Determination of the number of automated guided vehicles required at a semi-automated container terminal

This paper studies a new automated container terminal (ACT) system which utilizes multistory frame bridges and rail-mounted trolleys to transport containers between the quay and the yard. Beside typical ACT systems use trucks or automated guided vehicles for transporting containers between quay cranes and yard cranes, the new design uses three types of handling machines, namely, ground trolleys (GTs), transfer platforms (TPs), and frame trolleys (FTs). These three types of handling machines collaborate with one another to transport containers. This study decomposes the system into several subsystems. Each subsystem has one TP and several FTs and GTs dedicated to this TP. Then, a Markov chain model is developed to analyze the throughput of TPs. At last, the performance of the new ACT system is estimated. Sensitivity analyzes the numbers, and the processing rates of trolleys are conducted through the numeric experiments.

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Connected Automated Guided Vehicles in a Smart Container Terminal

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/533/1/012029 ◽

2019 ◽

Vol 533 ◽

pp. 012029

Author(s):

Mohamed K. Watfa ◽

Khaldon Al Karmadi

Keyword(s):

Container Terminal ◽

Automated Guided Vehicles

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Noisy Optimization of Dispatching Policy for the Cranes at the Storage Yard in an Automated Container Terminal

Applied Sciences ◽

10.3390/app11156922 ◽

2021 ◽

Vol 11 (15) ◽

pp. 6922

Author(s):

Jeongmin Kim ◽

Ellen J. Hong ◽

Youngjee Yang ◽

Kwang Ryel Ryu

Keyword(s):

Container Terminal ◽

Scoring Function ◽

Weight Vector ◽

Container Terminals ◽

Exploration And Exploitation ◽

Noisy Optimization ◽

Automated Container Terminal ◽

Simulation Based ◽

The Cost ◽

Operation Schedule

In this paper, we claim that the operation schedule of automated stacking cranes (ASC) in the storage yard of automated container terminals can be built effectively and efficiently by using a crane dispatching policy, and propose a noisy optimization algorithm named N-RTS that can derive such a policy efficiently. To select a job for an ASC, our dispatching policy uses a multi-criteria scoring function to calculate the score of each candidate job using a weighted summation of the evaluations in those criteria. As the calculated score depends on the respective weights of these criteria, and thus a different weight vector gives rise to a different best candidate, a weight vector can be deemed as a policy. A good weight vector, or policy, can be found by a simulation-based search where a candidate policy is evaluated through a computationally expensive simulation of applying the policy to some operation scenarios. We may simplify the simulation to save time but at the cost of sacrificing the evaluation accuracy. N-RTS copes with this dilemma by maintaining a good balance between exploration and exploitation. Experimental results show that the policy derived by N-RTS outperforms other ASC scheduling methods. We also conducted additional experiments using some benchmark functions to validate the performance of N-RTS.

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