Development of Basic Planning Support System Using Marine Logistics Big Data and Its Application to Ship Basic Planning

Kunihiro Hamada, Noritaka Hirata, Kai Ihara, Dimas Angga Fakhri Muzhoffar, Mohammad Danil Arifin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Recently, the utilization of big data has been conducted in various sectors. Big data is a large collection of datasets that is effective for understanding current situations and future development. In marine industries, big data in the form of automatic identification system (AIS) data, ship principal dimension, port facilities, shipping routes, and international trade data has increased significantly. It is possible to acquire the insights of shipping industry activities with the effective utilization of big data. Similarly, the dynamic situation of the global market requires aggressive ship development. Accordingly, it is critical to realize certain ship specifications to satisfy market requirements. Considering this background, the authors examined the utilization of marine logistics big data to support ship basic planning, and developed a ship basic planning support system that can predict the demand of ships using marine logistic big data. However, in a previous study, a methodology to generate effective information using big data was the focus, and the applicability to practical ship design was not examined. Herein, the applicability of our ship basic planning support system to practical ship design is examined. Hence, the reliability of marine logistics big data (such as AIS data and ship movement data) according to region are examined. Moreover, a ship allocation model is developed in conformity with a marine logistic database. Applying a ship allocation model, we can reproduce the actual ship allocation of targeted shipping conditions and predict ships with high future demand. Therefore, effective ship size, ship performance, and ship demand are examined by assuming some future scenarios using the developed system, and some examples are presented.

Original languageEnglish
Title of host publicationPractical Design of Ships and Other Floating Structures - Proceedings of the 14th International Symposium, PRADS 2019 - Volume 3
EditorsTetsuo Okada, Yasumi Kawamura, Katsuyuki Suzuki
PublisherSpringer Science and Business Media Deutschland GmbH
Pages287-307
Number of pages21
ISBN (Print)9789811546792
DOIs
Publication statusPublished - 2021
Event14th International Symposium on Practical Design of Ships and Other Floating Structures, PRADS 2019 - Yokohama, Japan
Duration: 22 Sept 201926 Sept 2019

Publication series

NameLecture Notes in Civil Engineering
Volume65 LNCE
ISSN (Print)2366-2557
ISSN (Electronic)2366-2565

Conference

Conference14th International Symposium on Practical Design of Ships and Other Floating Structures, PRADS 2019
Country/TerritoryJapan
CityYokohama
Period22/09/1926/09/19

Keywords

  • Big data
  • Deep learning
  • Demand forecast
  • Marine logistics
  • Ship basic planning

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