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Simulation-Based Optimization of Truck Appointment Systems in Container Terminals: A Dual Transactions Approach with Improved Congestion Factor Representation

Author

Listed:
  • Davies K. Bett

    (Department of Industrial and Manufacturing Engineering, Egypt-Japan University of Science and Technology, New Borg El-Arab, Alexandria 21934, Egypt)

  • Islam Ali

    (Department of Industrial and Manufacturing Engineering, Egypt-Japan University of Science and Technology, New Borg El-Arab, Alexandria 21934, Egypt
    Production Engineering Department, Alexandria University, Alexandria 21544, Egypt)

  • Mohamed Gheith

    (Department of Industrial and Manufacturing Engineering, Egypt-Japan University of Science and Technology, New Borg El-Arab, Alexandria 21934, Egypt
    Production Engineering Department, Alexandria University, Alexandria 21544, Egypt)

  • Amr Eltawil

    (Department of Industrial and Manufacturing Engineering, Egypt-Japan University of Science and Technology, New Borg El-Arab, Alexandria 21934, Egypt
    Production Engineering Department, Alexandria University, Alexandria 21544, Egypt)

Abstract

Background : Container terminals (CTs) have constantly administered truck appointment systems (TASs) to effectively accomplish the planning and scheduling of drayage operations. However, since the operations in the gate and yard area of a CT are stochastic, there is a need to incorporate uncertainty during the development and execution of appointment schedules. Further, the situation is complicated by disruptions in the arrival of external trucks (ETs) during transport, which results in congestion at the port due to unbalanced arrivals. In the wake of Industry 4.0, simulation can be used to test and investigate the present CT configurations for possible improvements. Methods : This paper presents a simulation optimization (SO) and simulation-based optimization (SBO) iteration framework which adopts a dual transactions approach to minimize the gate operation costs and establish the relationship between productivity and service time while considering congestion in the yard area. It integrates the use of both the developed discrete event simulation (DES) and a mixed integer programming (MIP) model from the literature to iteratively generate an improved schedule. The key performance indicators considered include the truck turnaround time (TTT) and the average time the trucks spend at each yard block (YB). The proposed approach was verified using input parameters from the literature. Results : The findings from the SO experiments indicate that, at most, two gates were required to be opened at each time window (TW), yielding an average minimum operating cost of USD 335.31. Meanwhile, results from the SBO iteration experiment indicate an inverse relationship between productivity factor (PF) values and yard crane (YC) service time. Conclusions : Overall, the findings provided an informed understanding of the need for dynamic scheduling of available resources in the yard to cut down on the gate operating costs. Further, the presented two methodologies can be incorporated with Industry 4.0 technologies to design digital twins for use in conventional CT by planners at an operational level as a decision-support tool.

Suggested Citation

  • Davies K. Bett & Islam Ali & Mohamed Gheith & Amr Eltawil, 2024. "Simulation-Based Optimization of Truck Appointment Systems in Container Terminals: A Dual Transactions Approach with Improved Congestion Factor Representation," Logistics, MDPI, vol. 8(3), pages 1-30, August.
    • Handle: RePEc:gam:jlogis:v:8:y:2024:i:3:p:80-:d:1452885
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    References listed on IDEAS

    as
    1. Doaa Naeem & Amr Eltawil & Junichi Iijima & Mohamed Gheith, 2022. "Integrated Scheduling of Automated Yard Cranes and Automated Guided Vehicles with Limited Buffer Capacity of Dual-Trolley Quay Cranes in Automated Container Terminals," Logistics, MDPI, vol. 6(4), pages 1-17, December.
    2. Yavuz Keceli, 2016. "A simulation model for gate operations in multi-purpose cargo terminals," Maritime Policy & Management, Taylor & Francis Journals, vol. 43(8), pages 945-958, November.
    3. Satyajith Amaran & Nikolaos V. Sahinidis & Bikram Sharda & Scott J. Bury, 2016. "Simulation optimization: a review of algorithms and applications," Annals of Operations Research, Springer, vol. 240(1), pages 351-380, May.
    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.
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    6. Ngoc Anh Dung Do & Izabela Ewa Nielsen & Gang Chen & Peter Nielsen, 2016. "A simulation-based genetic algorithm approach for reducing emissions from import container pick-up operation at container terminal," Annals of Operations Research, Springer, vol. 242(2), pages 285-301, July.
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