隨機環境中自動化物料搬運系統晶舟車配置問題：新模型和新方法

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研究生：	黃昱璇 Huang, Yu-Hsuan
論文名稱：	隨機環境中自動化物料搬運系統晶舟車配置問題：新模型和新方法 Optimal Vehicle Allocation for Automated Materials Handling System in Stochastic Environment: New Formulation and Solution Methodology
指導教授：	張國浩 Chang, Kuo-Hao
口試委員:	桑慧敏 Song, Whey-Ming 陳俊宏 Chen, Chun-Hung
學位類別：	碩士 Master
系所名稱：	工學院 - 工業工程與工程管理學系 Department of Industrial Engineering and Engineering Management
論文出版年：	2012
畢業學年度：	100
語文別：	英文
論文頁數：	46
中文關鍵詞：	自動化物料搬運系統、隨機規劃、序列抽樣、超模型、模擬最佳化
外文關鍵詞：	AMHS, Stochastic Programming, Sequential Sampling, Metamodeling, Simulation Optimization
相關次數：	點閱：1 下載：0
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在自動化物料搬運系統(automated material handling system; AMHS)中，晶舟車的數量顯著影響半導體廠的設備成本和生產效率。然而，製造環境的複雜度和不確定性使得決定晶舟車數量是項挑戰。本研究提出新的模型以找出 AMHS 在購置成本最小且同時滿足運輸需求時的最佳車數分配，並提出以模擬最佳化為基礎稱作「模擬序列超模型(simulation sequential metamodeling；SSM)」的方法來求解模型。數值分析證明了SSM優於傳統的方法，尤其是在限制式數較多或模型變異較大的問題上。最後以一實證研究驗證本方法在實務上的可行性。

The number of vehicles (vehicle fleet size) in automated material handling system (AMHS) significantly influences cost of equipments and efficiency of production. However, it is not an easy task to decide the number of vehicles required in the system due to the complexity and uncertainty of manufacturing system. In this research, we propose a new formulation to determine the optimal number of vehicles of the AMHS so as to achieve the minimal vehicle cost, while satisfying transportation requirement. Furthermore, we develop a simulation-based methodology, called simulation sequential metamodeling, to solve the problem. Numerical experiments show that the proposed method outperforms the traditional approaches, especially in the problems with larger number of constraints or variance level. An empirical study is conducted to verify the viability of the proposed method.

 Introduction 1
 Literature Review 3
1   Analytical Approach 3
2   Simulation-based Approach 4
 System Description and Simulation Model 6
 Method 7
1   Overview of the Developed Methodology 7
2   Simulation Sequential Metamodeling Algorithm 10
2.1    Main Framework 10
2.2    Metamodel Construction 11
2.3    Estimating Feasibility Conﬁdence 14
2.4    Estimating Optimality Gap 16
2.5    Input Parameter 18
2.6    Sampling Scheme 18
2.7    Stopping Criterion 19
 A Numerical Example 20
1   Problem Description 20
2   Performance Evaluation 23
 An Empirical Study 28
1   Problem Formulation 29
2   Result 36
 Conclusions 38
Reference 39
Appendix 43
                                

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