隨著電子商務的全球潮流，基於客戶期待更快速的運送服務，許多公司追求更高的包裹配送服務品質。許多公司致力於提高物流效率及降低成本，以提高其市場競爭力。包裹物流中心也因此扮演非常重要的角色，其功能在於將包裹分揀並裝載為滿載卡車。包裹物流中心有很大的優點，例如：降低運送時間及相關成本。另一方面，自動化分揀系統(ASS)有提高作業速度、同時分揀大量包裹、高可靠度的特色，也被大量用應用在許多供應鏈上。因此，本研究目標為應用排程手法，降低在自動分揀系統的處理時間以提高物流效率。
本研究為考量貨車既定離開時間於具捷徑之封閉式分揀系統之貨車排程問題，目標為最小化用以運送延誤的包裹的加班車輛成本及包裹放置於暫存區的持有成本的總和。如果包裹沒有及時裝載上預定班表的出貨卡車，則會利用加班車來運送包裹。此研究使用混合整數規劃模型對問題進行建模，此問題已被證明為NP困難問題。因此，提出了一個含有區域搜尋的自調式基因演算法（LSAGA）在十二個情境下解此問題，與其他演算法比較，並進行實驗設計。實驗結果顯示佈局、進貨貨車、出貨貨車及演算法，皆是影響目標函數值的顯著因子，且提出的演算法能穩定並有效得到較優的解。

With the global trend of e-commerce, companies pursue a higher quality of parcel delivery service since customers expect faster transportation in this fast-paced society. Several of them are committed to improving the efficiency of logistics and reducing operating costs and increasing their market competitiveness in the industry. The parcel distribution center thus plays a critical role in parcel delivery industries to sort and consolidate parcel flows to full truckloads. The benefit of the strategy is significant such as reducing transfer time and related costs. On the other hand, an automated sorting system (ASS) is also highly used in many supply chains with impressive characteristics like fast operation speed, large capacity, high reliability. The goal of this research is to apply the scheduling method to reduce the sorting time in an automated sorting system to improve distribution efficiency.
This study focuses on the truck scheduling problem with fixed outbound schedules in a closed-loop conveyor system with shortcuts. The objective is to minimize the costs of extra trucks used to deliver delayed parcels and holding cost of parcels at each shipping dock door. If a parcel fails to be loaded onto the pre-determined outbound trucks, an extra outbound truck will be used to deliver the parcel. The problem is modeled with a mixed integer nonlinear programming model. This problem is proven to be NP-hard in the strong sense. As a result, an adaptive genetic algorithm with local search (LSAGA) is developed to solve the problem under twelve scenarios and compared with other algorithms, and a full factorial design of experiment was conducted. The computational experiments show that four factors, layout, inbound truck, outbound truck, and algorithm are significant to the objective value, and the proposed algorithm can obtain high-quality solutions with more stability.

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 and Motivation 1
1.2 Research Objective 3
1.3 Organization of Thesis 4
Chapter 2 Literature Review 5
2.1 Automated Sortation System in Distribution Industry 5
2.2 Cross-dock Scheduling with Destination Mode of Product Allocation 6
2.3 Truck Scheduling with Fixed Outbound Schedule 7
Chapter 3 Problem Definition 11
3.1 Problem Statement 11
3.2 Notation and System Description 13
3.2.1 Notation 13
3.2.2 Composition of routes 14
3.2.3 Grouping for transshipment 16
3.2.4 Converging and Diverging Policy 17
3.3 Problem Formulation 19
Chapter 4 Methodology 23
4.1 Algorithm Framework 23
4.2 Steps of LSAGA 26
4.2.1 Define Problem Environment 26
4.2.2 Chromosome Representation 26
4.2.3 Generate Initial Population 28
4.2.4 Fitness Value Evaluation 28
4.2.5 Selection 32
4.2.6 Crossover 32
4.2.7 Mutation 36
4.2.8 Local Search 37
4.2.9 Replacement 41
4.2.10 Termination Criteria 41
4.2.11 Adaptive Probabilities of Crossover and Mutation 42
Chapter 5 Computational Study 44
5.1 Design of Experiments 44
5.1.1 Factors of DOE 44
5.1.2 Results of DOE 50
5.2 Performance Comparison of Different Types of GAs 53
5.2.1 Objective Value Comparison 53
5.2.2 CPU Time 57
5.2.3 Convergent Condition 58
5.2.4 Quality Ratio 60
Chapter 6 Conclusion 62
Reference 64
Appendix 66
Appendix A: Objective value boxplot of each scenario 66
Appendix B: Convergence plot of each scenario 68

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