Prediction of empty container demand using deep neural networks, a case study of Shahid Rajaee Port

Articles in Press

Document Type : Original Article

Authors

1 Head of Maritime Transport Department, BHRC

2 Member of Board of Directors; South Shipping lines - Iran line

10.22034/tri.2024.417293.3194

Abstract

Shahid Rajaee Port, as the largest and most important commercial port in the country, serves as the main hub for containerized cargo exchange. In recent years, with the development of the container terminal as part of the expansion project of the west edge of basin No. 3 in Shahid Rajaee Port, to accommodate the largest seventh-generation container vessels with a draft of approximately 17 meters and the enhancement of the port's container capacity from 6 to 8 million TEUs, along with private sector investments in the supporting lands of the port, there is a growing need for reassessing the operational and terminal management practices at the container terminal of this port. Consequently, the optimization and improvement of the efficiency of various terminal facilities and supporting lands have become the focus of attention for operators and stakeholders in the port industry. Considering the projected increase in port operations and the demand for containers in Shahid Rajaee Port, South Shipping-Iran line Company, as the operational arm of the IRI Shipping Lines (IRISL) Group and the main provider and responsible party for the majority of empty container operations for the country's exports, has prioritized the optimization of empty container management operations using new technologies. This article introduces the optimization process of empty container operations using machine learning and artificial intelligence methods. Given the possibility of predicting the demand for empty containers, it is possible to reduce the daily operation volume through advance planning and consider necessary measures regarding the appropriate spatial distribution of empty containers before the demand arises.

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Journal of Transportation Research

Articles in Press, Accepted Manuscript
Available Online from 20 April 2024

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