排程問題的研究,已經有很多的專家、學者以及業界前輩先進,研究出許多的解決方法,而在實務的應用上,應該選擇哪一種方法來解決排程問題,仍必須考量產業特性、產品特性、加工類型,以及環境設備等限制因素;除此之外,所選定之方法或模式,亦須配合這些限制條件,來略做調整或改善,以求得最佳解。 本研究以通信產業的測試製程為例,採用基因演算法(Genetic Algorithm),並針對測試製程之特性與測試儀器數量的限制條件,以及已知的生產條件,來進行流線型排程,追求生產線上之測試階位(Test Flow)的測試排程之總測試工時最小化為目標,提高現今微利時代的電子組裝業之產能提昇、降低成本、增加競爭力。 本研究發展的GA排程系統,是考慮測試條件(測試站數、每站的生產工時及各站需求儀器數量的關係) 並符合T公司的生產環境…等因素下,所建立的流程型測試排程系統,此系統有效的減少 30% 的完工工時、提高高價儀器的利用率,證明此排程系統確實能有效解決T公司的瓶頸製程問題。
Many experts, academics, and seniors in industry have developed many of the solutions, and practical applications in scheduling studies. But in practical applications, they must consider industry characteristics, product characteristics, process type, and environmental equipment limitations for deciding which method should be chosen to solve scheduling problems. In addition, the selected method or model must meet these constraints, to be slightly adjusted or improved in order to obtain the optimal solution. In this study, we take the testing process in communications industry as an example. We arrange Flow Shop scheduling with Genetic Algorithm, restrict the characteristics in testing process and the number of test instruments, and with the known the conditions of production.,To Pursue the goalss of minimizing total test working hours in the test flow , and improving the current low-profit era of electronic assembly industry's production capacity, reduce costs and increase competitiveness The research and development of the GA scheduling system is to consider the test conditions (test stations, production hours per station and the relationship between station requirement and the the number of instruments) and with the production environment and other factors of T company' to create tjeProcess test scheduling system. This system effectively reduced by 30% working hours, improves the utilization of expensive equipment, this scheduling system that really can effectively solve the bottleneck of the process T's problem.