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Fuzzy Risk Assessment Based on Interval Numbers and Assessment Distributions

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Abstract

In risk assessment problems, multiple experts are often involved. On many occasions, assessment experts cannot give crisp scores even if there are scoring standards because of limitation and uncertainty. It is reasonable that the assessment data are expressed in the form of interval numbers with self-confidence. A fuzzy risk assessment model based on interval numbers with self-confidence is proposed in this paper. First, a multi-expert interval numbers with self-confidence fusion model is constructed. In the model, experts weights are determined based on subjective weights and objective weights, and the objective weights are calculated based on the length of the base of interval number and self-confidence simultaneously. Second, a novel method determining the symbolic proportion in an assessment distribution is proposed. This method integrates the concept of similarity measure between generalized fuzzy numbers and the length of the base of intersection of fuzzy numbers. Some properties of the proposed symbolic proportion measure are proved. Third, a fuzzy risk assessment model is proposed based on the fuzzy inference system. The output of the model is a distribution with the possible risk ranks and corresponding symbolic proportions. Finally, an illustrative example which shows the proposed fuzzy risk assessment model effective is demonstrated.

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References

  1. Kangari, R., Riggs, L.S.: Construction risk assessment by linguistics. IEEE Trans. Eng. Manag. 36(2), 147–159 (1989)

    Google Scholar 

  2. Andersen, M.C., Adams, H., et al.: Risk assessment for invasive species. Risk Anal. 24(4), 787–793 (2004)

    Google Scholar 

  3. Xin, R., Yin, Z., Dan, M.F.: Risk matrix integrating risk attitudes based on utility theory. Risk Anal. 35(8), 1437–1447 (2015)

    Google Scholar 

  4. Tian, D.H., Yang, B.W., et al.: A multi-experts and multi-criteria risk assessment model for safety risks in oil and gas industry integrating risk attitudes. Knowl. Based Syst. 156, 62–73 (2018)

    Google Scholar 

  5. Tian, D.H., Zhao, C.L., Wang, B., Zhou, M.: A MEMCIF-IN method for safety risk assessment in oil and gas industry based interval numbers and risk attitudes. Eng. Appl. Artif. Intell. 85, 269–283 (2019)

    Google Scholar 

  6. Markowski, A.S., Mannan, M.S.: Fuzzy risk matrix. J. Hazard. Mater. 159(1), 152–157 (2008)

    Google Scholar 

  7. Tah, J.H.M., Carr, V.: A proposal for construction project risk assessment using fuzzy logic. Construct. Manag. Econ. 18(4), 491–500 (2000)

    Google Scholar 

  8. Wuab, D.D., Wu, D., Olsone, D.L.: Corrigenda to: “fuzzy multi-objective programming for supplier selection and risk modeling: a possibility approach”. Eur. J. Operat. Res. 216(1), 255–256 (2012)

    Google Scholar 

  9. Dikmen, I., Birgonul, M.T., Han, S.: Using fuzzy risk assessment to rate cost overrun risk in international construction projects. Int. J. Project Manag. 25(5), 494–505 (2007)

    Google Scholar 

  10. Wei, S.H., Chen, S.M.: Fuzzy risk analysis based on interval-valued fuzzy numbers. Expert Syst. Appl. 36(2), 2285–2299 (2009)

    Google Scholar 

  11. Liang, R.X., Wang, J.Q., Zhang, H.Y.: Projection-based PROMETHEE methods based on hesitant fuzzy linguistic term sets. Int. J. Fuzzy Syst. 20(7), 2161–2174 (2018)

    Google Scholar 

  12. Liao, H.C., Xu, Z.S., Herrera-Viedma, E.: Hesitant fuzzy linguistic term set and its application in decision making: a state-of-the-art survey. Int. J. Fuzzy Syst. 20(7), 2084–2110 (2018)

    MathSciNet  Google Scholar 

  13. Zhao, X., Hwang, B.G., Gao, Y.: A fuzzy synthetic evaluation approach for risk assessment: a case of singapore’s green projects. J. Clean. Prod. 115, 203–213 (2016)

