Modelling and resolving the joint problem of capacity expansion and allocation with multiple resources and a limited budget in the semiconductor testing industry

In the semiconductor testing process, many resources such as testers, handlers, loadboards and toolings are required to be ready simultaneously so that testing tasks can be conducted. A limited budget under depressed economy enhances the need for exploring better solutions of testing capacity expansion and allocation. However, to maximize profit as planning with multiple resources is very challenging. This study focused on the issues pertaining to the decisions of (1) the type and number of testers that should be invested to deal with forthcoming orders at a semiconductor testing facility under a constrained budget and (2) the allocation of tester capacity for the orders so as to maximize company profit with limited, multiple resources. Owing to the high computational complexity of the problem, the study developed a genetic algorithm to resolve the two issues simultaneously. A mathematical model was developed to formalize the problem and serve as a benchmark for comparison with the proposed algorithm that attacked the same problem more efficiently. Taguchi experimental design was employed to find the most appropriate parameters for the proposed genetic algorithm under a variety of budget set-up. Experimental results indicated that the proposed algorithm was robust enough to budget plans, and its performance approximated closely with that of the mathematical model.