Subpopulation preference adjective non-dominated sorting genetic algorithm for multi-objective capacity expansion for matured fabs

Abstract

Capacity expansion strategy consists of fulfilling the demand with capacity portfolio via alternative configurations with long lead time for procurement and installation. Sequential dependent decisions including demand planning and cost structure of capital expenditure shall be taken into consideration for achieving enterprise profitability. Though a number of studies have been done to address related issues, limitations of existing approaches can be traced in part to the lack of a systematic framework in which multiple objectives of related total resource management problems can be considered and integrated. Little research has been done to address the present problem for capacity expansion for matured fabs from the perspective of total resource management. To fill the gaps, this study applies the concept of total resource management to integrate operational strategies and the overall usage of resources. This study develops a capacity expansion model with multiple objectives including minimizing resource costs, maximizing overall return, maximizing revenue, and minimizing capacity risk. Since the formulation of the multi-objectives can be non-linear and the size of the problem is increasing, it is difficult to solve the problem in reasonable time for practical use. A subpopulation preference adjective non-dominated sorting genetic algorithm (SPANS-GA) is developed to solve the decision problem. An empirical study in a leading semiconductor company in Taiwan is conducted for validation. The results have shown practical viability of the developed solution for multi-objective capacity planning for matured wafer fabs for total resource management.