Abstract
High-tech industries face rapidly changing of industrial environment and dramatically increasing of the competitive pressure, while capacity planning is affected by pricing strategies, cost structure, market demand, inventory management, capacity portfolio,and demand forecast. Furthermore, high-tech industries are capital-intensive industries, which requires substantial amount of capital for the capacity-building equipment machine. With the industrial environment and its capital-intensive quality, the capacity manage is especially improtant to reaceh operational goals. In order to enhance the overall return, multi-objectives for capacity planning is crutial. This study imports the concept of capacity management of high-tech industry, using UNISON decision framework to construct capacity planning decision support system for the decision-oriented. The genetic algorithms as the the core module to solve the multi-objectives capacity planning for financial goals and production goal. In the interactive scheme, the decision makers are guided iteratively to the most preferred solution. The decision makers can use the systematic procedure to analyze and to solve the problem to reach the optimal financial goals and production goal. This study applied to semiconductor manufacturing as empirical objects, and the results show our decision support system is better than case corporations and also show the preference of the goal of decision makers through interactive modules, and thus to enhance the consistency of decision-making quality and faster response time.