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The operation of any vessel includes risks, such as mechanical failure, collision, property loss, cargo loss, or damage. For modern container ships, safe navigation is challenging as the rate of innovation regarding design, speed profiles, and carrying capacity has experienced exponential growth over the past few years. Prevention of cargo loss in container ship liners is of high importance for the Maritime industry and the waterborne sector as it can lead to potentially disastrous, harmful, or even life-threatening outcomes for the crew, the shipping company, the marine environment, and aqua-culture. With the installment of onboard decision support system(s) (DSS) that will provide the required operational guidance to the vessel’s master, we aim to prevent and overcome such events. This paper explores cargo losses in container ships by employing a novel weather routing optimization DS framework that aims to identify excessive motions and accelerations caused by bad weather at specific times and locations; it also suggests alternative routes and, thus, ultimately prevents cargo loss and damage

According to the International Maritime Organization’s (IMO) Greenhouse Gas (GHG ) strategy, the total annual GHG emissions from international shipping should be reduced by at least 50% by 2050 compared to 2008. Shipping adopts policies to comply with the set target, including ship re-design, structural retrofit, use of low-carbon material, and the installation of emission abatement technologies. All these approaches pave the way to circularity in the maritime economy, abandoning the linear model in vessel lifetime and adopting lean management, re-manufacturing, and re-usability of the asset. To this end, in the SmartShip project, we give prominence to data-driven ship monitoring by delivering an Information and Communication Technology (ICT) & Internet of Things (IoT)-enabled holistic cloud-based maritime performance and monitoring system.

Decision-making is one of the much-discussed problem of operational research. The problem is formalized as linear or nonlinear multivariable objective while constraints, values, and relations are defined and finally optimization techniques are applied to unify the better decision. Frequently the solutions of existing optimization techniques to rank the dmus differ concluding to low-level trust or confidence of decision maker(s). To overcome the doubt shortcoming a model suggesting a set of composite indicators (weights) to harmonize the most popular ordering techniques weighted/ non-weighted sum, multi criteria analysis, topsis index and dea superior index is implemented. 94 EWG MCDA | 2022 | Elounda Crete

This paper deals with the challenge of estimating Fuel Oil Consumption (FOC) in the context of Weather Routing (WR). We examine how a predictive FOC scheme can be coupled with WR optimization algorithms in order to reduce the vessel’s FOC, emissions, and the overall cost of a voyage. In order to handle the amount of data required for FOC prediction, we employ a streaming pipeline that harvests data in real-time and processes them appropriately for visualization, causal analysis, and forecasting purposes.

The DT4GS project aims to provide an Open Digital Twin Framework (DDF) for both shipping companies and the wider industry, to take advantage of new opportunities available through the use of DS. The project will make it easier for shipping companies to adopt the full range of IP innovations to support smart green shipping in upgrading existing ships and new vessels. The project will cover the entire life cycle of the ship including DS applications and using policies and related developments concerning the common data space in this area. The applications will focus on shipping companies, but will also provide a decision support system for reducing carbon dioxide emissions for shipyards, equipment manufacturers, port authorities and operators, energy companies and transport infrastructure companies.

Estimating the Fuel Oil Consumption (FOC) of a vessel is a critical task for the maritime industry, affecting route planning and the overall management of the vessel’s operation and maintenance. Even when a FOC estimation model is perfectly trained on a specific vessel, its performance may degrade over time, when new weather conditions apply or when the hydrodynamics of the vessel change over time, due to fouling, aging and negligent maintenance. This work presents an online learning framework that employs a custom encoding-decoding Neural Network scheme and real-time data from various on-board sensors, to appropriately update FOC estimation models.

ORISMA the operation research and information science in e-maritime covers holistically the theory and solving problems of ships management optimization based on authors’ selected papers and EU research projects contribution with the intention to be used by academia and maritime professionals to face up problems as challenges for continuous improvement

In this paper we describe a scenario from the Shipping industry, that employs analytics, stream processing, monitoring, alerting and vessel route optimization over big data. This includes the business process modelling, infrastructure management and monitoring along with dimensioning and deployment of focused services requiring different stakeholders roles for their parameterization and enactment.

Will innovation in shipping reduce carbon emissions by 2050? Interview with Prof Takis Varelas DANAOS RESEARCH CENTER (DRC) Revolve fall 2019 pg 52-53

In this work we face the challenge of estimating a ship’s mainengine rotational speed from vessel data series, in the context of sea vessel route optimization. To this end, we study the value of different vessel data types as predictors of the engine rotational speed. ….

Danaos Revolutionists early anticipated the potential of maritime digitalizing and leads the 4th generation shipping transformation

Optimizing Ship routing to maximize fleet revenue at Danaos | Interfaces vol 43,issn 1526-551x Awarded by INFORMS 2012

Maritime Information Systems in zone of Lykofos Guest Speech: Operational dromena |29th symposium of managerial thinking | Delphi

A KPIs archetype for ships management continuous improvement (2007) Trends & Developments in shipping management pp 43- 50, ISBN 978-9608592-3-2 | Awarded by EBA (European Business Award) 2010

Emulation in intelligent management systems (2011), Kathimerini Album pp20-25 | Greece Innovates! Applied Research award winner

Optimizing long term feet wide crew assignment (2014) 3rd international symposium in OR | ISBN 978-618-80631-4-7 pp191-195 Part of SEAHORSE Project awarded with Maritime safety award by LR +RINA (2016)

RE-ALARM(into): Crew resilience abilities assessment(2016) SEAHORSE Conference Maritime Safety and Human Factors conference , Glasgow 21-23 Sep 2016

Minimizing subjectivity noise at sea, 5TH International symposium in OR Pp 008-012 | ISBN 978-618-80631-6-2 | Presented by TV in his award of excellence in applied operational research ceremony

Intelligence in crew option systems cos(i) (2011) 3rd International symposium on ships operations, management and economics / SNAME ISBN 978-1-61839-1 pp 291-296

Intelligence in maritime Risk Management ras(i) (2011) 3rd International symposium on ships operations, management and economics / SNAME ISBN 978-1-61839-1 pp 23-28

Implementation of SMART methodology to maximize resilience performance (2016b) SEAHORSE Conference Maritime Safety and Human Factors conference , Glasgow 21-23 Sep 2016

Intelligent Algorithm for fuel oil supply (Iafos) 3rd International symposium on ships operations, management and economics SNAME ISBN 978-1-61839-1 pp 264-268

Fermat minimizes fuel cost in anisotropic passages (2015b) 4th International symposium in OR | Chania | 978-618-80361-4-7 pp 191-195

Definition of waiting point | 1st symposium EME/IFORS(2011) Athens www.eme-eeee.teipir.gr

CHAOS (I) intelligence in chartering option summary. (2010b) 9th Internat. Conf. Hellenic Oper. Res. Soc. (HELORS), Agios Nikolaos, Crete.

Intelligent voyage planning for emission lowering, LCS June 2011, University of Strathclyde pp135-140 | Awarded by EU ICT 2007

FTA & ann modelling fora predictive ship machinery maintenance methodology Royal institution of naval architects (RINA), smart ship technology conference proceedings, 24-25 January 2017, london, uk isbn: 978-1-909024-63-2