Optimization of the Storage Sites for Export, Inbound, and Reorganize Containers by Timing Location

Mohammed Saleh, Attariuas Hicham, M. L. Ben Maâti, Hatem Taha

2021

Abstract

Today, seaports subtend an increasing growth of containers stacking. Countries are striving to get the most benefit from seaports and increase their share of this sector's resources, as well as optimizing their competitiveness. Despite this increase, the ports suffer from many problems, including how to take the appropriate decision to store and empty containers of various kinds. In this paper, we propose a method for storing containers at (El Qasr El Saghir) terminal in Morocco, based on the hypothesis of time dynamics for choosing the optimal location for the container in the yard. This hypothesis provides ideal storage locations for containers arranged by time to avoid the accumulation of containers, reduce the involuntary movement of previously-stored containers. As well as facilitate the decision to relocate containers stored in the terminal to allow the provision of new storage places, reducing time and operating cost. We propose to apply artificial intelligence (particularly ANN) to this methodology (a case study on El Qasr El Saghir); for example, deciding for stacking containers with different departure dates; because the parameters of our method are compatible with the ANN algorithm.

Paper Citation

in Harvard Style

Saleh M., Hicham A., Ben Maâti M. and Taha H. (2021). Optimization of the Storage Sites for Export, Inbound, and Reorganize Containers by Timing Location. In Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning - Volume 1: BML, ISBN 978-989-758-559-3, pages 333-338. DOI: 10.5220/0010733700003101

in Bibtex Style

@conference{bml21,
author={Mohammed Saleh and Attariuas Hicham and M. L. Ben Maâti and Hatem Taha},
title={Optimization of the Storage Sites for Export, Inbound, and Reorganize Containers by Timing Location},
booktitle={Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning - Volume 1: BML,},
year={2021},
pages={333-338},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010733700003101},
isbn={978-989-758-559-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning - Volume 1: BML,
TI - Optimization of the Storage Sites for Export, Inbound, and Reorganize Containers by Timing Location
SN - 978-989-758-559-3
AU - Saleh M.
AU - Hicham A.
AU - Ben Maâti M.
AU - Taha H.
PY - 2021
SP - 333
EP - 338
DO - 10.5220/0010733700003101