本研究主要探討變頻器電子組裝產業後段組裝製程之排程問題。聚焦於組裝線製程並將組裝線視為非等效平行機台(unrelated parallel machine scheduling)，同時考慮次站的燒機問題。燒機站為批次排程問題，批次排程又可分為兩個決策點，第一，決定批次的組成，第二，將批次進行機台指派。本研究主要探討組裝線排程與燒機站的批次組成問題，考量生產系統的特性與限制，包含:機台限制、順序相依設置時間、不同容量大小、可用電壓級距限制等。
本研究在Joo & Kim(2015)以及Basir et al.(2018)提出的基因演算法架構下，根據問題特性做變化，提出「二階段排程基因演算法」與「整合式排程基因演算法」。「二階段排程基因演算法」主要提出一個進行機台排程同時粗估燒機站容量使用的派工法，另外，根據裝箱問題文獻提出一個優化批次組成的啟發式算法。「整合式排程基因演算法」則是將問題特性融入重新設計染色體組成，使組裝線排程與燒機的批次組成問題能夠在整合式方法中一次解決。
本研究經實驗發現在不同工單數量的問題下「二階段排程基因演算法」的求解效率表現較「整合式排程基因演算法」為佳。總結來說，進行組裝線排程時粗略估計工單使用的電壓級距容量，組裝線排程完成後再根據工單來到的先後順序進行批次組成，在本研究範圍內有較好的表現。

關鍵詞：非等效平行機台排程、批次排程、基因演算法

This research investigates the scheduling of the assembly process in the back-end process of the variable-frequency drive assembly industry. The assembly line process is regard as an unrelated parallel machine scheduling. Also, parallel batch scheduling problem at burn-in station is considered. The batch scheduling can be divided into two decisions: (i) forming the batches; and (ii) assigning batches to the machines. This study mainly discusses the assembly line scheduling and the batch forming of the burn-in test station and considering the production characteristics: machine eligibility, sequence-dependent setup time, different capacity and burn-in level availability.
In this research, based on the genetic algorithm framework proposed by Joo & Kim (2015) and Basir et al. (2018), two heuristic models are developed to solve this problem. The “two-stage scheduling genetic algorithm” proposes a dispatching method that performs machine scheduling and roughly estimates the capacity of the burn-in station. In addition, an algorithm for optimizing batch forming is proposed based on the bin packing problem. "Integrated scheduling genetic algorithm" is to combine the characteristics of the problem into the chromosome composition, so that the assembly line scheduling and the batch forming can be solved at one time by the integrated method.
It is shown that the "two-stage scheduling genetic algorithm" performs better than the "integrated scheduling genetic algorithm" in both solution efficiency and performance. In the conclusion, if the burn-in level capacity is roughly estimated when arranging assembly line schedule, the batch forming will outperform in this research.
Keywords: Unrelated parallel machine scheduling, Batch scheduling, Genetic algorithm

目錄
摘要 i
Abstract ii
致謝 iii
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 2
1.3 研究範圍 2
1.4 研究步驟 3
第二章 變頻器電子組裝廠個案分析 4
2.1 組裝廠產業與生產製程 4
2.2 製程生產特性 5
2.2.1 組裝線生產特性 6
2.2.2 燒機站生產特性 6
2.3 生產規劃 7
2.3.1 生產規劃說明 7
2.3.2 生產規劃範例 9
2.4 變頻器組立站排程與燒機站排程問題特性分析 15
2.4.1 組立站排程分析 15
2.4.2 燒機站排程分析 19
2.4.3 組立站與燒機站關係分析 27
2.5 問題說明 30
第三章 文獻回顧 32
3.1 排程環境分類與問題特性 32
3.2 平行機台排程問題相關文獻 34
3.3 批次排程相關文獻 37
3.4 基因演算法相關文獻 40
3.5 文獻回顧小結 48
第四章 基因演算法應用於含燒機之組裝線排程問題 50
4.1 問題描述 50
4.1.1 問題特性 50
4.1.2 問題假設條件 51
4.1.3 績效指標規劃 52
4.2 基因演算法研究方法 54
4.3 「二階段排程基因演算法」 57
4.4 「整合式排程基因演算法」 65
4.5 兩個方法之差異 75
4.6 實驗結果與分析 76
4.6.1 實驗資料說明 77
4.6.2 實驗結果與分析 79
第五章 結論與建議 88
5.1 結論 88
5.2 建議 89
參考文獻 90
附錄 93

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