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研究生:吳彥霆
研究生(外文):Wu, Yen-Ting
論文名稱:多階段隨機規劃模型求解不確定性產量與需求下 之生產批量與排程問題
論文名稱(外文):A Multistage Stochastic Programming Model for Lot Sizing and Scheduling Problem under Uncertain Yields and Demand
指導教授:陳勝一陳勝一引用關係
指導教授(外文):Chen, Sheng-I
口試委員:陳勝一林則孟洪暉智
口試委員(外文):Chen, Sheng-ILin, James. T.Hung, Hui-Chih
口試日期:2020-07-01
學位類別:碩士
校院名稱:國立交通大學
系所名稱:工業工程與管理系所
學門:工程學門
學類:工業工程學類
論文種類:學術論文
論文出版年:2020
畢業學年度:108
語文別:英文
論文頁數:43
中文關鍵詞:批量與排程問題多階段隨機規劃矽晶棒生產
外文關鍵詞:Lot-sizing and schedulingMultistage stochastic programmingSilicon ingot manufacturing
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本研究針對不確定性產量與需求下之批量生產與排程問題作探討。生產批量與排程問題同時決定了產品在每段時間內的生產數量與加工順序,以最小化生產成本或最大化收益。然而,不確定性的發生,使得問題更加複雜且難以處理。本研究受到半導體產業中前段的矽晶棒製造所面臨的實際問題所啟發,建置了一個多階段隨機規劃模型以解決多作業參數組合、機台、產品和多期的批量生產與排程問題,並且同時考慮了機器限制、設置時間與產品順序相關以及不確定的生產量與客戶需求。除此之外,兩項長晶站中的特色被考量進模型設計。確定性模型和隨機模型在不同需求趨勢與多種變異程度的不確定參數下進行比較。實驗結果說明隨機規劃模型的預期成本表現優於確定性模型。多階段模型與兩階段的隨機規劃模型之間的價值也被比較於所有的測試問題。多階段隨機規劃模型提供的解減少了大約平均35%的生產成本,說明了兩階段模型提供了一個很弱的近似解。因此,在規劃存在不確定性產量與需求下之多週期的批量生產與排程問題時,應將不確定事件考慮進模型當中。
This study focuses on modeling and solving variant lot-sizing and scheduling problems, where decision-makers determine the quantity and sequence of products to be produced on a machine to minimize overall production costs or maximize total profits. The first challenge is that uncertainties often occur in a manufacturing environment making the problem more complicated and difficult to handle. We motivated by a real problem in silicon ingot manufacturing to develop a multistage stochastic programming model of multi-recipe, multi-machine, multi-product, and multi-period capacitated lot sizing and scheduling problem with the additional considerations of machine eligibilities, sequence-dependent setups, and uncertain yields and demands. Besides, characteristics in the crystal growth station are added into our model. Both deterministic and stochastic models are compared under three demand trends with different variance levels on uncertainties. The experiment results show that the expected cost performance of the stochastic programming model outperforms the deterministic model. Finally, we explore the value between multistage and two-stage stochastic programming models for all testing problems. The multistage stochastic program solution reduces about 35% production cost on average from the two-stage one suggested that the model to aggregate periods can rarely provide a weak approximation. Therefore, uncertain events in each period should be considered when making multi-period lot-sizing and scheduling decisions.
中文摘要 i
Abstract ii
Acknowledgment iii
Contents iv
List of table v
List of figure vi
I. Introduction 1
1.1 Research background 1
1.2 Research motivation and contributions 5
II. Literature review 6
2.1. Lot sizing and scheduling problem (LSSP) 6
2.2. LSSP under uncertainties 9
III. Methodology 13
3.1. Introduction of stochastic programming 13
3.2. Problem statement and assumptions 16
3.3. Deterministic model 17
3.4. Multistage stochastic programming model 21
IV. Computational Results 28
4.1. Case study 28
4.2. Parameter setting 29
4.3. Quality of deterministic and stochastic solutions 31
4.4. Value of multistage stochastic programming 37
V. Conclusions and Discussions 40
Reference 41
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