Financial exergy maximization by calibrating weather routing with FF artificial network

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Dissertation title: 
Financial exergy maximization by calibrating weather routing with FF artificial network
Author name: 
Kaklis Dimitrios
School: 
Harokopio University
NCSR Supervisor: 
Spyropoulos Constantinos
Abstract: 

- The maximization of exergy in maritime transportation without additional investment just by intelligent reduction of the consumed energy to overcome added resistance due to weather factors; and even more: the transformation of the exergy, technically defined, function to a profit-utility function including the time as well as marketing data, that is the exergy of the corresponding financial system.
- FF ANN (Feed forward artificial neural network) and big data analytics will be designed and be developed for source-agnostic interoperability, eliminating vessels’-specific consumed time and cost theoretical hydrodynamic model.
- Algorithms to find the best seaway under calm weather conditions between any two waypoints, that avoids lands, restricted areas, war zones, draft limitations and navigation rules.

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