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A cooperative quay crane-based stochastic model to estimate vessel handling time

Author

Listed:
  • Vibhuti Dhingra

    (University of British Columbia)

  • Debjit Roy

    (Indian Institute of Management)

  • René B. M. Koster

    (Erasmus University)

Abstract

Having a good estimate of a vessel’s handling time is essential for planning and scheduling container terminal resources, such as berth positions, quay cranes (QCs) and transport vehicles. However, estimating the expected vessel handling time is not straightforward , because it depends on vessel characteristics, resource allocation decisions, and uncertainties in terminal processes. To estimate the expected vessel handling time, we propose a two-level stochastic model. The higher level model consists of a continuous-time Markov chain (CTMC) that captures the effect of QC assignment and scheduling on vessel handling time . The lower level model is a multi-class closed queuing network that models the dynamic interactions among the terminal resources and provides an estimate of the transition rate input parameters to the higher level CTMC model. We estimate the expected vessel handling times for several container load and unload profiles and discuss the effect of terminal layout parameters and crane service time variabilities on vessel handling times. From numerical experiments, we find that by having QCs cooperate, the vessel handling times are reduced by up to 15 %. The vessel handling time is strongly dependent on the variation in the QC service time and on the vehicle travel path topology.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:flsman:v:29:y:2017:i:1:d:10.1007_s10696-015-9225-3
    DOI: 10.1007/s10696-015-9225-3
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. Roy, Debjit & van Ommeren, Jan-Kees & de Koster, René & Gharehgozli, Amir, 2022. "Modeling landside container terminal queues: Exact analysis and approximations," Transportation Research Part B: Methodological, Elsevier, vol. 162(C), pages 73-102.
    3. Zhang, Xiaoju & Zeng, Qingcheng & Sheu, Jiuh-Biing, 2019. "Modeling the productivity and stability of a terminal operation system with quay crane double cycling," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 122(C), pages 181-197.
    4. Li, Xinyan & Xie, Chi & Bao, Zhaoyao, 2022. "A multimodal multicommodity network equilibrium model with service capacity and bottleneck congestion for China-Europe containerized freight flows," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).

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