    Google Scholar 

  14. Islam, M.S., Nepal, M.P., et al.: Current research trends and application areas of fuzzy and hybrid methods to the risk assessment of construction projects. Adv. Eng. Inf. 33, 112–131 (2017)

    Google Scholar 

  15. Chen, S.M., Chen, J.H.: Fuzzy risk analysis based on similarity measures between interval-valued fuzzy numbers and interval-valued fuzzy number arithmetic operators. Expert Syst. Appl. 36(3), 6309–6317 (2018)

    Google Scholar 

  16. Cho, H.N., Choi, H.H., Kim, Y.B.: A risk assessment methodology for incorporating uncertainties using fuzzy concepts. Reliabil. Eng. Syst. Saf. 78(2), 173–183 (2002)

    Google Scholar 

  17. Nieto-Morote, A., Ruz-Vila, F.: A fuzzy approach to construction project risk assessment. Int. J. Proj. Manag. 29(2), 220–231 (2011)

    MATH  Google Scholar 

  18. Ilbahar, E., Karaşan, A., Cebi, S., Kahraman, C.: A novel approach to risk assessment for occupational health and safety using pythagorean fuzzy ahp & fuzzy inference system. Saf. Sci. 103(3), 124–136 (2018)

    Google Scholar 

  19. Huang, J., Li, Z., Liu, H.C.: New approach for failure mode and effect analysis using linguistic distribution assessments and todim method. Reliabil. Eng. Syst. Saf. 167, 302–309 (2017)

    Google Scholar 

  20. Zhang, Z., Guo, C., Martínez, L.: Managing multigranular linguistic distribution assessments in large-scale multiattribute group decision making. IEEE Trans. Syst. Man Cyber. Syst. 47(11), 3063–3076 (2017)

    Google Scholar 

  21. Wu, Y., Zhang, H., Dong, Y.: Linguistic distribution assessments with interval symbolic proportions. Knowl. Based Syst. 82(C), 139–151 (2015)

    Google Scholar 

  22. Wu, Z., Xu, J.: Possibility distribution-based approach for magdm with hesitant fuzzy linguistic information. IEEE Trans. Cyber. 46(3), 694–705 (2016)

    Google Scholar 

  23. Ma, J., Fan, Z.P., Huang, L.H.: A subjective and objective integrated approach to determine attribute weights. Eur. J. Oper. Res. 112(2), 397–404 (1999)

    MATH  Google Scholar 

  24. Xu, Z., Cai, X.: Minimizing group discordance optimization model for deriving expert weights. Group Decis. Negot. 21(6), 863–875 (2012)

    MathSciNet  Google Scholar 

  25. Cheng, D., Zhou, Z., Cheng, F., Wang, J.: Deriving heterogeneous experts weights from incomplete linguistic preference relations based on uninorm consistency. Knowl. Based Syst. 150, 150–165 (2018)

    Google Scholar 

  26. Moore, R.E.: Methods and applications of interval analysis, 10–25. Society for Industrial and Applied Mathematics, Philadephia (1979)

  27. Abootalebi, S., Hadi-Vencheh, A., Jamshidi, A.: An improvement to determining expert weights in group multiple attribute decision making problem. Group Decis. Negot. 27, 215–221 (2018)

    Google Scholar 

  28. Koksalmis, E., Kabak, d: Deriving decision makers’ weights in group decision making: An overview of objective methods. Inf. Fus. 49, 146–160 (2019)

    Google Scholar 

  29. Liu, W., Li, L.: An approach to determining the integrated weights of decision makers based on interval number group decision matrices. Knowl. Based Syst. 90, 92–98 (2015)

    Google Scholar 

  30. Herrera, F., Martínez, L.: A 2-tuple fuzzy linguistic representation model for computing with words. IEEE Trans. Fuzzy Syst. 8(6), 746–752 (2001)

    Google Scholar 

  31. Ni, H.H., Chen, A., Ning, C.: Some extensions on risk matrix approach. Saf. Sci. 48(10), 1269–1278 (2010)

    Google Scholar 

  32. Hsu, W.K.K., Huang, S.H.S., Tseng, W.J.: Evaluating the risk of operational safety for dangerous goods in airfreights-A revised risk matrix based on fuzzy AHP. Trans. Res. Part D. 48, 235–247 (2016)

