與動機 1 1.2 研究目的 4 1.3研究流程 5 第二章 文獻回顧 7 2.1排隊網絡(queueing network) 7 2.2 派工問題 (Dispatching problem ) 8 2.2.1基於優先規則方法(Priority rule-based method) 8 2.2.2啟發式方法(Heuristic approach) 9 2.2.3 動態控制方法(Dynamic Control method) 10 2.2.4 代理人算法(agent approach) 11 2.3 多智能體(Multi Agent) 11 2.4 小結 12 第三章 問題敘述與研究方法 13 3.1研究問題與假設 13 3.1.1研究網路模型 13 3.1.2 馬可夫決策問題歸一化技巧 14 3.1.3產能配置 15 3.2 分解排隊網路 16 3.2.1 Self-interested Agent 17 3.2.2整合agent決策 19 3.3 權重控制方法 22 3.4模型的通用性與小結 27 3.5小結 31 第四章 高維度問題權重搜索 32 4.1 PY-BOBYQA 33 4.2 與其他DFO方法比較 34 4.2.1 Nelder-Mead algorithm 35 4.2.2 Sequential model-based optimization 36 4.2.3比較結果 37 4.3 解法框架 37 4.4 數值結果 39 第五章 結論與未來研究方向 55 5.1 結論 55 5.2 未來研究方向 55 第六章 參考文獻 57 附錄 61 | |
dc.language.iso | zh-TW | |
dc.title | 生產系統之動態分解派工方法 | zh_TW |
dc.title | A Decomposition Method for Dynamic Job Shop Dispatching | en |
dc.type | Thesis | |
dc.date.schoolyear | 108-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 黃奎隆,藍俊宏 | |
dc.subject.keyword | 動態派工,生產網路分解,多代理協作, | zh_TW |
dc.subject.keyword | Dynamic dispatching,Manufacturing network decomposition,Multi-agent cooperation, | en |
dc.relation.page | 70 | |
dc.identifier.doi | 10.6342/NTU202000777 | |
dc.rights.note | 未授權 | |
dc.date.accepted | 2020-04-28 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 工業工程學研究所 | zh_TW |
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