A tool planning approach considering cycle time constraints and demand uncertainty

Abstract

The tool planning problem is to determine how many tools should be allocated to each tool group to meet some objectives. Recent studies aim to solve the problem for the cases of uncertain demand. Yet, most of them do not involve cycle time constraints. Cycle time, a key performance index in particular in semiconductor foundry, should not be ignored. The uncertain demand is modeled as a collection of scenarios. Each scenario, with an occurrence probability, represents the aggregate demand volume under a given product mix ratio. A genetic algorithm embedded with a queuing analysis is developed to solve the problem. Experiments indicate that the proposed solution outperforms that obtained by considering only a particular scenario.