    Google Scholar 

  33. Skorupski, J.: The simulation-fuzzy method of assessing the risk of air traffic accidents using the fuzzy risk matrix. Saf. Sci. 88, 76–87 (2016)

    Google Scholar 

  34. Herrera, F., Martínez, L.: An approach for combining linguistic and numerical information based on the 2-tuple fuzzy linguistic representation model in decision-making. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 8(5), 539–562 (2000)

    MathSciNet  MATH  Google Scholar 

  35. Chen, S.H., Hsieh, C.H.: Ranking generalized fuzzy number with graded mean integration representation. In: Proceedings of the eighth international conference of fuzzy sets and systems association world congress. 2, 551–555 (1999)

  36. Zhang, G., Dong, Y., Xu, Y.: Consistency and consensus measures for linguistic preference relations based on distribution assessments. Inf. Fus. 17(1), 46–55 (2014)

    Google Scholar 

  37. Chen, S.M.: New methods for subjective mental workload assessment and fuzzy risk analysis. J. Cybern. 27(5), 449–472 (1996)

    MathSciNet  MATH  Google Scholar 

  38. Chen, S.J., Chen, S.M.: Fuzzy risk analysis based on similarity measures of generalized fuzzy numbers. IEEE Press. 11(1), 45–56 (2003)

    Google Scholar 

  39. Xu, Z., Shang, S., et al.: A method for fuzzy risk analysis based on the new similarity of trapezoidal fuzzy numbers. Expert Syst. Appl. 37(3), 1920–1927 (2010)

    Google Scholar 

  40. Khorshidi, H.A., Nikfalazar, S.: An improved similarity measure for generalized fuzzy numbers and its application to fuzzy risk analysis. Appl. Soft Comput. 52, 478–486 (2017)

    Google Scholar 

  41. Ruge, B.: Risk matrix as tool for risk assessment in the chemical process industries. Probab. Saf. Assess. Manag. 6, 2693–2698 (2004)

    Google Scholar 

  42. Zhang, K., Duan, M., et al.: A fuzzy risk matrix method and its application to the installation operation of subsea collet connector. J. Loss Prevent. Process Ind. 45, 147–159 (2017)

    Google Scholar 

  43. Can, G.F., Toktas, P.: A novel fuzzy risk matrix based risk assessment approach. Kybernetes. 47(9), 1721–1751 (2018)

    Google Scholar 

  44. Gilbert, H., Spanjaard, O.: A double oracle approach for minmax regret optimization problems with interval data. Eur. J. Oper. Res. 262(3), 929–943 (2017)

    MathSciNet  MATH  Google Scholar 

  45. Yager, R.R.: Owa aggregation over a continuous interval argument with applications to decision making. In: IEEE transactions on systems an cybernetics part B cybernetics a Publication of the IEEE systems man and cybernetics society. 34(5), 1952–1963 (2004)

    Google Scholar 

Download references

Acknowledgements

This research is jointly supported by the Program of Science and Technology of Sichuan Province of China (No. 20YYJC0145), and the Science and Technology Innovation Team of Education Department of Sichuan Province for Dynamical System and its Applications (No. 18TD0013). The numerical calculations in this paper have been done on the super-computing system in the Super-computing Center for science and engineering of Southwest Petroleum University.

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  1. School of Sciences, Southwest Petroleum University, Chengdu, China

    Donghong Tian, Yong Wang & Ting Yu

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  1. Donghong Tian
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  2. Yong Wang
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  3. Ting Yu
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Correspondence to Donghong Tian.

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Tian, D., Wang, Y. & Yu, T. Fuzzy Risk Assessment Based on Interval Numbers and Assessment Distributions. Int. J. Fuzzy Syst. 22, 1142–1157 (2020). https://doi.org/10.1007/s40815-020-00837-6

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  • DOI: https://doi.org/10.1007/s40815-020-00837-6

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