International Conference on Operations Research and Enterprise Systems

Operations Research and Enterprise Systems pp 165-190 | Cite as

A Simulation Study of Evaluation Heuristics for Tug Fleet Optimisation Algorithms

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 577)

Abstract

Tug fleet optimisation algorithms can be designed to solve the problem of dynamically positioning a fleet of tugs in order to mitigate the risk of oil tanker drifting accidents. In this paper, we define the 1D tug fleet optimisation problem and present a receding horizon genetic algorithm for solving it. The algorithm can be configured with a set of cost functions such that each configuration effectively constitute a unique tug fleet optimisation algorithm. To measure the performance, or merit, of such algorithms, we propose two evaluation heuristics and test them by means of a computational simulation study. Finally, we discuss our findings and some of our related work on a parallel implementation and an alternative 2D nonlinear mixed integer programming formulation of the problem.

Keywords

Receding horizon control Genetic algorithm Computational simulation Dynamic optimisation Algorithm evaluation Modelling Risk mitigation 

References

  1. 1.
    Bye, R.T., Schaathun, H.G.: Evaluation heuristics for tug fleet optimisation algorithms: a computational simulation study of a receding horizon genetic algorithm. In: Proceedings of the 4th International Conference on Operations Research and Enterprise Systems (ICORES 2015), pp. 270–282 (2015)Google Scholar
  2. 2.
    Havforskningsinstituttet: Fisken og havet, særnummer 1a–2010: Det faglige grunnlaget for oppdateringen av forvaltningsplanen for Barentshavet og havområdene utenfor Lofoten. Technical report, Institute of Marine Research (Havforskningsinstituttet) (2010)Google Scholar
  3. 3.
    Bye, R.T., van Albada, S.B., Yndestad, H.: A receding horizon genetic algorithm for dynamic multi-target assignment and tracking: a case study on the optimal positioning of tug vessels along the northern Norwegian coast. In: Proceedings of the International Conference on Evolutionary Computation (ICEC 2010) – part of the International Joint Conference on Computational Intelligence (IJCCI 2010), pp. 114–125 (2010)Google Scholar
  4. 4.
    Bye, R.T.: A receding horizon genetic algorithm for dynamic resource allocation: a case study on optimal positioning of tugs. In: Madani, K., Dourado Correia, A., Rosa, A., Filipe, J. (eds.) Computational Intelligence. SCI, vol. 399, pp. 131–148. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  5. 5.
    Bye, R.T., Schaathun, H.G.: An improved receding horizon genetic algorithm for the tug fleet optimisation problem. In: Proceedings of the 28th European Conference on Modelling and Simulation, pp. 682–690 (2014)Google Scholar
  6. 6.
    Vardø VTS: The Vardø Vessel Traffic Service - For increased safety at sea. Information pamphlet (2011). http://kystverket.no
  7. 7.
    Vardø, VTS: Annual report on petroleum transport statistics. Technical report, Norwegian Coastal Administration (2014)Google Scholar
  8. 8.
    Vardø, VTS: Annual report on petroleum incidents. Technical report, Norwegian Coastal Administration (2015)Google Scholar
  9. 9.
    Vardø, VTS: Annual report on petroleum incidents. Technical report, Norwegian Coastal Administration (2014)Google Scholar
  10. 10.
    Eide, M.S., Endresen, Ø., Breivik, Ø., Brude, O.W., Ellingsen, I.H., Røang, K., Hauge, J., Brett, P.O.: Prevention of oil spill from shipping by modelling of dynamic risk. Mar. Pollut. Bull. 54, 1619–1633 (2007)CrossRefGoogle Scholar
  11. 11.
    Eide, M.S., Endresen, Ø., Brett, P.O., Ervik, J.L., Røang, K.: Intelligent ship traffic monitoring for oil spill prevention: risk based decision support building on AIS. Mar. Pollut. Bull. 54, 145–148 (2007)CrossRefGoogle Scholar
  12. 12.
    Assimizele, B., Oppen, J., Bye, R.T.: A sustainable model for optimal dynamic allocation of patrol tugs to oil tankers. In: Proceedings of the 27th European Conference on Modelling and Simulation, pp. 801–807 (2013)Google Scholar
  13. 13.
    Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. University of Michigan Press, Oxford (1975) Google Scholar
  14. 14.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Professional, Reading (1989) MATHGoogle Scholar
  15. 15.
    Haupt, R.L., Haupt, S.E.: Practical Genetic Algorithms, 2nd edn. Wiley, New York (2004)MATHGoogle Scholar
  16. 16.
    Goodwin, G.C., Graebe, S.F., Salgado, M.E.: Control System Design. Prentice Hall, Upper Saddle River (2001) Google Scholar
  17. 17.
    Maciejowski, J.M.: Predictive Control with Constraints, 1st edn. Prentice Hall, Upper Saddle River (2002)Google Scholar
  18. 18.
    Rossiter, J.A.: Model-Based Predictive Control. CRC Press, Boca Raton (2004)Google Scholar
  19. 19.
    Det Norske Veritas: Rapport Nr. 2009–1016. Revisjon Nr. 01. Tiltaksanalyse – Fartsgrenser for skip som opererer i norske farvann. Technical report, Sjøfartsdirektoratet (2009)Google Scholar
  20. 20.
    Schaathun, H.G.: Parallell slump (Om å parallellisera genetiske algoritmar i Haskell). In: Norsk Informatikkonferanse (NIK 2014) (2014)Google Scholar
  21. 21.
    Burton, F.W., Page, R.L.: Distributed random number generation. J. Funct. Program. 2, 203–212 (1992)CrossRefMathSciNetMATHGoogle Scholar
  22. 22.
    Claessen, K., Pałka, M.H.: Splittable pseudorandom number generators using cryptographic hashing. ACM SIGPLAN Not. 48, 47–58 (2013). ACMCrossRefGoogle Scholar
  23. 23.
    Frederickson, P., Hiromoto, R., Jordan, T.L., Smith, B., Warnock, T.: Pseudo-random trees in Monte Carlo. Parallel Comput. 1, 175–180 (1984)CrossRefMATHGoogle Scholar
  24. 24.
    Steele Jr., G.L., Lea, D., Flood, C.H.: Fast splittable pseudorandom number generators. ACM SIGPLAN Not. 49, 453–472 (2014)Google Scholar
  25. 25.
    Schaathun, H.G.: Evaluation of splittable pseudo-random generators. J. Funct. Program. (2015). Manuscript under revisionGoogle Scholar
  26. 26.
    Assimizele, B., Royset, J.O., Bye, R.T., Oppen, J.: Preventing Environmental Disasters from Grounding Accidents: A Case Study of Tugboat Positioning along the Norwegian Coast (2015, submitted)Google Scholar
  27. 27.
    Ni, Z., Qiu, Z., Su, T.: On predicting boat drift for search and rescue. Ocean Eng. 37, 1169–1179 (2010)CrossRefGoogle Scholar
  28. 28.
    Vanem, E., Endresen, Ø., Skjong, R.: Cost-effectiveness criteria for marine oil spill preventive measures. Reliab. Eng. Syst. Saf. 93, 1354–1368 (2008)CrossRefGoogle Scholar
  29. 29.
    Kontovas, C.A., Psaraftis, H.N., Ventikos, N.P.: An empirical analysis of IOPCF oil spill cost data. Mar. Pollut. Bull. 60, 1455–1466 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Software and Intelligent Control Engineering Laboratory, Faculty of Engineering and Natural SciencesAalesund University CollegeÅalesundNorway

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