隨著物流配送業的成長，包裹配送需求也不斷增加，本研究致力於提高包裹中央整合中心（CPCT）的處理效率，目標為找到最佳的卡車入庫排程以最小化帶有捷徑之封閉式分揀系統的最大完工時間。然而，由於分揀系統上多增加了捷徑，其網絡複雜度提高，且於捷徑上的擁塞行為無法通過公式量化。為了解決此難題，本研究提出了一種基於自適應混合基因離散粒子群最佳化（AGDPSO）的模擬最佳化（SO）模型。考慮到所開發模型的計算複雜性，將使用AGDPSO確定最佳計劃，並進行分析以驗證所提出的模擬最佳化模型。在擬議的AGDPSO中，採用時變加速度係數（time-varying acceleration coefficients）來放大全局和局部最佳解的效益，從而在搜索空間的開發與利用之間取得更好的平衡，結果表明ADPSO可以更有效率且穩定的產生最佳解。此外本研究針對不同的卡車輛及包裹目的地分布設計了決定CPCT配置決策之實驗，其中卡車自動卸貨模式已被證明可以大大提高系統效率；以及當堵塞現象嚴重時，可以有條件的減少進入捷徑的包裹量，當網路流量達到平衡可以有效增加系統整體效能。

Facing the growing needs of in parcel delivery, this research is dedicated to improving the operational efficiency in a central parcel consolidation terminals (CPCT). The objective of this study is to find the optimal schedule of inbound trucks to the inbound docks by minimizing the makespan of the sorting process in a closed-loop automated sorting conveyor (ASC) with shortcuts. Furthermore, with the addition of shortcuts to the closed-loop system, the complexity of the network has increased sharply, and the congestion behavior on the conveyors cannot be quantified by the equation, which increases the difficulty of this research. To solve the congestion issue, a simulation-optimization (SO) model based on an adaptive genetic-based discrete particle swarm optimization algorithm (AGDPSO) is proposed to solve the problem. Given the computational complexity of the developed model, AGDPSO is used to determine the optimal schedule, and some further analysis would be performed to verify the proposed simulation-optimization model. In the proposed AGDPSO, time-varying acceleration coefficients are adopted to amplifying the effectiveness of the global and local best solution to take a better balance between exploration and exploitation of the searching space. The result showed that ADPSO could perform the solution better and more stable. Furthermore, decisions and design of CPCT are also considered; among them, the automatic truck unloading mode has been proven to improve the system efficiency significantly. Additionally, when the congestion is severe, it can conditionally reduce the number of packages entering the shortcut. When the network flow reaches a balance, it can effectively increase the overall system performance.

摘要 I
Abstract II
致謝 III
Contents IV
List of Tables VI
List of Figures VII
1 Introduction 1
2 Literature Review 6
3 Problem Definition 11
3.1 Problem Statement & Assumption 11
3.2 Problem Formulation & System Description 13
4 Methodology 20
4.1 Simulation Modeling 20
4.1.1 Simulation Assumption 20
4.1.2 Commands of Pulling Rules of shortcuts 23
4.2 Adaptive Genetic-based Discrete Particle Swarm Optimization 23
4.2.1 Genetic-based Discrete Particle Swarm Optimization 24
4.2.2 Time-varying Acceleration Coefficients (TVAC) 28
5 Results & Discussion 29
5.1 Design of Experiments 29
5.2 Data analysis of different PSOs 33
5.3 Result of DOE 34
6 Conclusion 49
Reference 51
Appendix 57
Appendix A: Research Method 57
Appendix B: Process Flow of the Whole Working Process in a CPCT 59
Appendix C: ADPSO Parameter Optimization Experiment 60
Appendix D: Fitness Value Weighted Determination Experiment 62
Appendix E: Detailed Statistical Analysis Data of DOE 64

Anjana, V., Sridharan, R., & Kumar, P. R. (2019). Metaheuristics for solving a multi-objective flow shop scheduling problem with sequence-dependent setup times. Journal of Scheduling, 1-21.
Apte, U. M., & Viswanathan, S. (2000). Effective cross docking for improving distribution efficiencies. International Journal of Logistics, 3(3), 291-302.
Azadivar, F., & Wang, J. (2000). Facility layout optimization using simulation and genetic algorithms. International Journal of Production Research, 38(17), 4369-4383.
Bodnar, P., de Koster, R., & Azadeh, K. (2015). Scheduling trucks in a cross-dock with mixed service mode dock doors. Transportation Science, 51(1), 112-131.
Boysen, N. (2010). Truck scheduling at zero-inventory cross docking terminals. Computers & Operations Research, 37(1), 32-41.
