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Modeling and Design of Container Terminal Operations

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  • Roy, D.
  • de Koster, M.B.M.

Abstract

Design of container terminal operations is complex because multiple factors affect the operational perfor- mance. These factors include: topological constraints, a large number of design parameters and settings, and stochastic interactions that interplay among the quayside, vehicle transport, and stackside processes. In this research, we propose new integrated queuing network models for rapid design evaluation of container terminals with Automated Lift Vehicles (ALVs) and Automated Guided Vehicles (AGVs). These models offer the flexibility to analyze alternate design variations and develop insights. For instance, the effect of alternate vehicle dwell point policy is analyzed using state-dependent queues, whereas the efficient terminal layout is determined using variation in the service time expressions at the stations. Further, using embedded Markov chain analysis, we develop an approximate procedure for analyzing bulk container arrivals. These models form the building block for design and analysis of large-scale terminal operations. We test the model efficacy using detailed in-house simulation experiments and real-terminal validation by partnering with an external party.

Suggested Citation

  • Roy, D. & de Koster, M.B.M., 2014. "Modeling and Design of Container Terminal Operations," ERIM Report Series Research in Management ERS-2014-008-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    • Handle: RePEc:ems:eureri:51536
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    References listed on IDEAS

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    Cited by:

    1. Vibhuti Dhingra & Debjit Roy & René B. M. Koster, 2017. "A cooperative quay crane-based stochastic model to estimate vessel handling time," Flexible Services and Manufacturing Journal, Springer, vol. 29(1), pages 97-124, March.
    2. Debjit Roy & Akash Gupta & René B.M. De Koster, 2016. "A non-linear traffic flow-based queuing model to estimate container terminal throughput with AGVs," International Journal of Production Research, Taylor & Francis Journals, vol. 54(2), pages 472-493, January.
    3. Kumawat, Govind Lal & Roy, Debjit & De Koster, René & Adan, Ivo, 2021. "Stochastic modeling of parallel process flows in intra-logistics systems: Applications in container terminals and compact storage systems," European Journal of Operational Research, Elsevier, vol. 290(1), pages 159-176.
    4. Barenji, Ali Vatankhah & Wang, W.M. & Li, Zhi & Guerra-Zubiaga, David A., 2019. "Intelligent E-commerce logistics platform using hybrid agent based approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 126(C), pages 15-31.
    5. Egor PLOTNIKOV & Aleksandr RAKHMANGULOV, 2021. "Modeling China'S Dry Port Cooperation In Supply Chains," Transport Problems, Silesian University of Technology, Faculty of Transport, vol. 16(3), pages 89-103, September.
    6. Dmitri Muravev & Aleksandr Rakhmangulov & Hao Hu & Hengshuo Zhou, 2019. "The Introduction to System Dynamics Approach to Operational Efficiency and Sustainability of Dry Port’s Main Parameters," Sustainability, MDPI, vol. 11(8), pages 1-21, April.

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    Keywords

    container terminal; intra-terminal transport; design decisions; queuing models;
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