ISSN 2083-6473
ISSN 2083-6481 (electronic version)




Associate Editor
Prof. Tomasz Neumann

Published by
TransNav, Faculty of Navigation
Gdynia Maritime University
3, John Paul II Avenue
81-345 Gdynia, POLAND
www http://www.transnav.eu
e-mail transnav@umg.edu.pl
Multicriteria Optimization Method of LNG Distribution
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.
Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T., A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Transactions on Evolutionary Computation 6(2) (2002) 182-197. - doi:10.1109/4235.996017
Fonseca C.M, Fleming P.J.: Genetic Algorithms for Multiobjective Optimization: Formulation, Discussion and Generalization. In Stephanie Forrest, editor, Proceedings of the Fifth International Conference on Genetic Algorithms, pages 416–423, San Mateo, California, 1993. University of Illinois at Urbana-Champaign, Morgan Kauffman Publishers.
Goldberg D. E., Algorytmy genetyczne i ich zastosowanie. Warszawa: WNT, 2003.
Gucma M., Bąk A., Chłopińska E.: Concept of LNG transfer and bunkering model of vessels at South Baltic Sea Arena. Annual of Navigation 25/2018, pages 79-91. - doi:10.1515/aon-2018-0006
Holland, J.H., Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, 1975.
Horn, J., Nafpliotis, N., and Goldberg, D.E. A niched Pareto genetic algorithm for multiobjective optimization. in Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence, 27-29 June 1994.
Knowles, J.D. and Corne, D.W., Approximating the nondominated front using the Pareto archived evolution strategy, Evolutionary Computation 8(2) 149-172. - doi:10.1162/106365600568167
Koza J. R., Rice J. P., i Roughgarden J., „Evolution of food foraging strategies for the Caribbean Anolis lizard using genetic programming.”, Adaptive Behavior., t. 1, nr 2, ss. 47–74, 1992. - doi:10.1177/105971239200100203
Lu, H. and Yen, G.G., Rank-density-based multiobjective genetic algorithm and benchmark test function study, IEEE Transactions on Evolutionary Computation 7(4) (2003) 325-343. - doi:10.1109/TEVC.2003.812220
Michalewicz Z., Algorytmy genetyczne + struktury danych = programy ewolucyjne. Warszawa: Wydawnictwo Naukowo-Techniczne, 1999.
Migawa K.: Method for control of technical objects operation process with the use of semi-Markov decision processes, Journal of KONES Powertrain and Transport., t. 19, nr 4, 2012. - doi:10.5604/12314005.1138615
Mitchell M., An introduction to genetic algorithms., 1. wyd. Cambridge: MIT Press, 1996.
Mitsuo G., Runwei Ch.: Genetic algorithms and engineering optimization. John Wiley & Sons, inc. New York, 2000.
Murata, T. and Ishibuchi, H. MOGA: multi-objective genetic algorithms. in Proceedings of 1995 IEEE International Conference on Evolutionary Computation, 29 Nov.-1 Dec. 1995.
Sarker, R., Liang, K.-H., and Newton, C., A new multiobjective evolutionary algorithm, European Journal of Operational Research 140(1) (2002) 12-23. - doi:10.1016/S0377-2217(01)00190-4
Schaffer, J.D. Multiple Objective optimization with vector evaluated genetic algorithms. in International Conference on Genetic Algorithm and their applications. 1985.
Srinivas, N. and Deb, K., Multiobjective Optimization Using Nondominated Sorting in Genetic Algorithms, Journal of Evolutionary Computation 2(3) (1994) 221-248. - doi:10.1162/evco.1994.2.3.221
Tutorial A., Konak A., Coit D.W., Smith A.E.: Multi-Objective Optimization Using Genetic Algorithms: A tutorial. Reliability Engineering and System Safety 91 (2006), s.992 – 1007. - doi:10.1016/j.ress.2005.11.018
Zitzler, E. and Thiele, L., Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach, IEEE Transactions on Evolutionary Computation 3(4) (1999) 257-271. - doi:10.1109/4235.797969
Dyrektywa Parlamentu Europejskiego i Rady 2012/33/UE
Citation note:
Chłopińska E., Gucma M.: Multicriteria Optimization Method of LNG Distribution. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, Vol. 14, No. 2, doi:10.12716/1001.14.02.30, pp. 493-497, 2020

Other publications of authors:

M. Perkovic, E. Twrdy, R. Harsch, M. Gucma, P. Vidmar

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