Boysen, N., Briskorn, D., Fedtke, S., & Schmickerath, M. (2018). Automated sortation conveyors: A survey from an operational research perspective. European Journal of Operational Research, 276(3), 796-815.
Boysen, N., Briskorn, D., & Tschöke, M. (2013). Truck scheduling in cross-docking terminals with fixed outbound departures. OR spectrum, 35(2), 479-504.
Boysen, N., Fedtke, S., & Weidinger, F. (2017). Truck scheduling in the postal service industry. Transportation Science, 51(2), 723-736.
Boysen, N., & Fliedner, M. (2010). Cross dock scheduling: Classification, literature review and research agenda. Omega, 38(6), 413-422.
Boysen, N., Fliedner, M., & Scholl, A. (2010). Scheduling inbound and outbound trucks at cross docking terminals. OR spectrum, 32(1), 135-161.
Briskorn, D., Emde, S., & Boysen, N. (2017). Scheduling shipments in closed-loop sortation conveyors. Journal of Scheduling, 20(1), 25-42.
Bureau of Transportation Statistics, & Federal Highway Administration. (2018). Freight Analysis Framework Data Tabulation. Federal Highway Administration Retrieved from http://faf.ornl.gov
Cai, Q., Gong, M., Ma, L., Ruan, S., Yuan, F., & Jiao, L. (2015). Greedy discrete particle swarm optimization for large-scale social network clustering. Information Sciences, 316, 503-516.
Carson, Y., & Maria, A. (1997). Simulation optimization: methods and applications. Paper presented at the 29th conference on Winter simulation, Atlanta, Georgia, USA
Chen, F., & Lee, C. Y. . (2009). Minimizing the makespan in a two-machine cross-docking flow shop problem. European Journal of Operational Research, 193(1), 59-72.
Chen, F., & Song, K. (2009). Minimizing makespan in two-stage hybrid cross docking scheduling problem. Computers & Operations Research, 36(6), 2066-2073.
Chen, J. C., Chen, T. L., Ou, T. C., & Lee, Y. H. (2019). Adaptive Genetic Algorithm for Parcel Hub Scheduling Problem with Shortcuts in Closed-Loop Sortation System. Computers & Industrial Engineering, 138, 106114.
Chen, J. C., Huang, C. F., Chen, T. L., & Lee, Y. H. (2019). Solving a Sortation Conveyor Layout Design Problem with Simulation-optimization Approach. Paper presented at the 2019 IEEE 6th International Conference on Industrial Engineering and Applications (ICIEA), Tokyo, Japan.
Chen, X., Chau, V., Xie, P., Sterna, M., & Błażewicz, J. (2017). Complexity of late work minimization in flow shop systems and a particle swarm optimization algorithm for learning effect. Computers & Industrial Engineering, 111, 176-182.
Clausen, U., Diekmann, D., Baudach, J., Kaffka, J., & Pöting, M. (2015). Improving parcel transshipment operations-impact of different objective functions in a combined simulation and optimization approach. Paper presented at the 2015 Winter Simulation Conference (WSC).
Eberhart, R., & Kennedy, J. (1995). A new optimizer using particle swarm theory. Paper presented at the Sixth International Symposium on Micro Machine and Human Science.
Eberhart, R., Simpson, P., & Dobbins, R. (1996). Computational intelligence PC tools. Boston, MA: Academic Press Professional, Inc.
Heidari, F., Zegordi, S. H., & Tavakkoli-Moghaddam, R. (2018). Modeling truck scheduling problem at a cross-dock facility through a bi-objective bi-level optimization approach. Journal of Intelligent Manufacturing, 29(5), 1155-1170.
Hosseini-Nasab, H., Fereidouni, S., Ghomi, S. M. T. F., & Fakhrzad, M. B. (2018). Classification of facility layout problems: a review study. The International Journal of Advanced Manufacturing Technology, 94(1-4).
Hou, S. (2013). Distribution center logistics optimization based on simulation. Journal of Applied Sciences, Engineering and Technology, 5(21), 5107-5111.
Jin, N., & Rahmat-Samii, Y. (2010). Hybrid real-binary particle swarm optimization (HPSO) in engineering electromagnetics. IEEE Transactions on Antennas and Propagation, 58(12), 3786-3794.
Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization (PSO). Paper presented at the International Conference on Neural Networks, Perth, Australia.
Krause, J., Cordeiro, J., Parpinelli, R. S., & Lopes, H. S. (2013). A survey of swarm algorithms applied to discrete optimization problems. In Swarm Intelligence and Bio-Inspired Computation (Vol. 169-191): Elsevier.
Larbi, R., Alpan, G., Baptiste, P., & Penz, B. (2011). Scheduling cross docking operations under full, partial and no information on inbound arrivals. Computers & Operations Research, 38(6), 889-900.
