A Multi-Criteria Decision-Making Method Based Upon Type-2 Interval Fuzzy Sets For Auxiliary Systems Of A Ship’s Main Diesel Engine

Authors

  • Abit BALIN

DOI:

https://doi.org/10.18201/ijisae.2017528727

Keywords:

Interval type-2 fuzzy sets, VIKOR, troubleshooting, ship main diesel engine

Abstract

Abstract: A well-qualified ship engine conductor having an effective error detection system is required to find failure as a result of which action are of immediate to be taken to prevent any possible engine impairments. Otherwise failures cumulatively can end up with crippling and irreversible profit loss. This paper proposes a fuzzy MADM methodology can help determine the most effective system for a ship’s main diesel engine. A novel interval type-2 fuzzy MADM method is chosen for the study, resting on VIKOR, to assess and employ the failure detection of auxiliary systems of a marine diesel engine. The evaluation is conducted by various groups of experts. It has been presumed that this study will also work out as a useful future maintenance process reference for marine engineering operators. All the same, the importance of the using time effectively to determine and respond to such failures is also underlined within the study. The results reveal that a fuel system is categorized as the most effective alternative followed subsequently by governor system, air supply system, and lastly cooling system. The results are grounded on the opinions expressed by three decision-making groups who put the MDEAS alternatives according to twenty ably selected criteria

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References

. Yeh C.H., and Chang Y.H., Modelling subjective evaluation for fuzzy group multi-criteria decision making, European Journal of Operational Research, 2009Vol. 194, 464–473.

. Ma J., Lu J., and Zhang G.Q., Decider: a fuzzy multi-criteria group decision support system, Knowledge-Based Systems, 2010, Vol. 23, 23–31.

. Fan Z.P., and Liu Y., A method for group decision-making based on multi-granularity uncertain linguistic information, Expert Systems with Applications, 2010, Vol. 37(5), 4000–4008.

. Cebi S., Celik M., Kahraman C., and Er I. D., An expert system auxiliary machinery troubleshooting: Shipamtsolver, Expert Systems with Applications, 2009, Vol. 36, 7219-7227.

. Calder N., Marine diesel engines, maintenance troubleshooting and repair, 2nd ed., International Marine, Camden, Maine. 1992.

. Liu H.C., Liu L., Liu N., and Mao L.X. Risk evaluation in failure mode and effects analysis with extended VIKOR method under fuzzy environment." Expert Systems with Applications, 2012, Vol.39.17 pp. 12926-12934.

. Vinodh S., Sarangan S. and Chandra Vinoth S.. Application of fuzzy compromise solution method for fit concept selection. Applied Mathematical Modelling. 2013.

. Martinez-Martin E., Escrig M. T., and Pobil A. P. Naming Qualitative Models Based on Intervals: A General Framework, International Journal of Artificial Intelligence, 2013, Vol. 11.A13 pp. 74-92.

. Lee, L. W., and Chen, S. M. “Fuzzy multiple attributes hierarchical group decision-making based on the ranking values of interval type-2 fuzzy sets”. In Machine Learning and Cybernetics, 2008 International Conference on IEEE: 2008, 3266-3271.

. Chen, S. M., and Lee, L. W., “Fuzzy multiple attributes group decision-making based on the interval type-2 TOPSIS method”. Expert Systems with Applications, 2010, 37(4): 2790-2798.

. Chen, S. M., Yang, M. W., Lee, L. W., and Yang, S. W. “Fuzzy multiple attributes group decision-making based on ranking interval type-2 fuzzy sets”. Expert Systems with Applications, 2012, 39(5): 5295-5308.

. Chen, T. Y., “Multiple criteria group decision-making with generalized interval-valued fuzzy numbers based on signed distances and incomplete weights”. Applied Mathematical Modelling, 2012, 36(7): 3029-3052.

. Mendel, J. M., John, R. I., and Liu, F., “Interval type-2 fuzzy logic systems made simple”. Fuzzy Systems, IEEE Transactions on, 2006, 14(6): 808-821.

. Lee, L. W., and Chen, S. M. “A new method for fuzzy multiple attributes group decision-making based on the arithmetic operations of interval type-2 fuzzy sets”. In Machine Learning and Cybernetics, 2008 International Conference on IEEE: 2008, 3084-3089.

. Celik, E., Bilisik, O. N., Erdogan, M., Gumus, A. T., & Baracli, H., An integrated novel interval type-2 fuzzy MCDM method to improve customer satisfaction in public transportation for Istanbul. Transportation Research Part E: Logistics and Transportation Review, 2013, 58, 28-51.

. Celik, E., Gumus, A. T., & Alegoz, M. A trapezoidal type-2 fuzzy MCDM method to identify and evaluate critical success factors for humanitarian relief logistics management. Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology, 2014, 27(6), 2847-2855.

. Wang, Y. M., and Elhag, T., “Fuzzy TOPSIS method based on alpha level sets with an application to bridge risk assessment”. Expert Systems with Applications, 2006, 31(2): 309-319.

. Opricovic, S., Multicriteria optimization of civil engineering systems. Faculty of Civil Engineering, Belgrade, 1998, 2(1), 5-21.

. Opricovic, S., & Tzeng, G. H., Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research, 2004, 156(2), 445-455.

. Tzeng, G. H., Lin, C. W., & Opricovic, S., Multi-criteria analysis of alternative-fuel buses for public transportation. Energy Policy, 2005, 33(11), 1373-1383.

. Vahdani, B., Hadipour, H., Sadaghiani, J. S., & Amiri, M., Extension of VIKOR method based on interval-valued fuzzy sets. The International Journal of Advanced Manufacturing Technology, 2010, 47(9-12), 1231-1239.

. Kuo, M. S., A novel interval-valued fuzzy MCDM method for improving airlines’ service quality in Chinese cross-strait airlines. Transportation Research Part E: Logistics and Transportation Review, 2011, 47(6), 1177-1193.

. Wan, S. P., Wang, Q. Y., & Dong, J. Y., The extended VIKOR method for multi-attribute group decision making with triangular intuitionistic fuzzy numbers. Knowledge-Based Systems, 2013, 52, 65-77.

. Ju, Y., & Wang, A., Extension of VIKOR method for multi-criteria group decision making problem with linguistic information. Applied Mathematical Modelling, 2013, 37(5), 3112-3125.

. Celik, E., Gul, M., Aydin, N., Gumus, A. T., & Guneri, A. F., A comprehensive review of multi criteria decision making approaches based on interval type-2 fuzzy sets. Knowledge-Based Systems. 2015,

. Celik, E., Aydin, N., & Gumus, A. T., A multiattribute customer satisfaction evaluation approach for rail transit network: A real case study for Istanbul, Turkey. Transport Policy, 2014, 36, 283-293.

. Kuo,M.S.,Liang,G.S., A soft computing method of performance evaluation with MCDM based on interval-valued fuzzy numbers. Applied Soft Computing, 2012, 12(1), 476–485.

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Published

29.06.2017

How to Cite

BALIN, A. (2017). A Multi-Criteria Decision-Making Method Based Upon Type-2 Interval Fuzzy Sets For Auxiliary Systems Of A Ship’s Main Diesel Engine. International Journal of Intelligent Systems and Applications in Engineering, 5(2), 44–51. https://doi.org/10.18201/ijisae.2017528727

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Section

Research Article