Basic Ship-Planning Support System Using Big Data in Maritime Logistics for Simulating Demand Generation

Dimas Angga Fakhri Muzhoffar; Kunihiro Hamada; Yujiro Wada; Yusuke Miyake; Shun Kawamura

doi:10.3390/jmse10020186

Basic Ship-Planning Support System Using Big Data in Maritime Logistics for Simulating Demand Generation

Dimas Angga Fakhri Muzhoffar, Kunihiro Hamada, Yujiro Wada, Yusuke Miyake, Shun Kawamura

Department of Mechanical Engineering

Research output: Contribution to journal › Article › peer-review

Abstract

Dynamic changes in the global market demand affect ship development. Correspond-ingly, big data have provided the ability to comprehend the current and future conditions in nu-merous sectors and understand the dynamic circumstances of the maritime industry. Therefore, we have developed a basic ship-planning support system utilizing big data in maritime logistics. Pre-vious studies have used a ship allocation algorithm, which only considered the ship cost (COST) along limited target routes; by contrast, in this study, a basic ship-planning support system is reinforced with particularized COST attributes and greenhouse gas (GHG) features incorporated into a ship allocation algorithm related to the International Maritime Organization GHG reduction strat-egy. Additionally, this system is expanded to a worldwide shipping area. Thus, we optimize the operation-level ship allocation using the existing ships by considering the COST and GHG emis-sions. Finally, the ship specifications demanded worldwide are ascertained by inputting the new ships instance.

Original language	English
Article number	186
Journal	Journal of Marine Science and Engineering
Volume	10
Issue number	2
DOIs	https://doi.org/10.3390/jmse10020186
Publication status	Published - Feb 2022

Keywords

Big data
GHG emissions
Greedy algorithm
Maritime logistics
Ship allocation

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10.3390/jmse10020186

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title = "Basic Ship-Planning Support System Using Big Data in Maritime Logistics for Simulating Demand Generation",

abstract = "Dynamic changes in the global market demand affect ship development. Correspond-ingly, big data have provided the ability to comprehend the current and future conditions in nu-merous sectors and understand the dynamic circumstances of the maritime industry. Therefore, we have developed a basic ship-planning support system utilizing big data in maritime logistics. Pre-vious studies have used a ship allocation algorithm, which only considered the ship cost (COST) along limited target routes; by contrast, in this study, a basic ship-planning support system is reinforced with particularized COST attributes and greenhouse gas (GHG) features incorporated into a ship allocation algorithm related to the International Maritime Organization GHG reduction strat-egy. Additionally, this system is expanded to a worldwide shipping area. Thus, we optimize the operation-level ship allocation using the existing ships by considering the COST and GHG emis-sions. Finally, the ship specifications demanded worldwide are ascertained by inputting the new ships instance.",

keywords = "Big data, GHG emissions, Greedy algorithm, Maritime logistics, Ship allocation",

author = "Muzhoffar, {Dimas Angga Fakhri} and Kunihiro Hamada and Yujiro Wada and Yusuke Miyake and Shun Kawamura",

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doi = "10.3390/jmse10020186",

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AU - Muzhoffar, Dimas Angga Fakhri

AU - Hamada, Kunihiro

AU - Wada, Yujiro

AU - Miyake, Yusuke

AU - Kawamura, Shun

PY - 2022/2

Y1 - 2022/2

N2 - Dynamic changes in the global market demand affect ship development. Correspond-ingly, big data have provided the ability to comprehend the current and future conditions in nu-merous sectors and understand the dynamic circumstances of the maritime industry. Therefore, we have developed a basic ship-planning support system utilizing big data in maritime logistics. Pre-vious studies have used a ship allocation algorithm, which only considered the ship cost (COST) along limited target routes; by contrast, in this study, a basic ship-planning support system is reinforced with particularized COST attributes and greenhouse gas (GHG) features incorporated into a ship allocation algorithm related to the International Maritime Organization GHG reduction strat-egy. Additionally, this system is expanded to a worldwide shipping area. Thus, we optimize the operation-level ship allocation using the existing ships by considering the COST and GHG emis-sions. Finally, the ship specifications demanded worldwide are ascertained by inputting the new ships instance.

AB - Dynamic changes in the global market demand affect ship development. Correspond-ingly, big data have provided the ability to comprehend the current and future conditions in nu-merous sectors and understand the dynamic circumstances of the maritime industry. Therefore, we have developed a basic ship-planning support system utilizing big data in maritime logistics. Pre-vious studies have used a ship allocation algorithm, which only considered the ship cost (COST) along limited target routes; by contrast, in this study, a basic ship-planning support system is reinforced with particularized COST attributes and greenhouse gas (GHG) features incorporated into a ship allocation algorithm related to the International Maritime Organization GHG reduction strat-egy. Additionally, this system is expanded to a worldwide shipping area. Thus, we optimize the operation-level ship allocation using the existing ships by considering the COST and GHG emis-sions. Finally, the ship specifications demanded worldwide are ascertained by inputting the new ships instance.

KW - Big data

KW - GHG emissions

KW - Greedy algorithm

KW - Maritime logistics

KW - Ship allocation

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ER -

Basic Ship-Planning Support System Using Big Data in Maritime Logistics for Simulating Demand Generation

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Keywords

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