Advanced Intelligent Manufacturing System – The Empirical Studies of Semiconductor Manufacturing Scheduling Problem and Hepatitis In-Vitro Diagnostic Device Manufacturing Scheduling Problem

• Main Authors: Wang, Hung Kai, 王宏鍇
• Other Authors: Chien, Chen Fu
• Format: Others
• Language: en_US
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/dcy3j8

博士 === 國立清華大學 === 工業工程與工程管理學系 === 105 === Advanced intelligent manufacturing system (AIMS) has become a crucial issue around the world. Many countries had proposed large scale project about AIMS, such as Manufacturing Renaissance of the USA, Industry 4.0 of the Germany, Industry 4.1J of the Japan, Made in China 2025. Executive Yuan of Taiwan proposed the similar project called Productivity 4.0 in 2015. This project focuses on the key technologies, such as sensing, intelligent robotic device, Internet of Things (IoT), and big data analysis. This dissertation aims to proposed a framework of AIMS for future smart factory. This framework consists with three parts, that is, open connected enterprise platform, smart manufacturing and smart product. Each part is connected with several research fields and topics, such as cloud computing, smart optimization method, big data analysis and sensor communication. Based on the AIMS framework, the industries can gradually improve their automation systems and processes in production line. Finally, the industries will upgrade to the smart factory in Industry 4.0. To validate the proposed smart optimization method, two empirical studies for semiconductor manufacturing and hepatitis in-vitro diagnostic device manufacturing are illustrated. The results show that the cycle time, the wafer scrap and product delivery tardiness can be reduced. Finally, the proposed smart optimization method can be embedded in the scheduling system of practical production line.