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DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 蔡曜陽 | zh_TW |
dc.contributor.advisor | Yao-Yang Tsai | en |
dc.contributor.author | 洪嘉宏 | zh_TW |
dc.contributor.author | CHIA-HUNG HUNG | en |
dc.date.accessioned | 2023-09-22T16:24:43Z | - |
dc.date.available | 2023-11-09 | - |
dc.date.copyright | 2023-09-22 | - |
dc.date.issued | 2023 | - |
dc.date.submitted | 2023-08-11 | - |
dc.identifier.citation | 參考文獻
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/89855 | - |
dc.description.abstract | 隨著電腦工藝規劃與演算法進步,學界開始使用各種演算法對各種議題進行研究,對各種目標進行最佳化,如:使用NSGA-II對柔性工藝路線優化、蒙地卡羅樹狀搜尋對多目標彈性零工式工廠排程最佳化……。查到的文獻中演算法在排程領域上多利用單任務多目標最佳化遺傳演算法或多任務單目標最佳化遺傳演算法,並且於限制條件上較為理想,而多目標多任務最佳化遺傳演算法以及改善演算法限制條件貼近實際工廠運作規則少人研究。
近期環境工廠增加以「客製化」方式提高利潤,客製化為相似工件或製程於同一廠區內進行生產,因為相似工件之工件工序不同,每種客製化訂單之排程問題視為獨立多目標最佳化問題,又因為客製化工件排程問題因工件相似,工序排程背後明顯相互關連,因此可將多個客製化訂單視為一多目標多任務最佳化問題,藉由不同任務間知識轉移增加演算法搜索效率達到更有效地排程。此外,碳排放議題日益受關注,因此使用多目標多任務最佳化遺傳演算法於實際工廠的運作提供排程之建議,預期提升實際工廠作業效率、減少總加工時間以降低碳排放量達到減碳目的。 綜合上述問題,本研究將發展一套排程系統,根據現實工件工程圖與真實加工參數使用HyperMill產生CAD/CAM,匯出加工參數與工序加工時間與加工功率,利用Excel的VBA巨集判斷與調整加工參數、時間與功率,輸出符合實際加工機台之數據,最後利用MATLAB經由符合真實情形之遺傳演算法,提出該情況下的最適排程方式,並得出預期加工時間與碳排放。 模擬結果顯示,本研究所發展的排程系統,可針對真實加工機台與加工工件進行可加工與不可加工之判斷並進行加工參數調整,達到增加可加工機台選擇之可能性與增加實際可加工之效率。並藉由符合真實情形之遺傳演算法獲得最適之加工排程結果與最小化總體加工時間與最小化碳排放。 | zh_TW |
dc.description.abstract | With the advancement of computer process planning and algorithms, the academic community has begun to utilize various algorithms to research diverse issues and optimize various goals. For instance, using NSGA-II for flexible process route optimization, Monte Carlo tree search for multi-objective flexible job shop scheduling optimization, etc. The literature often utilizes single-task multi-objective optimization genetic algorithms or multi-task single-objective optimization genetic algorithms in the field of scheduling. These algorithms tend to work well under ideal constraints, but there's limited research on multi-objective multi-task optimization genetic algorithms and the enhancement of algorithmic constraints closer to the actual operation rules of factories.
Recently, "customization" has been employed in factories to boost profits. Customization involves the production of similar workpieces or processes within the same factory area. As the processing order of similar workpieces differs, each customized order's scheduling problem is treated as an independent multi-objective optimization problem. Due to the similarity in the workpieces, the sequencing behind the operations is significantly correlated. Therefore, multiple customized orders can be considered as a multi-objective multi-task optimization problem. Knowledge transfer between different tasks can enhance the efficiency of the algorithm search, achieving more effective scheduling. In recent years, carbon emissions have increasingly drawn attention. Therefore, utilizing multi-objective multi-task optimization genetic algorithms can provide scheduling suggestions based on actual factory operation rules. It is expected to improve the operational efficiency of actual factories and reduce the total processing time, thereby reducing carbon emissions and achieving carbon reduction. In summary, this study will develop a scheduling system that uses HyperMill to generate CAD/CAM based on real workpiece engineering drawings and real processing parameters. The system will export processing parameters, processing