Abstract
Consumers pursue affordable and customized products and services with the changes of consumer products market. Manufacturing industry is facing rapid changes in market demand, short product life cycle, fast technology migration and high capital investment, resulting in difficult to accurately predict demand, lengthen production lead time, and low production yield and other operational challenges.Traditional manufacturing strategy is focusing on scale economies. Facing the changes of various marketing demands and short product life cycles, traditional production system has been unable to meet the complex and rapid changes in the manufacturing environment, and can not effectively deal with derived manufacturing problems. In order to cope with production planning, scheduling, and manufacturing allocation problems that caused by various product demands and complex product lines. This study aims to develop a framework for decision-based intelligent manufacturing system that including production system modules and decision agent system to pursue quickly response, high productivity, flexible and robust production. First, the proposed hierarchical manufacturing system is pursuing stability and efficiency to achieve production optimization. The order management module will accumulate all customers' forecast and orders to integrate total demands that as a follow-up demand input for production planning module and multiobjective scheduling module. Further, the manufacturing resource allocation module and cyber-physical system function module will provide promptly manufacturing and machine information to control production status. Second, the proposed decision-based manufacturing agent has the capability of adaptive, flexibility, coordination, decision-making to provide promptly production while abnormal events arise. The decision-based manufacturing agent could coordinate and communicate with others function modules and agents to integrate information and provide possible solutions for those disturb events. Thus, the decision maker could consult the possible solutions and do the final decision to access those disturb events, and let the production system return to normal operation. Three empirical cases are demonstrated to validate the proposed decision-based intelligent manufacturing system in this study. First, a multiobjective scheduling problem in TFT-LCD module assembly process is proposed that consider manufacturing conditions in shop floor. The multiobjective scheduling module used multi-objective hybrid genetic algorithm (MO-HGA) to solve multiple and conflicting production objectives, minimizing makespan, minimizing the weighted number of tardy jobs, and minimizing the total machine setup time, that are directly related to productivity and customer satisfaction. Second, a manufacturing disturb case in TFT-LCD module assembly process. When an abnormal event happened in tuffy glue coating process, decision agent was trigged that communicated with other agents to exchange information and understand the latest status. Then, decision agent could provide the possible solutions for decision makers for final judge. Third, a manpower allocation and route planning problem for manual material handling system (MMHS) in a 200mm wafer fab is proposed. The manufacturing resource allocation module used a novel route planning approach to utilize the routes that reduce the technician traveling distance and transportation time. And it also used manpower loading evaluation model is developed for determining the appropriate number of technicians that all improve the MMHS efficiency and productivity.