簡易檢索 / 詳目顯示

回結果列表
研究生： 范國宸
Kuo-Chen Fan
論文名稱： 智動化車輛倉儲系統之績效研究
Performance Studies of Robotic Vehicle Warehousing Systems
指導教授： 郭伯勳
Po-Hsun Kuo
口試委員: 喻奉天
Vincent F. Yu
曹譽鐘
Yu-Chung Tsao
學位類別： 碩士
Master
系所名稱： 管理學院 - 工業管理系
Department of Industrial Management
論文出版年： 2016
畢業學年度： 104
語文別： 英文
論文頁數： 74
中文關鍵詞： 物料搬運系統 、智動化車輛 、等候網路 、績效評估 、模擬
外文關鍵詞： Material handling system, Robotic vehicle, Queueing network, Simulation, Performance evaluation
相關次數： 點閱：208  下載：13 
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報
  •  上一筆

    • 訂單揀貨作業包含了大量非生產性的途程時間，但唯一具生產力的揀貨步驟卻只佔了整個作業流程中的一小部分。為了節省人力成本，員工只進行揀貨之操作，而不同位置之間的貨品運輸則使用智動化車輛同步執行之。這份研究提供了以等候網路為基礎的分析模型，根據訂單到達模式、系統配置及智動化車輛與揀貨人員之數量，對車輛及揀貨員的績效進行評估。模擬實驗在不同系統設計因子下被執行，以驗證此等候模型並預測系統之性能。依據客戶訂單及所需的系統性能，這個模型可以被擴展來衡量系統運作之成本。

    A large part of order picking operations involves nonproductive travel time, but the only productive picking process plays a small part in the entire procedures. To save labor costs, labors are only employed in the item picking process, and the robotic vehicles are deployed for concurrent transport between positions. This study provides analytical models based on queueing network to evaluate the performance of robotic vehicles and pickers according to the order arrival pattern, system configuration and number of vehicles and pickers. Simulation experiments are executed to validate the queueing models and predict system performance based on different system design factors. This model can be extended to estimate system operation costs depending on customer orders and required system performance.

    摘要 i ABSTRACT ii TABLE OF CONTENTS iii LIST OF FIGURES v LIST OF TABLES vi CHAPTER 1 INTRODUCTION 1 1.1 Research motivation and purpose 3 1.2 Research scopes and constraints 3 1.3 Research methodology 4 1.4 Research structure 4 CHAPTER 2 LITERATURE REVIEW 6 2.1 Different order picking systems 7 2.1.1 Conventional warehouses 7 2.1.2 Automated warehouses 9 2.2 Robotic vehicle warehousing systems 12 2.3 Queueing networks 17 CHAPTER 3 ANALYTICAL MODELS 19 3.1 Travel time estimation 20 3.2 Closed queueing network 27 3.2.1 Convolution Algorithm 29 3.2.2 Marie’s Method 31 3.3 Open queueing system 36 3.4 A small example 40 3.5 Extensions 45 CHAPTER 4 NUMERICAL EXAMPLES 48 4.1 Design of experiments 48 4.2 Validation and explanation 49 4.3 Approximate method investigation 57 4.4 Interesting insights 59 4.5 Modification for the service time variation 61 CHAPTER 5 CONCLUSIONS 64 5.1 Conclusions 64 5.2 Future research 66

