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研究生(外文):Hang-Chu Tasi
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(外文):Ta-Ho Yang
外文關鍵詞:Discrete-event SimulationWire BondingSetupGenetic Algorithm
  • 被引用被引用:17
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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.
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 Flow Time實驗結果 41 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
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