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研究生:蔡杭助
研究生(外文):Hang-Chu Tasi
論文名稱:結合基因演算法與離散事件模擬求解即時性平行機台之派工規劃問題
論文名稱(外文):A hybrid genetic algorithms and discrete-event simulation method in solving a real-time parallel machines dispatching problem
指導教授:楊大和楊大和引用關係
指導教授(外文):Ta-Ho Yang
學位類別:碩士
校院名稱:國立成功大學
系所名稱:製造工程研究所碩博士班
學門:工程學門
學類:機械工程學類
論文種類:學術論文
論文出版年:2002
畢業學年度:90
語文別:中文
論文頁數:77
中文關鍵詞:離散事件模擬銲線區換模基因演算法
外文關鍵詞:Discrete-event SimulationWire BondingSetupGenetic Algorithm
相關次數:
  • 被引用被引用:17
  • 點閱點閱:910
  • 評分評分:
  • 下載下載:269
  • 收藏至我的研究室書目清單書目收藏:4
半導體產品種類多、變異性大,造成以代工為主的封裝業面臨少量多樣的快速反應生產模式,因此其完工時間必須有效縮短,客戶滿意度也必須進一步提升,以符合市場要求。銲線區多為半導體封裝廠的瓶頸工作站,其加工時間長、機台數量多,若是排程或派工決策不良,將導致系統資源浪費,並造成系統流程阻塞,進而影響其他相關工作站,因此在不同產品投料量及有效分配資源的前提下,現場管理者必須即時的分配資源以應付產品快速轉換的需求,以達到即時性的排程與派工規劃。本研究提出結合基因演算法與離散事件模擬求解平行機台支派工問題,並以一封裝廠實際案例來驗證此方法的適用性,以作為實際工廠訂定決策的參考依據。
As there are varieties of semiconductor products, and they have a large variation on the specifications, the subcontractors whose major business is packing face a production mode of fast reaction to meet the request of diversification and small quantity. They must work very efficiently so that shorten their finish period, and also increase customer’s service level further in order to meet the market demand. In most cases, wire bonding is a bottleneck workstation for the semiconductor packing plants. The process time is so long, and the process requires many machines. Improper scheduling or dispatch might result in a waste on system resource, and a jam on the system flow, and then affects the relevant workstations. Therefore, under a premise of different input for each product and for efficient resource allocation, an administrator must to assign the resource immediately in order to meet the demand of fast change on the products, and to achieve the scheme of prompt scheduling and dispatching. This research is to suggest a model that combine genetic algorithm with discrete-event simulation in solving a real-time dispatching planning problem of parallel machines workstation . Eventually use a packing plant as the actual case to prove the practicability of the method. The plants may refer to this method to make their policy decision.
目錄
中 文 摘 要 i
Abstract ii
誌 謝 iii
目 錄 iv
圖 目 錄 vii
表 目 錄 viii
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機與目的 1
1.3 研究範圍、方法與流程 3
1.4 論文架構 4
第二章 文獻探討 6
2.1 半導體封裝製程簡介 6
2.2 排程派工與換模 9
2.3 基因演算法 13
2.3.1 基因演算法簡介 13
2.3.2 基因演算法應用 18
2.4 離散事件模擬 19
2.4.1 模擬介紹 19
2.4.2 模擬應用 19
第三章 模式建立 21
3.1 建構流程 21
3.1.1 派工流程說明 23
3.1.2 基因演算法參數設定 25
3.2 建構模擬模式 27
3.3 衡量指標 31
第四章 案例應用 32
4.1 案例描述 32
4.2 資料蒐集 33
4.3 實驗設計 35
4.3.1 Flow Time實驗設計 36
4.3.2 Service Level實驗設計 38
4.4 結果分析與討論 40
4.4.1 實驗結果 41
4.4.1.1 Flow Time實驗結果 41
4.4.1.2 Service Level實驗結果 43
4.4.2 分析與討論 44
第五章 結論與建議 50
5.1 結論 50
5.2 未來研究建議 51
參考文獻 53
附錄A 實驗設計(一)染色體分佈-以Flow Time為目標 55
附錄B 實驗結果染色體分佈-以Flow Time為目標 62
附錄C 投入量(67, 68, 0)擴增實驗之染色體分佈 66
附錄D eM-plant模擬模式相關語法 68
參考文獻
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