    Akyildiz, I. F., & Sieber, A. (1988). Approximate Analysis of Load Dependent General Queueing Networks. IEEE Transactions on Software Engineering, 14(11), 1537-1545.
    Allen, A. O. (1990). Probability, Statistics, and Queuing Theory (2 ed.). New York: Academic Press.
    Bolch, G., Greiner, S., de Meer, H., & Trivedi, K. S. (2006). Queueing Networks and Markov Chains: Modeling and Performance Evaluation With Computer Science Applications (2 ed.): John Wiley & Sons.
    Bozer, Y. A., & White, J. A., Sr. (1994). Travel-time models for automated storage/retrieval systems. IIE Transactions (Institute of Industrial Engineers), 16(4), 329-338.
    Buzen, J. P. (1973). Computational Algorithms for Closed Queueing Networks with Exponential Servers. Communications of the ACM, 16(9), 527-531.
    Chandy, K. M., Herzog, U., & Woo, L. (1975). Parametric Analysis of Queuing Networks. IBM Journal of Research and Development, 19(1), 36-42.
    Chew, E. P., & Tang, L. C. (1999). Travel time analysis for general item location assignment in a rectangular warehouse. European Journal of Operational Research, 112(3), 582-597.
    Cosmetatos, G. P. (1976). Some Approximate Equilibrium Results for The Multi-server Queue (M/G/r). Operational Research Quarterly, 27(3), 615-620.
    Curry, G. L., & Feldman, R. M. (2011). Manufacturing systems modeling and analysis (2 ed.): Springer Berlin Heidelberg.
    D'Andrea, R., & Wurman, P. (2008). Future challenges of coordinating hundreds of autonomous vehicles in distribution facilities. Paper presented at the 2008 IEEE International Conference on Technologies for Practical Robot Applications, TePRA.
    De Koster, M. E. M. (2012). Warehouse Assessment in a Single Tour. In R. Manzini (Ed.), Warehousing in the Global Supply Chain (pp. 457-473): Springer-Verlag London.
    De Koster, R., Le-Duc, T., & Roodbergen, K. J. (2007). Design and control of warehouse order picking: A literature review. European Journal of Operational Research, 182(2), 481-501.
    Ekren, B. Y., & Heragu, S. S. (2010). Approximate analysis of load-dependent generally distributed queuing networks with low service time variability. European Journal of Operational Research, 205(2), 381-389.
    Enright, J. J., & Wurman, P. R. (2011). Optimization and coordinated autonomy in mobile fulfillment systems. Paper presented at the AAAI Workshop - Technical Report.
    Gagliardi, J. P., Renaud, J., & Ruiz, A. (2012). Models for automated storage and retrieval systems: A literature review. International Journal of Production Research, 50(24), 7110-7125.
    Gu, J., Goetschalckx, M., & McGinnis, L. F. (2007). Research on warehouse operation: A comprehensive review. European Journal of Operational Research, 177(1), 1-21.
    Kobayashi, H. (1974). Application of the Diffusion Approximation to Queueing Networks - I: Equilibrium Queue Distributions. J Assoc Comput Mach, 21(2), 316-328.
    Kuo, P. H., Krishnamurthy, A., & Malmborg, C. J. (2007). Design models for unit load storage and retrieval systems using autonomous vehicle technology and resource conserving storage and dwell point policies. Applied Mathematical Modelling, 31(10), 2332-2346.
    Lagershausen, S. (2013). Performance Analysis of Closed Queueing Networks: Springer-Verlag Berlin Heidelberg.
    Malmborg, C. J. (2002). Conceptualizing tools for autonomous vehicle storage and retrieval systems. International Journal of Production Research, 40(8), 1807-1822.
    Marie, R. A. (1979). An Approximate Analytical Method for General Queueing Networks. IEEE Transactions on Software Engineering, SE-5(5), 530-538.
    Marie, R. A. (1980). Calculating Equilibrium Probabilities for λ(n)/Ck/1/N queues, Proceedings of the 1980 international symposium on Computer performance modelling, measurement and evaluation (pp. 117-125). Toronto, Ontario, Canada: ACM.
    Pan, J. C. H., Wu, M. H., & Chang, W. L. (2014). A travel time estimation model for a high-level picker-to-part system with class-based storage policies. European Journal of Operational Research, 237(3), 1054-1066.
    Parikh, P. J., & Meller, R. D. (2010). A travel-time model for a person-onboard order picking system. European Journal of Operational Research, 200(2), 385-394.
    Reiser, M., & Kobayashi, H. (1974). Accuracy of the Diffusion Approximation for Some Queuing Systems. IBM Journal of Research and Development, 18(2), 110-124.
    Reiser, M., & Lavenberg, S. S. (1980). Mean-Value Analysis of Closed Multichain Queuing Networks. Journal of the ACM, 27(2), 313-322.
    Roodbergen, K. J., & Vis, I. F. A. (2009). A survey of literature on automated storage and retrieval systems. European Journal of Operational Research, 194(2), 343-362.
    Roy, D., Krishnamurthy, A., Heragu, S., & Malmborg, C. (2015). Queuing models to analyze dwell-point and cross-aisle location in autonomous vehicle-based warehouse systems. European Journal of Operational Research, 242(1), 72-87.
    Tijms, H. (1986). Stochastic Modelling and Analysis: A Computational Approach (1 ed.): Wiley.
    Wurman, P. R., D'Andrea, R., & Mountz, M. (2008). Coordinating hundreds of cooperative, autonomous vehicles in warehouses. AI Magazine, 29(1), 9-19.

    QR CODE