Stochastic programming for qualification management of parallel machines in semiconductor manufacturing

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• 作者：Xiaokun Chang ; Ming Dong ; ^{mdong@sjtu.edu.cn}
• 关键词：Semiconductor manufacturing ; Qualification management ; Stochastic programming ; Random capacity ; Lagrangian relaxation ; Surrogate subgradient method
• 刊名：Computers & Operations Research
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：79
• 期：Complete
• 页码：49-59
• 全文大小：704 K
• 卷排序：79

文摘

We propose a general formulation model for qualification management problem in semiconductor manufacturing. This model considers three types of stochastic parameters: product demands, capacity loss resulting from traditional random capacity factors and capacity loss due to qualification management. A Lagrangian-relaxation-based surrogate subgradient approach is proposed to solve this model. One significant advantage of this approach is that it allows the full use of distribution information. In addition, a heuristic algorithm which requires the information on expected values of random variables is designed. Given that obtaining complete distribution information for random variables is unavailable in practice, a simplified approach is also developed to approximate the initial problem. This simplified approach provides an upper bound. Results of numerical experiments demonstrate that the surrogate subgradient methods, the proposed heuristic, and the simplified surrogate subgradient method are effective.