隨著電子商務的興起與包裹配送需求的增加，為了能以更少的時間與成本完成包裹的分揀與分流，物流配送中心的內部運作越來越受到重視，而設施規劃與佈局(Facility Layout Planning)對於物流配送中心的包裹分揀效能有很大的影響。本研究採用模擬最佳化(Simulation Optimization)方法，建構模擬模型並以基因演算法求解分揀系統之輸送帶配置問題，其中將針對分揀系統中的捷徑配置設計做優化的探討，以期提高分揀系統的效率表現。
本研究以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分揀系統產出，做為基因演算法中的適應值，同時也是本研究的績效指標。本研究亦以反應曲面法進行基因演算法之參數優化，並結合區域搜尋(Local Search)以提升演算法搜尋最佳解之能力。最後進行系統因子之實驗設計，分析各因子於系統的影響。本研究結合理論研究與實務應用，發展基因演算法結合區域搜尋，希望有效幫助分揀系統設施規劃之決策，進而提升物流配送中心之分揀能力。

Due to increasing demands in parcel transportation, it is becoming more and more important for logistics distribution centers to save time and money while completing parcel sortation operations. The role played by facility and layout planning in logistic distribution center has a great impact on the performance of parcel sortation. This study adopts a simulation optimization approach to solve the sortation conveyor layout problem, particularly the design of shortcuts in the sortation system that enhances processing capacity. Genetic algorithm with local search is proposed to find the optimal design of the sortation system. Simulation is used to evaluate the performance of algorithm results. Moreover, experimental design is used to optimize the parameter setting of the algorithm and the system. A real-world example is adopted to demonstrate the applicability of the proposed approach.

摘要 I
Abstract II
致謝 III
Contents IV
List of Tables VI
List of Figures VII
Chapter 1: Introduction 1
1.1 Background 1
1.2 Objectives 4
1.3 Research Process 4
1.4 Organization of Thesis 5
Chapter 2: Literature Review 7
2.1 Conveyor Layout Design Problem (CLDP) 7
2.2 Simulation Optimization 11
2.3 Genetic Algorithm (GA) 12
Chapter 3: Problem Definition 15
3.1 Alternative Design of Loop Sortation Conveyor 15
3.2 Problem Statement 17
3.3 Assumptions and Notations 20
3.4 Problem Formulations 21
Chapter 4: Methodology 23
4.1 Simulation Modeling 24
4.1.1 Facility Layout 24
4.1.2 Simulation Setting 26
4.1.3 Simulation Modeling 28
4.1.4 Verification and Validation 31
4.1.5 Steady-state Evaluation 34
4.2 Genetic Algorithm 36
4.2.1 Initial Population 36
4.2.2 Evaluation of Fitness Value 37
4.2.3 Selection 37
4.2.4 Crossover 39
4.2.5 Mutation 42
4.2.6 Local Search 42
Chapter 5: Simulation Evaluation 45
5.1 GA Parameter Setting Experimental Design 45
5.1.1 Result of GA Parameter Setting 47
5.1.2 Performance of GA by Substituting Best GA Parameter 50
5.2 Performance Comparison for Different Algorithms 56
5.3 Experiment Analysis of Simulation Model Parameters Setting 59
Chapter 6: Conclusion 65
Reference 66
Appendix 72
Appendix A: Central Composite Face-Centered Design of Three Factors 72
Appendix B: Layout Design Results of GA and LSGA 75
Appendix C: Greedy Algorithm 79
Appendix D: DOE of Simulation Model Parameters Setting 80

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