在現今的生產環境下，傳統製造系統已難以滿足市場對於多品種小批量產品的需求。因此，精密而高度自動化的彈性製造系統（FMS）逐漸受到業界的重視，且其無人搬運車（AGV）的高度彈性及自動化能夠同時因應傳統製造業及高科技業的各項需求，更加促進了彈性製造系統的發展。由此可知，審慎規劃、設計的無人搬運車運載系統在彈性製造系統中扮演著舉足輕重的角色。
此研究主要同步考量彈性製造系統中的生產排程及無人搬運車派車系統，其目標為達成總完工時間的最小化。其中，作業的排序及無人搬運車的指派是影響績效指標的主要因素。然而，現今的研究時常忽略無人搬運車系統的車輛壅塞問題。為使模擬的彈性製造系統更貼近現實產線，此研究導入無人搬運車的區域控制系統，並能有效解決車輛的壅塞及交通控制問題。此研究採用物件導向的模擬軟體Plant Simulation進行模擬環境的建構，藉由模擬程式計算出個情境下的完工時間並當作研究的績效指標。本研究提出一以分量形式達成模擬最佳化的演算法來找到最佳的作業排序及車輛指派方式，並達成最小化風險控制下的完工時間之目標。其中，分量的表示方式能代表決策所造成之不利趨勢，進而達到風險分擔的目標。

Nowadays, traditional manufacturing systems are not able to satisfy the needs of multiple kinds of small-batch production. Therefore, a highly automated and sophisticated system that can achieve high flexibility and productivity in large variety of products is developed, which is known as Flexible manufacturing system (FMS). Due to the flexibility and automation of automated guided vehicles (AGVs), they are now widely used in both traditional and high technology industries. Therefore, a carefully designed and efficiently managed material handling system plays an important role in planning and operation of a flexible manufacturing system.
In this paper, the problem of simultaneous scheduling the machines and identical AGVs in flexible manufacturing systems was addressed with the objective of minimizing the risk-controlled makespan. This problem was composed of two interrelated decision problems: the scheduling of machines, and the scheduling of AGVs. However, most research related to FMS have omitted the congestion issues of AGVs. In order to reach real manufacturing environment and implement a traffic control system, zone control is designed and added in this model. In this research, the simulation software Plant Simulation was used to build this FMS model, which could be the tool to satisfy the objective function of this research. A Quantile-based simulation optimization algorithm was proposed and used to find the optimal task sequence and assignment of vehicles. The computational performance results of this algorithm could show the best task sequence with minimized makespan. Owing to the flexibility of using quantile, the results of this research could be used on risk management.

摘要 I
Abstract II
致謝 III
Contents IV
List of Tables VII
List of Figures VIII
Chapter 1 Introduction 1
1.1 Background and Motivation 1
1.2 Research Objectives 4
1.3 Research Method 5
Chapter 2 Literature Review 6
2.1 Simultaneous Scheduling of Flexible Manufacturing System 6
2.2 Risk-controlled Scheduling 14
2.3 Genetic Algorithms 15
2.4 Quantile Estimation 17
Chapter 3 Problem Definition 20
3.1 Problem Statement 20
3.2 System Description 23
3.2.1 FMS Environment 24
3.2.2 Job Set Information 25
3.2.3 Layout Information 27
3.2.4 Vehicle Scheduling Methodology 28
3.2.5 Objective Function and Notations 30
3.3 Simulation Framework 31
Chapter 4 Methodology 35
4.1 Adaptive Genetic Algorithm with Fuzzy Logic Control 35
4.1.1 Adaptive Genetic Algorithm 37
4.1.2 Chromosome Representation and Codify 38
4.1.3 Generating Initial Population and Repair Module 39
4.1.4 Evaluation of Fitness Function and Selection 40
4.1.5 Order Preserving One-Point Crossover 41
4.1.6 Pairwise Interchange Mutation 43
4.1.7 Local Search Mutator 45
4.1.8 Replacement and Stopping Criteria 46
4.1.9 Fuzzy Logic Control 47
4.2 Control Risk through Quantile Estimation 53
4.2.1 Quantile Estimation 54
4.2.2 L-esimators and VaR 54
4.2.3 Control Risk by Calculating VaR 57
Chapter 5 Experiments and Results 58
5.1 Job Set Information 58
5.2 Data Analysis of different types of GAs 61
5.2.1 Convergent Condition Comparison 64
5.2.2 Run Time Comparison of Algorithms 67
5.2.3 Risk controlled Makespan Comparison of Algorithms 68
5.2.4 Standard Deviation Comparison of Algorithms 70
5.3 VaR and Risk Management 74
5.3.1 Calculating the VaR of Different Quantile Proportion 74
5.3.2 Out-of-sample Test 76
5.3.3 Comparison of the Out-of-sample Test 77
5.3.4 Risk Management Through VaR 81
Chapter 6 Conclusion 83
Reference 85
Appendix 91
Appendix A: Experimental Design Phase 91
Appendix B: Detail Information of Algorithm Comparison 94
Appendix C: VaR of Different Quantile Proportion 114

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