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Coordinated optimization of equipment operations in a container terminal

Author

Listed:
  • T. Jonker

    (Delft University of Technology
    TBA Group)

  • M. B. Duinkerken

    (Delft University of Technology)

  • N. Yorke-Smith

    (Delft University of Technology
    American University of Beirut)

  • A. Waal

    (TBA Group)

  • R. R. Negenborn

    (Delft University of Technology)

Abstract

Increasing international maritime transport drives the need for efficient container terminals. The speed at which containers can be processed through a terminal is an important performance indicator. In particular, the productivity of the quay cranes (QCs) determines the performance of a container terminal; hence QC scheduling has received considerable attention. This article develops a comprehensive model to represent the waterside operations of a container terminal. Waterside operations comprise single and twinlift handling of containers by QCs, automated guided vehicles and yard cranes. In common practice, an uncoordinated scheduling heuristic is used to dispatch the equipment operating on a terminal. Here, uncoordinated means that the different machines that operate in the container terminal seek optimal productivity solely considering their own respective stage. By contrast, our model provides a coordinated schedule in which operations of all terminal equipment can be considered at once to achieve productivity closer to the QC optimal. The model takes the form of a hybrid flow shop (HFS) with novel features for bi-directional flows and job pairing. The former enables jobs to move freely through the HFS in both directions; the latter constrains certain jobs to be performed simultaneously by a single machine. We solve the coordinated model by means of a tailored simulated annealing (SA) algorithm that balances solution quality and computational time. We empirically study time-bounded variants of SA and compare them with a branch-and-bound algorithm. We show that our approach can produce coordinated schedules for a terminal with up to eight QCs in near real time.

Suggested Citation

  • T. Jonker & M. B. Duinkerken & N. Yorke-Smith & A. Waal & R. R. Negenborn, 2021. "Coordinated optimization of equipment operations in a container terminal," Flexible Services and Manufacturing Journal, Springer, vol. 33(2), pages 281-311, June.
  • Handle: RePEc:spr:flsman:v:33:y:2021:i:2:d:10.1007_s10696-019-09366-3
    DOI: 10.1007/s10696-019-09366-3
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    References listed on IDEAS

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    3. Zhou, Chenhao & Yuan, Mengxue & Zhang, Jingwen & Zhang, Wei, 2024. "A tree search algorithm for uncertainty-considered consecutive discharging and loading operations between ship and offshore platform," European Journal of Operational Research, Elsevier, vol. 315(2), pages 729-749.
    4. Meixian Jiang & Fangzheng Ma & Yuqiu Zhang & Shuying Lv & Zhi Pei & Guanghua Wu, 2024. "Collaborative Scheduling Optimization of Container Port Berths and Cranes under Low-Carbon Environment," Sustainability, MDPI, vol. 16(7), pages 1-26, April.
    5. Michał Kłodawski & Roland Jachimowski & Norbert Chamier-Gliszczyński, 2024. "Analysis of the Overhead Crane Energy Consumption Using Different Container Loading Strategies in Urban Logistics Hubs," Energies, MDPI, vol. 17(5), pages 1-24, February.
    6. Meixian Jiang & Jiajia Feng & Jian Zhou & Lin Zhou & Fangzheng Ma & Guanghua Wu & Yuqiu Zhang, 2023. "Multi-Terminal Berth and Quay Crane Joint Scheduling in Container Ports Considering Carbon Cost," Sustainability, MDPI, vol. 15(6), pages 1-20, March.

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