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Vol. 32, No. 8(2), S&M2292

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Sensors and Materials
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Sensors and Materials, Volume 34, Number 2(3) (2022)
Copyright(C) MYU K.K.
pp. 843-853
S&M2857 Research Paper of Special Issue
https://doi.org/10.18494/SAM3645
Published in advance: January 12, 2022
Published: February 28, 2022

Port Container Terminal Quay Crane Allocation Based on Simulation and Machine Learning Method [PDF]

Indranath Chatterjee and Gyusung Cho

(Received September 6, 2021; Accepted December 2, 2021)

Keywords: container terminal, quay crane, port operation, simulation method, machine learning, data analysis, IoT sensors

Container terminals play a crucial role in exporting and importing goods, where export and import containers are loaded and unloaded. Containers are usually loaded and unloaded with a dock crane. A quay crane is assigned at a container port in advance, considering a ship’s arrival schedule. However, allocating a quay crane is difficult owing to the limited number of quay cranes available and the need to consider the shipping timetable. In this study, by considering the schedule of each ship arriving from a container terminal, the number of unloaded containers for each ship, and the limited number of quay cranes, we conduct quay crane assignment through a simulation model to increase the productivity of a container terminal. Alongside, it is evident that artificial intelligence (AI) and machine learning (ML) are necessary for port management in many ways, from berth scheduling to quay allocation. In this study, we also investigate the applicability of ML and metaheuristic approaches in quay allocation problems and explore further possibilities. In this study, we also highlight the sensor-based automation of quay allocation using Internet of Things (IoT) technologies for future research in the domain of port and terminal management.

Corresponding author: Gyusung Cho


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This work is licensed under a Creative Commons Attribution 4.0 International License.

Cite this article
Indranath Chatterjee and Gyusung Cho, Port Container Terminal Quay Crane Allocation Based on Simulation and Machine Learning Method, Sens. Mater., Vol. 34, No. 2, 2022, p. 843-853.



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