Special Issue "Modeling Computing and Data Handling for Marine Transportation"

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (31 October 2018).

Special Issue Editors

Guest Editor
Dr. Charalampos Konstantopoulos

Department of Informatics, University of Piraeus, Greece
Website | E-Mail
Interests: operational research; optimization algorithms; parallel and distributed computing; wireless sensor networks
Guest Editor
Prof. Dr. Grammati Pantziou

Department of Informatics, University of West Attica, Greece
Website | E-Mail
Phone: 306973388695
Interests: operational research; optimization algorithms; parallel and distributed computing; wireless sensor networks

Special Issue Information

Dear Colleagues,

Maritime transportation is the major conduit of international trade. In terms of cost, maritime transport is very competitive relative to land and airborne transport, increasing only by a few percent the total product cost. On the other hand, maritime transportation takes longer or may cause harbor congestion, which further increases voyage times. Furthermore, there are difficulties in integrating this transportation mode efficiently with other transport or distribution options. On top of that, the safety and the environmental impact of maritime transportation, in particular, in the case of sea accidents, are always two challenging issues.  

As recent advances on maritime transportation require the synergy of both computer science and maritime science, the main focus in this special issue will be upon the latest developments on IT methodologies for maritime transportation. Computational intelligence, data mining and knowledge discovery/representation, risk assessment methodologies as well as combinatorial optimization are the IT fields that have gain importance in maritime studies because of their potential in giving solutions for effective sea transportation.

In particular, the topics of interest in this Special Issue covers the scope of the 3rd workshop on modeling computing and data handling for marine transportation (MCDMT 2018) (http://iisa2018.unipi.gr/mcdmt/). Extended versions of papers presented at MCDMT 2018 are sought, but this Special Issue is also open to all who wish to contribute by submitting a research paper relevant to the area.

Dr. Charalampos Konstantopoulos
Prof. Dr. Grammati Pantziou
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Graph and Network algorithms for Marine Transportation
  • Combinatorial optimization techniques for Marine Transportation
  • Weather Routing
  • Environmentally Safe Shipping
  • Safety and Security of Maritime Shipping
  • Risk and Safety Analysis, Assessment and Prediction
  • Piracy Protection
  • GIS in Maritime Applications
  • Spatiotemporal and Marine Data Handling
  • Route Planning and Monitoring
  • Maritime Data Mining and Knowledge Discovery Applications: surveillance, maritime traffic control, anomaly detection, emergency management, situation recognition, etc.
  • Decision Support Tools for Marine Transportation
  • Integration of Heterogeneous Marine Data Sources

Published Papers (4 papers)

View options order results:
result details:
Displaying articles 1-4
Export citation of selected articles as:

Research

Open AccessArticle
An INS-UWB Based Collision Avoidance System for AGV
Algorithms 2019, 12(2), 40; https://doi.org/10.3390/a12020040
Received: 20 December 2018 / Revised: 9 February 2019 / Accepted: 13 February 2019 / Published: 18 February 2019
PDF Full-text (1157 KB) | HTML Full-text | XML Full-text
Abstract
As a highly automated carrying vehicle, an automated guided vehicle (AGV) has been widely applied in various industrial areas. The collision avoidance of AGV is always a problem in factories. Current solutions such as inertial and laser guiding have low flexibility and high [...] Read more.
As a highly automated carrying vehicle, an automated guided vehicle (AGV) has been widely applied in various industrial areas. The collision avoidance of AGV is always a problem in factories. Current solutions such as inertial and laser guiding have low flexibility and high environmental requirements. An INS (inertial navigation system)-UWB (ultra-wide band) based AGV collision avoidance system is introduced to improve the safety and flexibility of AGV in factories. An electronic map of the factory is established and the UWB anchor nodes are deployed in order to realize an accurate positioning. The extended Kalman filter (EKF) scheme that combines UWB with INS data is used to improve the localization accuracy. The current location of AGV and its motion state data are used to predict its next position, decrease the effect of control delay of AGV and avoid collisions among AGVs. Finally, experiments are given to show that the EKF scheme can get accurate position estimation and the collisions among AGVs can be detected and avoided in time. Full article
(This article belongs to the Special Issue Modeling Computing and Data Handling for Marine Transportation)
Figures

