隨機規劃法在半導體機台組態規劃之應用

Application of Stochastic Programming to Tool Portfolio Planning In Semiconductor Manufacturing

林毓舜(Yu-Shun Lin)

指導教授 ： 周雍強

國立臺灣大學/工學院/工業工程學研究所/碩士(2004年)

https://doi.org/10.6342/NTU.2004.01790

摘要

半導體製造的機台組態規劃是指依據產品需求與製程要求，考量投資報酬與風險等因素，以決定工廠內機台類別與數量的規劃程序，所面臨的困難主要在於產品需求不確定以及設備採購的前置時間很長。隨機規劃法是一種適用於不確定性問題的數學規劃法，其效益與不確定性的程度有關，本研究將評估隨機規劃法在機台組態決策的適用性，並證明該方法確有顯著效益。隨機規劃法還未為產業所普遍採用，本文建議在面對需求不確定的產業環境，晶圓製造廠非常值得採用這個方法。
另一方面，雖然隨機規劃法對需求預測偏誤有較高的容忍度，但也相對造成解空間複雜度的增加，透過本研究所提出如何選擇具代表性的樣本情境點及合宜的規劃視野亦能夠有效的縮減解空間，取代部分文獻是發展演算法改進求解效率的觀點，使此法應用於機台組態規劃上能更加的完備，進而創造社會經濟福祉。

關鍵字

隨機規劃 ； 半導體製造 ； 產能規劃 ； 機台組態規劃

並列摘要

Tool portfolio planning is an important task in semiconductor manufacturing in which the types and quantities of processing tools are determined. The challenging facing tool portfolio planning is the uncertainty in product demand and process technology. The paper is made up of two parts. Stochastic programming is a method that is suitable for problems with uncertain factors. However, its utility will depend on the level of uncertainty. We evaluate the economic benefits of using stochastic programming in this thesis. We show that the benefits are very significant and recommend that the method of stochastic programming should be used in tool portfolio planning in the semiconductor manufacturing industry.
Although the stochastic programming method has more tolerant of demand forecast inaccuracy, it causes to increase solution space complexity relatively. The core issues of this study how to select representative scenario outcomes and planning horizons will be reduce solution space efficiently in order to replace the viewpoint of developing algorithm.

並列關鍵字

semiconductor manufacturing ； stochastic programming ； capacity planning ； tool portfolio planning

參考文獻

2. Barahona, Francisco, Stuart Bermon, Oktay Gunluk, and Sarah Hood, “Robust Capacity Planning in Semiconductor Manufacturing,” Research Report RC22196, IBM, 2001.

4. Birge, J.R. and Francois Louveaux (1997) Introduction to Stochastic Programming, Springer-Verlag, New York, no 142.

5. Charnes, A. and W.W. Copper, “Chance-constrained programming,” Management Science 5 (1959) 73-79.

6. Chou, Yon-Chun and Chuan-Shun Wu, “Economic Analysis and Optimization of Tool Portfolio in Semiconductor Manufacturing,” IEEE Transactions on Semiconductor Manufacturing, Vol. 15, No. 4, pp.447-453, 2002.

8. Christie, M. E. Robert and David Wu, “Semiconductor Capacity Planning: Stochastic Modeling and Computational Studies,” IIE Transactions, vol. 34, pp. 131-143, 2002.