time, and processing power, use Excel's VBA macros to judge and adjust these parameters, and output data that match the actual machining equipment. MATLAB, equipped with a genetic algorithm that aligns with real-world scenarios, will then be used to propose the most suitable scheduling method under these conditions and predict processing time and carbon emissions. Validation results show that the scheduling system developed in this study can make feasible and unfeasible judgments for real machining equipment and workpieces and make parameter adjustments, thereby increasing the possibility of choosing more machines and increasing the actual machining efficiency. Moreover, it can derive the optimal machining scheduling results and minimize total machining time and carbon emissions through a genetic algorithm that aligns with real-world scenarios. | en |
dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-09-22T16:24:43Z No. of bitstreams: 0 | en |
dc.description.provenance | Made available in DSpace on 2023-09-22T16:24:43Z (GMT). No. of bitstreams: 0 | en |
dc.description.tableofcontents | 目錄
誌謝 i 摘要 ii Abstract iii 目錄 v 圖目錄 ix 表目錄 xiii 符號說明 xiv 第1章 緒論 1 1.1 前言 1 1.2 文獻回顧 3 1.2.1 客製化相關研究 3 1.2.2 電腦輔助工藝規劃相關研究 5 1.2.3 排程與演算法相關研究 9 1.2.4 多目標多任務最佳化遺傳演算法相關研究 10 1.3 研究動機 11 1.4 研究目的 12 1.5 論文架構 13 第2章 基礎理論 14 2.1 切削原理 14 2.2 排程原理 17 2.2.1 以工件到達及作業特性區分排程方式 17 2.2.2 依機器數目及途程型態區分排程方式 18 2.3 遺傳演算法 20 2.4 多目標最佳化問題 25 2.4.1 非支配排序遺傳演算法 II 26 2.5 多任務最佳化問題 27 2.5.1 多任務遺傳演算法 27 2.5.2 多任務遺傳演算法 II 28 2.6 多目標多任務最佳化問題 30 2.6.1 多目標多任務遺傳演算法 30 2.6.2 多目標多任務演算法II 31 2.7 演算法小結 32 2.8 碳排放原理 33 2.8.1 盤查邊界 33 2.8.2 基準年 33 2.8.3 溫室氣體排放計算 33 第3章 模擬實驗設備與規劃 36 3.1 實驗設備 36 3.2 模擬實驗規劃 36 第4章 參數讀取 38 4.1 CAM加工參數 38 4.2 場域工具機參數列表 38 第5章 數據篩選與調整 40 5.1 表格設計 40 5.1.1 訂單工件列表 40 5.1.2 功率調整參數表 41 5.1.3 時間調整參數表 43 5.2 VBA巨集設計 44 5.2.1 篩選調整參數機制 44 5.2.2 工序流程約束矩陣 45 第6章 染色體設計 47 第7章 演算法設計 51 7.1 初始族群 52 7.2 RMP矩陣 52 7.3 交配與突變 52 7.4 修復函數設計 53 7.5 懲罰函數設計 53 7.5.1 減少相同工件工序時間距離 54 7.5.2 減少相鄰工序換機次數 54 7.6 目標函數設計 55 7.6.1 最小化總加工時間 55 7.6.2 最小化總二氧化碳排放量 56 7.7 適應度計算 57 7.7.1 間接映射 57 7.7.2 數據處理 58 7.8 選擇 58 第8章 模擬實驗結果與討論 60 8.1 模擬實驗數據輸入 63 8.2 模擬實驗結果與討論 64 8.2.1 甘特圖說明 64 8.2.2 第一次模擬實驗 66 8.2.3 增加工具機可選擇數量 75 8.2.4 第二次模擬實驗 76 8.2.5 第三次模擬實驗 82 8.2.6 小結 95 第9章 結論與未來展望 97 9.1 結論 97 9.1.1 試算表軟體功能 97 9.1.2 演算法功能 97 9.1.3 演算法參數設計邏輯: 98 9.2 未來展望 98 參考文獻 99 附錄 101 附錄1 工程圖 101 附錄2 CAM輸出加工參數 104 附錄3 約束矩陣R 136 附錄4 工件工序-工具機加工時間表 150 附錄5 工件工序-工具機加工功率表 153 附錄6 調整後工件工序-工具機加工時間表 156 附錄6 調整後工件工序-工具機加工功率表 159 | - |
dc.language.iso | zh_TW | - |
dc.title | 多目標多任務最佳化遺傳演算法應用於加工排程 | zh_TW |
dc.title | Multi-objective multi-task optimization genetic method for Job-shop Scheduling | en |
dc.type | Thesis | - |
dc.date.schoolyear | 111-2 | - |
dc.description.degree | 碩士 | - |
dc.contributor.oralexamcommittee | 蔡孟勳;王世明;魏振隆 | zh_TW |
dc.contributor.oralexamcommittee | Meng-Shiun Tsai;Shin-Ming Wang;Zhen-Long Wei | en |
dc.subject.keyword | 彈性零工式工廠排程,多目標多任務最佳化遺傳演算法,切削製程規劃,碳排放,客製化, | zh_TW |
dc.subject.keyword | Flexible Job Shop Scheduling,Multiobjective Multifactorial Evolutionary Algorithm,Cutting Process Planning,Carbon Emissions,Customization, | en |
dc.relation.page | 161 | - |
dc.identifier.doi | 10.6342/NTU202304136 | - |
dc.rights.note | 同意授權(限校園內公開) | - |
dc.date.accepted | 2023-08-13 | - |
dc.contributor.author-college | 工學院 | - |
dc.contributor.author-dept | 機械工程學系 | - |
顯示於系所單位: | 機械工程學系 |
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