Port Recommendation System for Alternative Container Port Destinations Using a Novel Neural Language-Based Algorithm

Shipping containers are tokens of multimodal international transportation and rapid logistics. Container deliveries are scheduled to satisfy rapidly changing requirements. Unpredictable increases in costs and unforeseeable events such as pandemics compel ship owners and managers to adopt risk minimi...

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Bibliographic Details
Published inIEEE access Vol. 8; pp. 199970 - 199979
Main Authors Mei, Qiang, Hu, Qinyou, Yang, Chun, Zheng, Hailin, Hu, Zhisheng
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
AIS
Algorithms
Artificial intelligence
Cargo ships
Container shipping
Containers
Delivery scheduling
Language programming
Logistics
Marine vehicles
Natural language
Natural language (computers)
Natural languages
network embedding
neural natural language programming algorithm
port recommendation
port2vec
Ports
Programming
Recommender systems
Risk management
Sailing
Shipping
Transportation
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Summary:Shipping containers are tokens of multimodal international transportation and rapid logistics. Container deliveries are scheduled to satisfy rapidly changing requirements. Unpredictable increases in costs and unforeseeable events such as pandemics compel ship owners and managers to adopt risk minimization measures. This study addresses one issue: how to determine an alternative port of call from massive data to offer a realistic destination change recommendation for a container vessel. Recommendation algorithms have become ubiquitous and are used effectively in other fields, but there is no such model for the port of call selection or recommendation. Large scale automatic identification system (AIS) data are readily available. We developed a computational framework based on a novel natural language programming algorithm that was tailored to support port recommendation rather than use a conventional adjacency matrix method. We mined large scale AIS data to construct sequential berth records for container vessels and mapped each port onto a vector in an embedded space. The natural language neural programming algorithm can suggest ports similar to the scheduled ports of call that were unable to offer service. The recommendations were validated with geo-analysis of sailing distance and could offer viable alternative ports to shipping managers.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.3035503