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Scheduling External Trucks Appointments in Container Terminals to Minimize Cost and Truck Turnaround Times

Author

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  • Ahmed M. Abdelmagid

    (Department of Industrial and Manufacturing Engineering, Egypt-Japan University of Science and Technology (E-JUST), Alexandria 21934, Egypt
    Production Engineering Department, Faculty of Engineering, Alexandria University, Alexandria 21544, Egypt)

  • Mohamed Gheith

    (Department of Industrial and Manufacturing Engineering, Egypt-Japan University of Science and Technology (E-JUST), Alexandria 21934, Egypt
    Production Engineering Department, Faculty of Engineering, Alexandria University, Alexandria 21544, Egypt)

  • Amr Eltawil

    (Department of Industrial and Manufacturing Engineering, Egypt-Japan University of Science and Technology (E-JUST), Alexandria 21934, Egypt)

Abstract

Background: Scheduling the arrival of external trucks in container terminals is a critical operational decision that faces both terminal managers and trucking companies. This issue is crucial for both stakeholders since the random arrival of trucks causes congestion in the terminals and extended delays for the trucks. The objective of scheduling external truck appointments is not only to control the workload inside the terminal and the costs resulting from the excessive waiting times of trucks but also, to reduce the truck turnaround time. Methods: A binary programming model was proposed to minimize the waiting time cost, demurrage cost, and container delivery cost. Moreover, a sensitivity analysis was performed to compare various scenarios in terms of cost and to study to what extent the workload level is affected. The mathematical model was solved using Gurobi© 8.1.0 software. Results: 30 instances found in the literature were solved and evaluated in terms of the objective function value (i.e., cost) and truck turnaround time before and after controlling the workload inside the container terminal using the new proposed constraint. Conclusions: The obtained results showed a better distribution of the terminal workload, as well as a lower truck turnaround time that reduces the total cost.

Suggested Citation

  • Ahmed M. Abdelmagid & Mohamed Gheith & Amr Eltawil, 2022. "Scheduling External Trucks Appointments in Container Terminals to Minimize Cost and Truck Turnaround Times," Logistics, MDPI, vol. 6(3), pages 1-22, July.
    • Handle: RePEc:gam:jlogis:v:6:y:2022:i:3:p:45-:d:857685
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    References listed on IDEAS

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    1. Pierre, Cariou & Francesco, Parola & Theo, Notteboom, 2019. "Towards low carbon global supply chains: A multi-trade analysis of CO2 emission reductions in container shipping," International Journal of Production Economics, Elsevier, vol. 208(C), pages 17-28.
    2. Xiaoju Zhang & Qingcheng Zeng & Zhongzhen Yang, 2019. "Optimization of truck appointments in container terminals," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 21(1), pages 125-145, March.
    3. Phan, Mai-Ha & Kim, Kap Hwan, 2016. "Collaborative truck scheduling and appointments for trucking companies and container terminals," Transportation Research Part B: Methodological, Elsevier, vol. 86(C), pages 37-50.
    4. Torkjazi, Mohammad & Huynh, Nathan & Shiri, Samaneh, 2018. "Truck appointment systems considering impact to drayage truck tours," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 116(C), pages 208-228.
    5. Katta G. Murty & Yat-wah Wan & Jiyin Liu & Mitchell M. Tseng & Edmond Leung & Kam-Keung Lai & Herman W. C. Chiu, 2005. "Hongkong International Terminals Gains Elastic Capacity Using a Data-Intensive Decision-Support System," Interfaces, INFORMS, vol. 35(1), pages 61-75, February.
    6. Sanghyuk Yi & Bernd Scholz-Reiter & Taehoon Kim & Kap Hwan Kim, 2019. "Scheduling appointments for container truck arrivals considering their effects on congestion," Flexible Services and Manufacturing Journal, Springer, vol. 31(3), pages 730-762, September.
    7. Ahmed Mohssen Abdelmagid & Mohamed Samir Gheith & Amr Bahgat Eltawil, 2022. "A comprehensive review of the truck appointment scheduling models and directions for future research," Transport Reviews, Taylor & Francis Journals, vol. 42(1), pages 102-126, January.
    8. Zehendner, Elisabeth & Feillet, Dominique, 2014. "Benefits of a truck appointment system on the service quality of inland transport modes at a multimodal container terminal," European Journal of Operational Research, Elsevier, vol. 235(2), pages 461-469.
    9. Lu Zhen & Shucheng Yu & Shuaian Wang & Zhuo Sun, 2019. "Scheduling quay cranes and yard trucks for unloading operations in container ports," Annals of Operations Research, Springer, vol. 273(1), pages 455-478, February.
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