近年來，隨著資訊科技的蓬勃發展與作業研究方法的普遍應用，利用電腦輔助來處理需要大量計算的現場排程問題日漸可行與有效。由於半導體產業的製造流程複雜，使得其有效且可行排程方法的設計十分困難。經由文獻回顧發現：半導體排程相關的研究多數針對半導體前端製造的現場派工與排程問題，較少文獻針對後端製造之排程設計提出實務可行的解決方案。
本研究根據半導體後端元件測試（Final Testing）生產環境的特性與限制，考量製造環境不穩定、工件的迴流、有限資源、機台最佳組合與同一種產品可以在不同種機台組合上測試等實際狀況，建立半導體元件測試排程問題之最佳化數學模式，並且發展適合實務快速求解需要之啟發式演算法，除此之外，本研究發展半導體元件測試排程之遺傳演算法以提高求解之品質。為了使現場排程人員能夠即時得知排程結果以處理製造環境的不穩定，本研究發展之演算方法皆提供了以甘特圖（Gannt Chart）為基的視覺化排程結果。本研究經由模擬與實驗的比較，發現啟發式演算法與遺傳演算法求解的品質較一般派工法則求解的品質佳，並且發現遺傳演算法實際改善了啟發式演算法。

Recently, using computers to assist in detailed scheduling becomes more feasible and popular due to high improvement of information technique and operation research. It is very difficult to design an effective and efficient scheduling method for semiconductor manufacturing with complex manufacturing flow. Some researches have focus on scheduling and dispatching for Front-End of semiconductor manufacturing. However, little research has been done for designing practical scheduling methods of Back-End of semiconductor manufacturing. This research considers manufacturing dynamics, jobs recirculation, limited resource, optimized machine configuration, and that the same product can be tested on different types of machines with non-identical efficiency and constructs an optimal mathematical model for semiconductor final testing. Besides, for practical purpose, this research also develops a heuristic and a quality-improved genetic algorithm with procedures graphically representing schedules based on Gannt charts. After simulations and experiments, we find that the proposed heuristic and genetic algorithm perform better than dispatching rule-based heuristics.

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