臺灣博碩士論文加值系統

排程(Scheduling)在預鑄廠中扮演著重要角色，良好的排程會帶給公司資源上最有效的運用，減少不必要的浪費，然而目前預鑄廠的排程大多仰賴經驗法則，如此作法可能導致資源無效的運用與錯失交期。電腦化排程技術可提供比人工排程更精確之排程計畫，本研究提出一個符合預鑄廠生產情況的流程型生產排程模型，考慮生產暫存區容量，並以多目標基因演算法對此模型進行搜尋，搜尋目標分別為總完工時間最小化與延遲懲罰值最小化，最後，以範例來測試基因演算法的效率與績效，測試結果顯示基因演算法能夠有效地對此一模型進行求解，此外，本研究將生產暫存區容量納入排程考量，可獲得較合理且可行之排程計畫。

The goal of production scheduling is to strike a profitable balance among on-time delivery, short customer lead time, and maximum utilization of resources. A good one will bring company the most effective utilization of resources and reduce wastes. However, current practices in precast fabrication are fairly basic and depend greatly on experience, resulting inefficient resource utilization and late delivery. Computational techniques have been proven as an effective way in scheduling. To enhance precast production scheduling, this research develops a flow shop sequencing model. In the model, constraints encountered in practice and buffer sizes between stations are taken into account. A multi-objective genetic algorithm is used to search for optimum solutions with minimum makespan and tardiness penalty. The performance of proposed method is validated using a case study. Application results show that the multi-objective genetic algorithm can successfully search for optimum solutions for the model. In addition, considering buffer sizes between stations is crucial in acquiring reasonable and feasible precast production schedules.

目錄

封面內頁
簽名頁
授權書 iii
中文摘要 iv
ABSTRACT v
誌謝 vi
目錄 vii
圖目錄 ix
表目錄 x

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 研究範圍與限制 3
1.4 論文架構 4
第二章 文獻探討 6
2.1 預鑄廠現況說明 6
2.2 預鑄廠生產排程文獻探討 10
2.3 多目標基因演算法文獻探討 11
2.3.1 多目標規劃 11
2.3.2 基因演算法 13
2.3.3 多目標基因演算法 16
2.4 小結 17
第三章 預鑄廠生產模型 19
3.1生產模型符號說明 19
3.2 預鑄廠生產模型特性 20
3.3 排程衡量準則 26
第四章 多目標基因演算法 29
4.1演算機制說明 29
4.2多目標最佳化方式選定 30
4.3 多目標基因演算法流程 31
4.4 多目標基因演算法範例說明 38
第五章 實驗結果與分析 41
5.1 實驗相關資訊 41
5.2求解績效衡量 42
5.3演算參數說明 44
5.4 實驗範例比較分析 46
5.4.1單目標實驗範例分析 46
5.4.2多目標實驗範例分析 49
5.4.3排程最佳化實驗 55
5.5 系統說明 57
第六章 結論與建議 61
6.1 結論 61
6.2 未來研究方向 62
參考文獻 63

參考文獻

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