傳統製鞋業在過去多以實務經驗評估及設定產線之系統參數，然而鞋業生產參數眾多，分析不易，且以實務經驗評斷之方式有失其客觀性。因此，本研究透過建立具代表性之模擬模式進行實驗設計以及反應曲面法，分析生產參數和關鍵績效指標間之關聯，求解製鞋業生產線之最佳參數設定。
本研究提出一模擬最佳化架構並以實際鞋廠生產數據進行實證研究，驗證該架構之可行性。其中，最佳化過程是先以篩選實驗確認候選生產參數對於績效指標有無顯著的影響，結果顯示：各作業間之在製品轉移批量、投料頻率、楦頭數量以及楦頭週轉率對於總產出、週期時間和機台利用率三項績效指標皆有顯著的影響。其後，再將被選定之生產參數進行實驗設計建立一階模型，探討該模型中之曲率反應。最後以反應曲面法和中心點複合式設計建構二階模型，透過曲面圖、等高線圖和反應曲面分析估計最佳參數設定以及該參數設定下績效指標之預測值。不僅如此，本研究亦藉由平穩點分析以及典型分析，辨別該預測值屬於極小值、極大值或為一平穩點。
實證研究主要分為單目標最佳化以及多目標最佳化兩部份，其中多目標最佳化是以渴望函數及最佳化軟體進行求解。實驗結果顯示不論單目標或多目標最佳化，其預測值皆較當前設定佳。

The aim of proposed research is to build a representative simulation model and implement experimental design and response surface methodology (RSM) to analyze the effect between system parameters and key performance indicators. Subsequently, the optimal setting of footwear production line can be determined.
A simulation optimization framework is proposed and validated by an empirical study. In the optimization process, transfer batch size, material arrival frequency, number of shoe lasts and the turnover of shoe last are the selected parameters. After screening factors, they are determined that they can affect throughput, cycle time and machine utilization significantly. The first-order model is built by experimental design in order to analyze the curvature effect of model. Furthermore, the second-order model is constructed by RSM and central composite design. An optimal setting and predicted response are evaluated through the contour, surface plots and analysis of response surface. Moreover, the stationary and canonical analysis are performed to identify whether predicted response is maximum, minimum or saddle point.
The empirical study is divided into two parts, single-objective and multi-objective optimization. The multi-objective is optimized by using desirability function and optimization software. The experiment result shows that both of their predicted responses are better than current setting.

摘要 I
Abstract II
致謝 III
Contents IV
List of Tables VI
List of Figures VIII
Chapter 1 Introduction 1
1.1 Background and Motivation 1
1.2 Objectives 3
1.3 Organization of Thesis 4
Chapter 2 Literature Review 5
2.1 Simulation Optimization 5
2.2 Response Surface Methodology 7
2.3 Footwear Industry Simulation 8
Chapter 3 Problem Definition 12
3.1 Footwear Manufacturing System 12
3.1.1 Shoe Process and Shoe Model 13
3.1.2 Facility Description 15
3.2 Problem Statement 18
3.2.1 Decision Variables 20
Chapter 4 Simulation Optimization Framework 24
4.1 Simulation Model 25
4.1.1 Assumptions 27
4.1.2 Data Collection 28
4.1.3 Model Construction 28
4.1.4 Verification and Validation 30
4.2 Response Surface Methodology 32
4.3 Response Surface Design 37
4.3.1 Central Composite Circumscribed Design 38
4.3.2 Central Composite Inscribed Design 39
4.3.3 Central Composite Face-Centered Design 40
4.3.4 Box-Behnken Design 41


Chapter 5 Empirical Study 43
5.1 Experimental Design 43
5.1.1 Levels of Factors 44
5.2 Single-objective Optimization 46
5.2.1 Throughput 46
5.2.2 Machine Utilization 62
5.2.3 Cycle Time 77
5.2.4 Validation and Conclusion of Single-objective Optimization 88
5.3 Multi-objective Optimization 90
5.3.1 Meta-model 91
5.3.2 Desirability Function 97
5.3.3 LINGO 100
5.3.4 Validation and Conclusion of Multi-objective Optimization 104
Chapter 6 Conclusion 106
References 109
Appendices 113
Appendix A: Contour and Surface plots of Throughput 113
Appendix B: Contour and Surface plots of Machine Utilization 116
Appendix C: Contour and Surface plots of Multi-objective (TP) 120
Appendix D: Contour and Surface plots of Multi-objective (MU) 122
Appendix E: Contour and Surface plots of Multi-objective (CT) 124

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