Figure 1

Open AccessArticle
Data Analysis, Simulation and Visualization for Environmentally Safe Maritime Data
Algorithms 2019, 12(1), 27; https://doi.org/10.3390/a12010027
Received: 31 October 2018 / Revised: 4 January 2019 / Accepted: 17 January 2019 / Published: 21 January 2019
PDF Full-text (5966 KB) | HTML Full-text | XML Full-text
Abstract
Marine transportation in Aegean Sea, a part of the Mediterranean Sea that serves as gateway between three continents has recently seen a significant increase. Despite the commercial benefits to the region, there are certain issues related to the preservation of the local ecosystem [...] Read more.
Marine transportation in Aegean Sea, a part of the Mediterranean Sea that serves as gateway between three continents has recently seen a significant increase. Despite the commercial benefits to the region, there are certain issues related to the preservation of the local ecosystem and safety. This danger is further deteriorated by the absence of regulations on allowed waterways. Marine accidents could cause a major ecological disaster in the area and pose big socio-economic impacts in Greece. Monitoring marine traffic data is of major importance and one of the primary goals of the current research. Real-time monitoring and alerting can be extremely useful to local authorities, companies, NGO’s and the public in general. Apart from real-time applications, the knowledge discovery from historical data is also significant. Towards this direction, a data analysis and simulation framework for maritime data has been designed and developed. The framework analyzes historical data about ships and area conditions, of varying time and space granularity, measures critical parameters that could influence the levels of hazard in certain regions and clusters such data according to their similarity. Upon this unsupervised step, the degree of hazard is estimated and along with other important parameters is fed into a special type of Bayesian network, in order to infer on future situations, thus, simulating future data based on past conditions. Another innovative aspect of this work is the modeling of shipping traffic as a social network, whose analysis could provide useful and informative visualizations. The use of such a system is particularly beneficial for multiple stakeholders, such as the port authorities, the ministry of Mercantile Marine, etc. mainly due to the fact that specific policy options can be evaluated and re-designed based on feedback from our framework. Full article
(This article belongs to the Special Issue Modeling Computing and Data Handling for Marine Transportation)
Figures

Figure 1

Open AccessArticle
A Forecast Model of the Number of Containers for Containership Voyage
Algorithms 2018, 11(12), 193; https://doi.org/10.3390/a11120193
Received: 20 October 2018 / Revised: 23 November 2018 / Accepted: 26 November 2018 / Published: 28 November 2018
PDF Full-text (1674 KB) | HTML Full-text | XML Full-text
Abstract
Container ships must pass through multiple ports of call during a voyage. Therefore, forecasting container volume information at the port of origin followed by sending such information to subsequent ports is crucial for container terminal management and container stowage personnel. Numerous factors influence [...] Read more.
Container ships must pass through multiple ports of call during a voyage. Therefore, forecasting container volume information at the port of origin followed by sending such information to subsequent ports is crucial for container terminal management and container stowage personnel. Numerous factors influence container allocation to container ships for a voyage, and the degree of influence varies, engendering a complex nonlinearity. Therefore, this paper proposes a model based on gray relational analysis (GRA) and mixed kernel support vector machine (SVM) for predicting container allocation to a container ship for a voyage. First, in this model, the weights of influencing factors are determined through GRA. Then, the weighted factors serve as the input of the SVM model, and SVM model parameters are optimized through a genetic algorithm. Numerical simulations revealed that the proposed model could effectively predict the number of containers for container ship voyage and that it exhibited strong generalization ability and high accuracy. Accordingly, this model provides a new method for predicting container volume for a voyage. Full article
(This article belongs to the Special Issue Modeling Computing and Data Handling for Marine Transportation)
Figures

Graphical abstract

Open AccessArticle
Numerical Modeling of Wave Disturbances in the Process of Ship Movement Control
Algorithms 2018, 11(9), 130; https://doi.org/10.3390/a11090130
Received: 19 July 2018 / Revised: 13 August 2018 / Accepted: 29 August 2018 / Published: 31 August 2018
PDF Full-text (3647 KB) | HTML Full-text | XML Full-text
Abstract
The article presents a numerical model of sea wave generation as an implementation of the stochastic process with a spectrum of wave angular velocity. Based on the wave spectrum, a forming filter is determined, and its input is fed with white noise. The [...] Read more.
The article presents a numerical model of sea wave generation as an implementation of the stochastic process with a spectrum of wave angular velocity. Based on the wave spectrum, a forming filter is determined, and its input is fed with white noise. The resulting signal added to the angular speed of a ship represents disturbances acting on the ship’s hull as a result of wave impact. The model was used for simulation tests of the influence of disturbances on the course stabilization system of the ship. Full article
(This article belongs to the Special Issue Modeling Computing and Data Handling for Marine Transportation)
Figures

Figure 1

Algorithms EISSN 1999-4893 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top