在一般彈性流線型工廠（Flexible Flow Shop）研究中，通常只考慮單一站點之平行機台(Parallel Machine)，沒有人針對跨站點可同時運用之平行機台進行研究。但因製造機台設備之成本過於昂貴、廠房地形面積限制、有效節省空間之設施規劃、機台功能之多元化設計、相同製程需要重複加工，故常有跨站點運用同一群組平行機台之個案。故本研究將以彈性流線型工廠為例，考量相同功能之平行機台擺放位置和製造流程之相對關係特性，來探討可跨站點運用平行機台之排程規劃，進而模擬不同的訂單加工順序和加工機台選擇方式，來求得各批量最小化總完工時間，最終將計算其最小化總懲罰成本，期能有效提昇加工效率，求得各訂單批量之最佳產出時間和公司總懲罰成本最小化，增加工廠之實際產能，並將此研究成果提供生管部門做為產品生產排程（Production Scheduling）規劃之參考。
本研究根據文獻探討及彩色濾光片（Color Filter）工廠實務上的了解，訂定出可共用平行機台選擇之因子，並藉由基因演算法（Genetic Algorithm）之來求出最佳解，並使用Visual C++軟體撰寫其程式碼，使爾後能依實際狀況，模擬演練並能實際應用。且經由實證研究顯示，應用基因演算法求解少筆、多筆工件批量之案例，於求解效率、最小化總完工時間和總懲罰成本最小化之求解品質，都有極佳之成果。

In most researches of flexible flow shop, only the parallel machine of the single stage is considered instead of the parallel machine which conducts simultaneous utility in cross stages. But parallel machine with simultaneous utility in cross stages is essential for the high cost of equipment, limitation of the factory terrain, utility maximization of space, diversified design of the equipment functions, and required repetition in process. This research, therefore, takes the flexible flow shop as an example to study how to arrange the production schedule for the parallel machine with simultaneous utility in cross stands and the relative relational characteristic between the position and producing process of same parallel machines is considered. The simulation of different choice of producing process and machines based on different order is practiced as a further step to calculate the manufacturing time of each batch which is the key to minimize the total penalty cost and improvement of manufacturing efficiency. With the best manufacturing time of each batch and minimization of total penalty cost, the capacity will literally enhanced and the result of this research will be a production scheduling reference for the PC department.
This research, based on literature study and actual plant understanding, use the genetic algorithm to simulate production scheduling and develop possible factors of common parallel machine. This research uses the elite preserve strategy of the genetic algorithm and the program code written by Visual C++ to stimulate and practice. And researches have shown that Genetic Algorithm is a much better way to calculate the results of minimize total working time and total punishment cost in spite of cases of more or less working sheets.

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