marine terminals
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2021 ◽  
Vol 13 (5) ◽  
pp. 2917
Author(s):  
Wenrui Qu ◽  
Tao Tao ◽  
Bo Xie ◽  
Yi Qi

As international trade and freight volumes increase, there is a growing port congestion problem, leading to the long truck queues at US marine terminal gates. To address this problem, some countermeasures have been proposed and implemented for reducing truck queue length at marine terminals. To assess the effectiveness of these countermeasures, a method for accurately estimating terminal gate truck queue length is needed. This study developed a new method, named the state-dependent approximation method, for estimating the truck queue length at marine terminals. Based on the simulation of the truck queuing system, it was found that it takes several hours for the truck queue length to reach its steady state, and neglecting the queue formation (queue dispersion) processes will cause overestimation (underestimation) of truck queue length. The developed model can take into account the queue formation and dispersion processes, and it can be used to estimate the truck queue length caused by short-term oversaturation at marine terminals. For model evaluation, a simulation-based case study was conducted to evaluate the prediction accuracy of the developed model by comparing its results with the simulated queue lengths and the results of other four existing methods, including the fluid flow model, the M/M/S queuing model, and a simulation-based regression model developed a previous study. The evaluation results indicate that the developed model outperformed the other four modeling methods for different states of queue formation and dispersion processes. In addition, this new method can accurately estimate the truck queue length caused by the short-term system oversaturation during peak hours. Therefore, it will be useful for assessing the effectiveness of the countermeasures that are targeted at reducing the peak-hour congestion at marine terminals.


2020 ◽  
Vol 1 ◽  
pp. 77-80 ◽  
Author(s):  
N.N. Gorban ◽  
◽  
G.G. Vasiliev ◽  
I.A. Leonovich ◽  
A.P. Salnikov ◽  
...  

2019 ◽  
Vol 47 (4) ◽  
pp. 456-478
Author(s):  
Na Li ◽  
Gang Chen ◽  
Manwo Ng ◽  
Wayne K. Talley ◽  
Zhihong Jin

Author(s):  
Nikolay Vladimirovich Kuptsov ◽  
Alexander Lvovich Kuznetsov

The article focuses on the problem of identifying the functional structure of the processes occurring on marine cargo handling area with modern export coal terminals using machines of continuous transporting (conveyor mechanization). Methods of data analysis and data mining allow to form a complete picture of the cycle of ship-loading operations and downtime, which are taken into account within the “ignorance” coefficients in the Russian regulatory framework of technological design of seaports. Today, designing high-tech terminals using “ignorance” ratios is found as archaism, because it reduces the calculated results of the throughput capacity of coal export terminals by 50-60%. The main purpose of the terminal functioning is the principle of economic efficiency: it is necessary to understand in detail the technological processes and to perform ship loading operations without exceeding the lay time to avoid additional costs for demurrage. For marine terminals it is important to identify, deeply understand and manage the factors hidden behind these coefficients. Identification of the functional structure of the marine side processes allows to influence on them, more efficiently operate the terminal and reduce the range of uncertainties, increasing the throughput of the marine side using the principles of the technical limit. Considered detailing the processes can be used as a toolkit for creating more accurate methods of calculating the throughput capacity of the maritime side, as well as being an informative description of simulation models. The use of detailed parametric probabilistic design models will allow achieving transparency in the relationships “a design engineer - a client” and generating results in terms of the most probable amounts of throughput and the potential for increasing freight turnover.


Author(s):  
N.N. Gorban ◽  
◽  
G.G. Vasiliev ◽  
I.A. Leonovich ◽  
◽  
...  

2018 ◽  
Author(s):  
Medhat Nabet ◽  
Sabah Noori ◽  
Muhammad Salman ◽  
Syed Abidi ◽  
Steve Orrock ◽  
...  
Keyword(s):  

2017 ◽  
pp. 417-444
Author(s):  
Thomas J. Fratar ◽  
Eric D. Dodge ◽  
Nicholas W. Philip
Keyword(s):  

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