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    Tytuł:
    Multi-Objective Optimization of Motor Vessel Route
    Autorzy:
    Marie, S.
    Courteille, E.
    Powiązania:
    https://bibliotekanauki.pl/articles/117604.pdf
    Data publikacji:
    2009
    Wydawca:
    Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
    Tematy:
    route planning
    Optimization of Vessel Route
    multi-objective optimization
    Motor Vessel
    Optimal Route
    Multi-Objective Genetic Algorithm (MOGA)
    Bézier Curve
    MATLAB
    Opis:
    This paper presents an original method that allows computation of the optimal route of a motor vessel by minimizing its fuel consumption. The proposed method is based on a new and efficient meshing procedure that is used to define a set of possible routes. A consumption prediction tool has been developed in order to estimate the fuel consumption along a given trajectory. The consumption model involves the effects of the meteorological conditions, the shape of the hull and the power train characteristics. Pareto-optimization with a Multi-Objective Genetic Algorithm (MOGA) is taken as a framework for the definition and the solution of the multi-objective optimization problem addressed. The final goal of this study is to provide a decision helping tool giving the route that minimizes the fuel consumption in a limited or optimum time.
    Źródło:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2009, 3, 2; 133-141
    2083-6473
    2083-6481
    Pojawia się w:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Multicriteria optimization method of LNG distribution
    Autorzy:
    Chłopińska, E.
    Gucma, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/117110.pdf
    Data publikacji:
    2020
    Wydawca:
    Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
    Tematy:
    cargo handling
    Liquefied Natural Gas (LNG)
    Multicriteria Optimization Method
    LNG Distribution
    Marine Diesel Oil (MDO)
    Heavy Fuel Oil (HFO)
    Vector Evaluated Genetic Algorithms (VEGA)
    Multi-Objective Genetic Algorithm (MOGA)
    Opis:
    Liquefied Natural Gas (LNG) is considered as a realistic substation of marine fuel in 21 century. Solution of building new engines or converting diesels into gas fueled propulsion meets the stringent international emission regulations. For HFO (heavy fuel oil) or MDO (marine diesel oil) propelled vessels, operation of bunkering is relatively wide known and simple. Its due to the fact that fuel itself doesn’t require high standards of handling. Where for LNG as a fuel its very demanding process – it evaporates and requires either consuming by bunker vessel or reliquefication. Distribution of such bunker is becoming multidimensional problem with time and space constrains. The objective of the article is to review the methods of optimization using genetic algorithms for a model of LNG distribution. In particular, there will be considered methods of solving problems with many boundry criteria whose objective functions are contradictory. Methods used for solving the majority of problems are can prevent the simultaneous optimization of the examined objectives, e.g. the minimisation of costs or distance covered, or the maximisation of profits or efficiency etc. Here the standard genetic algorithms are suitable for solving multi-criteria problems by using functions producing a diversity of results depending on the adopted approach.
    Źródło:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2020, 14, 2; 493-497
    2083-6473
    2083-6481
    Pojawia się w:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-2 z 2

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