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Modelling the Decision Process in Computer Simulation of Ship Navigation

Published online by Cambridge University Press:  21 October 2009

M. K. James
M. K. James
Affiliation:
James Cook University of North Queensland
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Extract

1. INTRODUCTION. The Collision Regulations are designed to assist navigators to avoid collisions at sea. However, because of their qualitative nature it has been proposed that the Regulations should be supplemented by quantitative references to give practical meaning to concepts like ‘safe’ passing distance, ‘early’ action, etc. (Cockcroft and Lameijer, 1982). The problem of interpretation and quantification of the Regulations has been analysed in a recent paper by Wu (1984), considering in particular the concepts of ‘substantial’ action to avoid a collision, and the distance of ‘last-minute’ action in turning to avoid a collision. These analyses, along with many others, are concerned with developing prescriptive guidelines for mariners, in accordance with the Regulations.

Type
Research Article
Information
The Journal of Navigation , Volume 39 , Issue 1 , January 1986 , pp. 32 - 48
Copyright
Copyright © The Royal Institute of Navigation 1986

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References

REFERENCES

Bellman, R. E. and Zadeh, L. A. (1970). Decision-making in a fuzzy environment. Management Science, 17, B-141.CrossRefGoogle Scholar
Cockcroft, A. N. and Lameijer, J. (1982). A Guide to the Collision Avoidance Rules. 3rd ed. London: Stanford Maritime Press.Google Scholar
Coldwell, T. G. (1981). Marine traffic flow and casualities on the Humber. J. Navig. 3, 38.CrossRefGoogle Scholar
Coldwell, T. G. (1983). Marine traffic behaviour in restricted waters. J. Navig. 36, 430.CrossRefGoogle Scholar
Colley, B. A.Curtis, R. G. and Stockel, C. T. (1983). Manoeuvring times, domains and arenas. J. Navig. 36, 324.CrossRefGoogle Scholar
Colley, B. A., Curtis, R. G. and Stockel, C. T. (1984). A marine traffic flow and collision avoidance computer simulation. J. Navig. 37, 232.CrossRefGoogle Scholar
Davis, P. V., Dove, M. J.. and Stockel, C. T. (1980). A computer simulation of marine traffic using domains and arenas. J. Navig. 33, 215.CrossRefGoogle Scholar
Davis, P. V.Dove, M. J. and Stockel, C. T. (1982). A computer simulation of multi-ship encounters. J. Navig. 35, 347.CrossRefGoogle Scholar
Dubois, D.. and Prade, H. (1980). Fuzzy Sets and Systems: Theory and Applications. New York: Academic Press.Google Scholar
French, S. (1984). Fuzzy decision analysis: some criticisms. In Fuzzy Sets and Decision Analysis (ed. Zimmermann, H. J.et al.) p. 29. Amsterdam: North-Holland.Google Scholar
Goodwin, E. M. (1975). A statistical study of ship domains. J. Navig. 28, 328.CrossRefGoogle Scholar
Goodwin, E. M. and Kemp, J. F. (1980a). The study of real-life and simulated marine traffic flows for determining collision risks. In Automation for Safety in Shipping and Offshore Petroleum Operations, (ed. Aune, A. B. and Vlietstra, J.) p. 395. Amsterdam: North-Holland.Google Scholar
Goodwin, E. M. and Kemp, J. F. (1980b). Collision risks for fixed off-shore structures. J. Navig. 33. 351.CrossRefGoogle Scholar
Heideman, F. D. (1981). Analysis of Shipping Risk in the Great Barrier Reef Region. Undergraduate dissertation, Department of Civil and Systems Engineering, James Cook University of North Queensland.Google Scholar
Holmes, J. D. (1980). A statistical study of factors affecting navigation decision making. J. Navig., 33, 206.CrossRefGoogle Scholar
James, M. K., Jenssen, T. K.Lamberton, N. G. and Stark, K. P. (1985). Shipping Risk Simulation Study Report. Townsville: James Cook University of North Queensland (in preparation).Google Scholar
Kickert, W. J. M. (1978). Fuzzy Theories on Decision-making. Leiden: Martinus Nijhoff.Google Scholar
Kochen, M. (1975). Applications of fuzzy sets in psychology. In Fuzzy Sets and Their Applications to Cognitive and Decision Processes (ed. Zadeh, L. A.et al.) p. 395. New York: Academic Press.CrossRefGoogle Scholar
Mamdani, E. H., Ostergaard, J. J. and Lembessis, E. (1984). Use of fuzzy logic for implementing rule-based control of industrial processes. In Fuzzy Sets and Decision Analysis (ed. Zimmermann, H. J.et al.), p. 429. Amsterdam: North-Holland.Google Scholar
Wenstop, F. (1976). Deductive verbal models of organizations. Int.J. Man’machine Studies, 8, 293.CrossRefGoogle Scholar
Whalley, T. P. (1982). Marine traffic analysis. J. Navig. 35, 479.CrossRefGoogle Scholar
Wu, Z. L. (1984). Quantification of action to avoid collision. J. Navig. 37, 420.Google Scholar
Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338.CrossRefGoogle Scholar
Zadeh, L. A. (1984). Making computers think like people. IEEE Spectrum (August), 26.CrossRefGoogle Scholar
Zimmermann, H. J. (1984). Fuzzy programming and linear programming with several objective functions. In Fuzzy Sets and Decision Analysis (ed. Zimmermann, H. J.et al.) p. 109. Amsterdam: North-Holland.Google Scholar