Levin, M. W. (2017). Congestion-aware system optimal route choice for shared autonomous vehicles. Transportation Research Part C: Emerging Technologies, 82, 229-247.
Lin, M. Y., Chin, K. S., & Tsui, K. L., & Wong, T. C. (2016). Genetic based discrete particle swarm optimization for Elderly Day Care Center timetabling. Computers & Operations Research, 65, 125-138.
Lope, H. S., & Coelho, L. S. (2005). Particle swarn optimization with fast local search for the blind traveling salesman problem. Paper presented at the Fifth International Conference on Hybrid Intelligent Systems (HIS'05).
McWilliams, D. L. (2009). Genetic-based scheduling to solve the parcel hub scheduling problem. Computers & Industrial Engineering, 56(4), 1607-1616.
McWilliams, D. L. (2010). A genetic based scheduling algorithm for the PHSP with unequal batch size inbound trailers. Journal of Industrial and Systems Engineering, 4(3), 167-182.
McWilliams, D. L., & McBride, M. E. (2012). A beam search heuristics to solve the parcel hub scheduling problem. Computers & Industrial Engineering, 62(4), 1080-1092.
McWilliams, D. L., Stanfield, P. M., & Geiger, C. D. (2005). The parcel hub scheduling problem: A simulation-based solution approach. Computers & Industrial Engineering, 49(3), 393-412.
McWilliams, D. L., Stanfield, P. M., & Geiger, C. D. (2008). Minimizing the completion time of the transfer operations in a central parcel consolidation terminal with unequal-batch-size inbound trailers. Computers & Industrial Engineering, 54(4), 709-720.
Mohtashami, A. (2015). A novel dynamic genetic algorithm-based method for vehicle scheduling in cross docking systems with frequent unloading operation. Computers & Industrial Engineering, 90(221-240).
Pan, Q. K., Tasgetiren, M. F., & Liang, Y. C. (2006). A discrete particle swarm optimization algorithm for the permutation flowshop sequencing problem with makespan criterion. Paper presented at the International Conference on Innovative Techniques and Applications of Artificial Intelligence, London.
Poeting, M., Rau, J., Clausen, U., & Schumacher, C. (2017). A combined simulation optimization framework to improve operations in parcel logistics. Paper presented at the 2017 Winter Simulation Conference.
Rameshkumar, K., Suresh, R. K., & Mohanasundaram, K. M. (2005). Discrete particle swarm optimization (DPSO) algorithm for permutation flowshop scheduling to minimize makespan. Paper presented at the International Conference on Natural Computation Berlin, Heidelberg.
Ratnaweera, A., Halgamuge, S. K., & Watson, H. C. (2004). Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Transactions on evolutionary computation, 8(3), 240-255.
Sengupta, S., Basak, S., & Peters, R. A. (2019). Particle Swarm Optimization: A survey of historical and recent developments with hybridization perspectives. Machine Learning and Knowledge Extraction, 1, 157-191.
Shen, M., Zhan, Z. H., Chen, W. N., Gong, Y. J., & Zhang, J., & Li, Y. (2014). Bi-velocity discrete particle swarm optimization and its application to multicast routing problem in communication networks. IEEE Transactions on Industrial Electronics, 61(12), 7141-7151.
Subramanian, D., Pekny, J. F., & Reklaitis, G. V. (2000). A simulation—optimization framework for addressing combinatorial and stochastic aspects of an R&D pipeline management problem. Computers & Chemical Engineering, 24(2-7), 1005-1011.
Vincent, F. Y., Redi, A. P., Jewpanya, P., & Gunawan, A. (2019). Selective discrete particle swarm optimization for the team orienteering problem with time windows and partial scores. Computers & Industrial Engineering, 138, 106084.
Yang, B., Chen, Y., & Zhao, Z. (2007). A hybrid evolutionary algorithm by combination of PSO and GA for unconstrained and constrained optimization problems. Paper presented at the IEEE International Conference on Control and Automation, Guangzhou, China.
Yperman, I., Logghe, S., & Immers, B. (2005). The link transmission model: An efficient implementation of the kinematic wave theory in traffic networks. Paper presented at the 10th EWGT Meeting, Poznan Poland.
Yu, W., & Egbelu, P. J. (2008). Scheduling of inbound and outbound trucks in cross docking systems with temporary storage. European Journal of Operational Research, 184(1), 377-396.
Yu, Y., Wang, X., Zhong, R. Y., & Huang, G. Q. (2017). E-commerce logistics in supply chain management: Implementations and future perspective in furniture industry. Industrial Management & Data Systems, 117(10), 2263-2286.
Zhan, Z. H., Zhang, J., Li, Y., & Chung, H. S. H. (2009). Adaptive particle swarm optimization. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 39(6), 1362-1381.