TY - GEN
T1 - Development of Basic Planning Support System Using Marine Logistics Big Data and Its Application to Ship Basic Planning
AU - Hamada, Kunihiro
AU - Hirata, Noritaka
AU - Ihara, Kai
AU - Muzhoffar, Dimas Angga Fakhri
AU - Arifin, Mohammad Danil
N1 - Publisher Copyright:
© 2021, Springer Nature Singapore Pte Ltd.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Big data
KW - Deep learning
KW - Demand forecast
KW - Marine logistics
KW - Ship basic planning
UR - http://www.scopus.com/inward/record.url?scp=85092789098&partnerID=8YFLogxK
U2 - 10.1007/978-981-15-4680-8_21
DO - 10.1007/978-981-15-4680-8_21
M3 - Conference contribution
AN - SCOPUS:85092789098
SN - 9789811546792
T3 - Lecture Notes in Civil Engineering
SP - 287
EP - 307
BT - Practical Design of Ships and Other Floating Structures - Proceedings of the 14th International Symposium, PRADS 2019 - Volume 3
A2 - Okada, Tetsuo
A2 - Kawamura, Yasumi
A2 - Suzuki, Katsuyuki
PB - Springer Science and Business Media Deutschland GmbH
T2 - 14th International Symposium on Practical Design of Ships and Other Floating Structures, PRADS 2019
Y2 - 22 September 2019 through 26 September 2019
ER -