隨著科技的進步，製造業面臨顧客需求多樣化的轉變，排程的最佳化也因此受到挑戰，實務上許多案例會透過基因演算法來進行求解。若有一套系統化的架構有效地協助基因演算法之開發，將使基因演算法之開發更加快速。本研究目的為針對單目標零工式生產排程問題發展基本型基因演算法之生成模式。考量零工式生產排程問題特性與過去文獻對基因演算法運算機制之探討，本研究彙整常見之編碼方式，並同時針對基因演算法之交配、突變與選擇運算元進行討論，以田口方法執行運算元組合實驗，以獲得最佳基因演算法，最後，提供基因演算法績效之改善方向以便進行客製化調整。本研究以某TFT-LCD模組組裝案例作為實證研究對象，找出最佳運算元組合設定，實驗結果顯示，本研究所提之方法比起全因子實驗，能夠以較少實驗次數獲得相同結果，有利基因演算法之開發。

As technology develops, the manufacturing industry has to meet a variety of customer needs, posing a challenge to scheduling optimization. In practice, genetic algorithms are the most common ways to optimize scheduling. If a systematic framework effectively assisting in creating a genetic algorithm exists, it will definitely accelerate the developing process. This study aims to build a model to obtain simple genetic algorithms for single-objective job shop scheduling problems. Considering the characteristics of job shop scheduling problems and the discussion about the way genetic algorithms work in the past literature, we collect some commonly used genetic representations, crossover operators, mutation operators, and selection operators. After that, we find the optimal combination of operators, meaning the optimal genetic algorithm, by Taguchi methods. To improve the performance of a genetic algorithm, we also provide some techniques for customizing the algorithm in the end. This study takes a TFT-LCD module assembly case as an empirical study. The result shows the proposed method attain the same algorithm with much lesser experiments compared to the full factorial design, which is favorable in terms of developing genetic algorithms.

蘇朝墩（2013），品質工程：線外方法與應用，前程文化，新北市。
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