@article{Chlopinska_Gucma_2020, author = {Chlopinska, Ewelina and Gucma, Maciej}, title = {Multicriteria Optimization Method of LNG Distribution}, journal = {TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation}, volume = {14}, number = {2}, pages = {493-497}, year = {2020}, url = {./Article_Multicriteria_Optimization_Method_Chlopinska,54,1030.html}, abstract = {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.}, doi = {10.12716/1001.14.02.30}, issn = {2083-6473}, publisher = {Gdynia Maritime University, Faculty of Navigation}, keywords = {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)} }