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An Ant Colony Algorithm for efficient ship routing

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
With the substantial rising of international oil price and global warming on the rise, how to reduce operational fuel consumption and decrease air pollution has become one of the pursued goals of green ship. Ship route planning is an indispensible part of the ship navigation process, especially in transoceanic crossing ship routing. The soundness of ship routing not only affects the safety of ship navigation but also the operation economy and environmental protection. This research is based on the platform of Electronic Chart Display and Information System (ECDIS), and founded on Ant Colony Algorithm (ACA) combined with the concept of Genetic Algorithm (GA), to model living organisms optimization behaviour to perform efficient ship route planning in transoceanic crossing. Besides the realization of route planning automation, ship routing will achieve the goal of optimum carbon dioxide reduction and energy conservation, and provide reference for route planning decision
Słowa kluczowe
Wydawca
-
Rocznik
Tom
20
Numer
3
Opis fizyczny
p.28-38,fig.,ref.
Twórcy
autor
  • Department of Shipping Technology, National Kaohsiung Marine University, Kaohsiung, Taiwan, R.O.C.
autor
  • Department of Marine Environment and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan, R.O.C.
Bibliografia
  • 1. Bijlsma, S. J.,: A Computational Method for the Solution of Optimal Control Problems in Ship Routing. NAVIGATION, Journal of the Institute of Navigation, Vol. 48, pp. 145-154, 2001.
  • 2. Bijlsma, S. J.: On the Application of Optimal Control Theory and Dynamic Programming in Ship Routing. NAVIGATION, Journal of the Institute of Navigation, Vol. 49, pp. 71-80, 2002.
  • 3. Bijlsma, S. J.: Minimal Time Route Computation for Ships with Pre-Specified Voyage Fuel Consumption. The Journal of Navigation, Vol. 61, pp. 723-733, 2008.
  • 4. Boditch, N.: The American Practical Navigator – 2002 Bicentennial Edition. National Imagery and Mapping Agency, U.S. Government, 2002.
  • 5. Dorigo, M., Manizzzo, V. and Colomi, A.: Ant system optimization by a colony of cooperating agents. IEEE Transaction on System, Man and Cybernetics- Part B: Cybernetics, Vol. 26, No. 1, pp. 29-41, 1991.
  • 6. Flecks, J.: Study Green Shipping. HVB Global Shipping, Hamburg, pp. 4-5, 2009.
  • 7. Hagiwara, H.:Weather Routing of Sail Assisted Motor Vessels. Ph. D. Thesis, Delft University, Holland, 1989.
  • 8. Hanssen, G. L. and James, R. W., “Optimum Ship Routing,” The Journal of Navigation, Vol. 13, pp. 253-272, 1960.
  • 9. Ito, M., Zhang, F. and Yoshida, N.: Collision avoidance of ship with genetic algorithm. Proceedings of 1999 IEEE International Conference on Control Applications, pp. 1791–1796, 1999.
  • 10. Khalilov, S. I.: Stochastic dynamic programming method for computing the most advantageous ship navigation routes. Meteoro. Hydrol., No. II, 1980.
  • 11. Kosmas, O. T., Vlachos, D. S. and Simos, T. E.: Obstacle Bypassing in Optimal Ship Routing Using Simulated Annealing. Proceedings of International Electronic Conference on Computer Science, Vol. 1060, pp. 79-82, 2008.
  • 12. Lee, H., Kong, G. & Kim, S.: Optimum Ship Routing and It’s Implementation on the Web. Lecture Notes in Computer Science, Vol. 2402/2002, pp. 11-34, 2002.
  • 13. Montes, A. A.: Network Shortest Path Application for Optimum Track Ship Routing, Master Thesis, U.S. Naval Postgraduate School, Monterey, California, 2005.
  • 14. Motte, R. Burns, R. S. and Calvert, S.: An Overview of Current Methods Used in Weather Routeing. The Journal of Navigation, Vol. 41, No. 1, pp. 101-114, 1988.
  • 15. Motte, R. and Calvert, S.: Operational Considerations and Constraints in Ship-based Weather Routeing Procedures. The Journal of Navigation, Vol. 41, No. 3, pp. 417-433, 1988.
  • 16. Motte, R. and Calvert, S.,: On The Selection of Discrete Grid Systems for On-Board Micro-based Weather Routeing. The Journal of Navigation, Vol. 43, No. 1, pp. 104-117, 1990.
  • 17. Motte, R., Fazal, R., Epshteyn, M. Calvert, S. and Wojdylak, H.: Design and Operation of a Computerized, On-Board, Weather Routeing System. The Journal of Navigation, Vol. 47, No. 1, pp. 54-69, 1994.
  • 18. Smierzchalski, R., Michalewicz, Z.: Modeling of ship trajectory in collision situations by an evolutionary algorithm. IEEE Transactions On Evolutionary Computation, Vol. 4, pp. 227-241, 2000.
  • 19. Szlapczynska, J., Smierzchalski, R.: Adopted Isochrone Method Improving Ship Safety in Weather Routing with Evolutionary Approach. International Journal of Reliability Quality and Safety Engineering, Vol. 14, No. 6, pp. 635-646, 2007.
  • 20. Tang, X.-T., Fen, G.-S., Zhao, W.-F.: Application of Dynamic Programming in Designing Ship’s Optimum Route, in Chinese. Journal of Guangzhou Maritime College, Vol. 17, No. 2, pp. 1820, 2009.
  • 21. Tian, W. and Zhan, A.,: Research on Path Planning for UCAV based on Improved Ant Colony Algorithm, in Chinese. Fire Control and Command Control, Vol. 33, No. 11, pp. 69-72, 2008.
  • 22. Tsou, M.-C., Kao, S.-L., Su, C.-M.: Decision Support for Genetic Algorithms for Ship Collision Avoidance Route Planning. The Journal of Navigation, Vol. 63, pp. 167-182, 2010.
  • 23. Tsou, M.-C.,: Integration of a Geographic Information System and Evolutionary Computation for Automatic Routing in Coastal Navigation. The Journal of Navigation, Vol. 63, pp. 323-341, 2010.
  • 24. Wang, F., Jia, C.: The Study on The Optimal Ship Routing, in Chinese. Journal of Dalian Maritime University, Vol. 24, No. 2, pp. 61-64, 1998.
  • 25. Wei, X., Yu, Z., Wang, Z.: Design of Optimum Ship Route Based on Dynamic Programming, in Chinese Navigation of China, Vol. 57, pp. 16-18, 2003.
  • 26. Wei, S., Zhou, P.: Development of a 3D Dynamic Programming Method for Weather Routing. International Journal on Marine Navigation and Safety of Sea Transportation, Vol. 6, No. 1, pp. 79-83, 2012.
  • 27. Zhou, P., Chen, H.-W.: Improved Calculation Method of the Shortest Time Route, in Chinese. Science Technology and Engineering, Vol. 18, No. 21, pp. 5876-5880, 2008.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.agro-00285ba9-120b-48fe-af07-aef2cbdddf8e
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