Truck Appointment System for Cooperation between the Transport Companies and the Terminal Operator at Container Terminals

Im, Hyeonu; Yu, Jiwon; Lee, Chulung

doi:10.3390/app11010168

Cited by 12 publications

(9 citation statements)

References 16 publications

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“…The outbound operation includes two types: container delivery and stacking operation and loading operation. The container delivery and stacking operation transport the outbound containers from the external container truck to the terminal yard after the ship gathering time window is opened in the CT yard, and the yard crane will unload and stack the outbound containers to the designated container position according to the formulated operation plan [24]. Loading operation refers to that after the ship arrives at the port, the outbound container is transported from the inner container truck to the wharf apron, and the shore bridge driver needs to stack the outbound container to the designated position according to the ship's stowage drawing and outbound container loading plan.…”

Section: Plos Onementioning

confidence: 99%

Storage strategy of outbound containers with uncertain weight by data-driven hybrid genetic simulated annealing algorithm

Wang

Bai

et al. 2023

PLoS ONE

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It is necessary to ensure the ship’s stability in container ship stowage and loading and unloading containers. This work aims to reduce the container dumping operation at the midway port and improve the efficiency of ship transportation. Firstly, the constraint problem of the traditional container ship stacking is introduced to realize the multi-condition mathematical model of the container ship, container, and wharf. Secondly, a Hybrid Genetic and Simulated Annealing Algorithm (HGSAA) model is proposed for the container stacking and loading stacking in the yard. The specific container space allocation and multi-yard crane adjustment scheme are studied. Finally, the effectiveness of the multi-condition container ship stowage model is verified by numerical experiments by changing the number of outbound containers, storage strategies, storage yards, and bridges. The experimental results show that the HGSAA mode converges to 106.1min at the 751st iteration. Of these, the non-loading and unloading time of yard bridge 1 is 3.43min. The number of operating boxes is 25. The non-loading and unloading time of yard bridge 2 is 3.2min, and the operating box volume is 25 boxes. The objective function of the genetic algorithm converges when it iterates to generation 903 and 107.9min. Among them, the non-loading and unloading time of yard bridge 1 is 4.1min. The non-loading and unloading time of yard bridge 2 is 3.1min. Therefore, the proposed HGSAA has a faster convergence speed than the genetic algorithm and can obtain relatively good results. The proposed container stacking strategy can effectively solve the specific container allocation and multi-yard crane scheduling problems. The finding provides a reference for optimizing container scheduling and improving shipping transportation efficiency.

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Section: Plos Onementioning

confidence: 99%

Storage strategy of outbound containers with uncertain weight by data-driven hybrid genetic simulated annealing algorithm

Wang

Bai

et al. 2023

PLoS ONE

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“…Their system considered the variability in turnaround times, which can be affected by various unpredictable factors. Hyeonu et al [4] introduced a novel formulation for a collaborative TAS that fosters cooperation between transportation companies and terminal operators. The primary objective of this collaborative TAS is to effectively address and mitigate truck congestion between the yard area and the terminal gate.…”

Section: Classification Based On the Modeling Approachmentioning

confidence: 99%

“…Similarly, both Shao et al [12] and Torkjazi et al [5] sought to avoid gate and yard congestion by implementing strategies that flatten truck arrival peaks and simultaneously minimize truck turnaround times. In addition, Hyeonu et al [4] addressed the minimization of truck congestion between the yard area and the terminal gate by proposing a TAS that fosters cooperation between trucking companies and the terminal operator.…”

Section: Classification Based On the Study Objectivesmentioning

confidence: 99%

A Multi-Stage Approach for External Trucks and Yard Cranes Scheduling with CO2 Emissions Considerations in Container Terminals

Talaat,

Gheith,

Eltawil

2023

Logistics

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Background: In container terminals, optimizing the scheduling of external trucks and yard cranes is crucial as it directly impacts the truck turnaround time, which is one of the most critical performance measures. Furthermore, proper scheduling of external trucks contributes to reducing CO2 emissions. Methods: This paper proposes a new approach based on a mixed integer programming model to schedule external trucks and yard cranes with the objective of minimizing CO2 emissions and reducing truck turnaround time, the gap between trucking companies’ preferred arrival time and appointed time, and the energy consumption of yard cranes. The proposed approach combines data analysis and operations research techniques. Specifically, it employs a K-means clustering algorithm to reduce the number of necessary truck trips for container handling. Additionally, a two-stage mathematical model is applied. The first stage employs a bi-objective mathematical model to plan the arrival of external trucks at the terminal gates. The second stage involves a mathematical model that schedules yard cranes’ movements between different yard blocks. Results: The results show that implementing this methodology in a hypothetical case study may lead to a substantial daily reduction of approximately 31% in CO2 emissions. Additionally, the results provide valuable insights into the trade-off between satisfying the trucking companies’ preferred arrival time and the total turnaround time. Conclusions: The integration of data clustering with mathematical modeling demonstrates a notable reduction in emissions, underscoring the viability of this strategy in promoting sustainability in port-related activities.

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“…The authors finally recommended optimal operational service gates to minimise the port authority's cost to handle the given truck arrival rate. Im et al (2021) developed a cooperation model that simultaneously considered the perspective of forwarders and terminal operators. They compared numerical results between cases in which truck companies and port operators make their own decisions and cooperate.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimising truck arrival management and number of service gates at container terminals

Minh

Noi²

2021

MABR

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PurposeTruck appointment systems have been applied in critical container ports in the United States due to their potential to improve handling operations. This paper aims to develop a truck appointment system to optimise the total cost experiencing at the entrance of container terminals by managing truck arrivals and the number of service gates satisfying a given level of service.Design/methodology/approachThe approximation of Mt/G/nt queuing model is applied and integrated into a cost optimisation model to identify (1) the number of arrival trucks allowed at each time slot and (2) the number of service gates operating at each time slot that ensure the average waiting time is less than a designated time threshold. The optimisation model is solved by the Genetic Algorithm and tested with a case study. Its effectiveness is identified by comparing the model's outcomes with observed data and other recent studies.FindingsThe results indicate that the developed truck appointment system can provide more than threefold and twofold reductions of the total cost experiencing at the terminal entrance compared to the actual data and results from previous research, respectively.Originality/valueThe proposed approach provides applicably coordinated truck plans and operating service gates efficiently to decrease congestion, emission and expenses.

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Truck Appointment System for Cooperation between the Transport Companies and the Terminal Operator at Container Terminals

Cited by 12 publications

References 16 publications

Storage strategy of outbound containers with uncertain weight by data-driven hybrid genetic simulated annealing algorithm

Storage strategy of outbound containers with uncertain weight by data-driven hybrid genetic simulated annealing algorithm

A Multi-Stage Approach for External Trucks and Yard Cranes Scheduling with CO2 Emissions Considerations in Container Terminals

Optimising truck arrival management and number of service gates at container terminals

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