Hostname: page-component-8448b6f56d-t5pn6 Total loading time: 0 Render date: 2024-04-17T22:14:16.638Z Has data issue: false hasContentIssue false
  • English
  • Français

A Novel Process Model for Marine Accident Analysis by using Generic Fuzzy-AHP Algorithm

Published online by Cambridge University Press:  15 August 2014

Bekir Sahin  and
Yunus Emre Senol
Bekir Sahin*
Affiliation:
(Surmene Faculty of Marine Science, Karadeniz Technical University, Trabzon, Turkey) (Maritime Department, Istanbul Technical University, Istanbul, Turkey)
Yunus Emre Senol
Affiliation:
(Surmene Faculty of Marine Science, Karadeniz Technical University, Trabzon, Turkey)
*
(E-mail: bekirs66@gmail.com)
Get access Rights & Permissions [Opens in a new window]

Abstract

Marine accident analysis is a sophisticated and complex official interpretation that requires a professional and fair judgment. For accidents such as a collision, contact and other incidents in the maritime field, the judgment mechanism of the courts depends on the decision process of the field experts. Field experts define the “Fraction Defectives” (FDs) of the vessels for the intended case based on the existing evidence and navigational specifications. However, evaluation of human judgment can be limited and problematic in analysing many aspects of a case. In this paper, a pairwise comparison method is used to simplify and clarify the judgment process. We aim to assess marine accidents in a stepwise approach that is inaccurately carried out in a holistic perspective by the field experts. A real accident that occurred in the past is simulated in front of the field experts. After conducting an expert consultation, FDs are re-calculated to compare both the regular judgment and the results in our suggested Generic Fuzzy Analytical Hierarchy Process (GF-AHP) approach.

Keywords

AlgorithmRadarNavigation
Type
Research Article
Information
The Journal of Navigation , Volume 68 , Issue 1 , January 2015 , pp. 162 - 183
Copyright
Copyright © The Royal Institute of Navigation 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Aguarón, J. and Moreno-Jiménez, J.M. (2003). The geometric consistency index: Approximated thresholds. European Journal of Operational Research, 147, 137–145.Google Scholar
Bellman, R.E. and Zadeh, L.A. (1977). Local and fuzzy logics, Modern Uses of Multiple- Valued Logic, Dunn, J.M. and Epstein, G. (eds.), 103–165.Google Scholar
Buckley, J.J. (1985). Fuzzy Hierarchical Analysis. Fuzzy Sets and System, 17, 233–247.Google Scholar
Bulut, E., Duru, O., Kececi, T. and Yoshida, S. (2012). Use of consistency index, expert prioritization and direct numerical inputs for generic fuzzy-AHP modeling: A process model for shipping asset management. Expert Systems with Applications, 39(2), 1911–1923.Google Scholar
Cakir, O. and Canpolat, M.S. (2008). A web-based decision support system for multi-criteria inventory classification using fuzzy AHP methodology. Expert Systems with Applications, 35, 1367–1378.Google Scholar
Chang, D.Y. (1996). Applications of the extent analysis method on fuzzy AHP. European Journal of Operational Research, 95, 649–655.Google Scholar
COLREGS (1972). [with amendments adopted from December 2009]. Convention on the International Regulations for Preventing Collisions at Sea. London: International Maritime Organization.Google Scholar
Crawford, G. and Williams, C. (1985). A note on the analysis of subjective judgment matrices. Journal of Mathematical Psychology, 29, 387–405.Google Scholar
Duru, O., Bulut, E. and Yoshida, S. (2012). Regime switching fuzzy AHP model for choice-varying priorities problem and expert consistency prioritization: A cubic fuzzy-priority matrix design. Expert Systems with Applications, 39, 4954–4964.Google Scholar
Laarhoven, P.J.M. and Pedrycz, W. (1983). A fuzzy extension of Saaty's priority theory. Fuzzy Sets and Systems, 11, 229–241.Google Scholar
Lee, M., Pham, H. and Zhang, X. (1999). A methodology for priority setting with application to software development process. European Journal of Operational Research, 118, 375–389.Google Scholar
Leung, L.C. and Cao, D. (2000). On consistency and ranking of alternatives in fuzzy AHP. European Journal of Operational Research, 124, 102–113.Google Scholar
Saaty, T.L. and Vargas, L.G. (1987). Uncertainty and rank order in the analytic hierarchy process. European Journal of Operational Research, 32, 107–117.Google Scholar
Saaty, T.L. (1980). The analytic hierarchy process. New York: McGraw- Hill.Google Scholar
Weck, M., Klocke, F., Schell, H. and Ruenauver, E. (1997). Evaluating alternative production cycles using the extended fuzzy AHP method. European Journal of Operational Research, 100, 351–366.CrossRefGoogle Scholar
Zadeh, L.A. (1975). The concept of linguistic variable and its application to approximate reasoning-I. Information Sciences, 8, 199–249.Google Scholar
Zadeh, L.A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353.Google Scholar