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A MCMEIF-LT model for risk assessment based on linguistic terms and risk attitudes

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Abstract

Due to the limitation of assessors’ knowledge and the uncertainty of risks, risk assessment data reasonably are given in the form of linguistic terms, safety risk assessment in petrochemical industry is often a multi-criterion and multi-expert information fusion based on linguistic terms(MCMEIF-LT) problem. A novel model dealing with the MCMEIF-LT problem is presented in this paper. Firstly, the individual linguistic assessment distributions are fused to collective distributions and multiple criteria are fused to a comprehensive criterion. In the fusion process, the objective weights of assessment experts are calculated with using the credibilities of assessment data and the attitudes of decision makers are considered. Secondly, a Fuzzy Number Weighted Ordered Weighted Aggregation(FN-WOWA) operator which can transform a fuzzy number into a crisp value is proposed. In the FN-WOWA operator, the utility function can incorporate the assessors’ loss-based risk attitudes and the membership function can reflect the importance of the values in the integrated fuzzy number. Based on crisp values, a risk matrix is constructed. Finally, a real application is demonstrated to show the flexibility and practicality of the MCMEIF-LT model.

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References

  1. Markowski AS, Mannan MS (2008) Fuzzy risk matrix. J Hazard Mater 159(1):152–157

    Google Scholar 

  2. Ni HH, Chen A, Chen N (2010) Some extensions on risk matrix approach. Saf Sci 48(10):1269–1278

    Google Scholar 

  3. Abrilaba D (2012) Improving record linkage with supervised learning for disclosure risk assessment. Information Fusion 13(4):274–284

    Google Scholar 

  4. Ruan X, Yin ZY, Dan MF (2015) Risk matrix integrating risk attitudes based on utility theory. Risk Analysis 35(8):1437– 1447

    Google Scholar 

  5. Tian DH, Yang BW, et al. (2018) 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

    Google Scholar 

  6. Tian DH, Zhao CL, et al. (2019) A MEMCIF-IN method for safety risk assessment in oil and gas industry based on interval numbers and risk attitudes. Eng Appl Artif Intel 85:269–283

    Google Scholar 

  7. Pérez-Fernández R, Alonso P, Díaz I, Montes S (2014) Multi-factorial risk assessment: An approach based on fuzzy preference relations. Fuzzy Sets & Systems 278:67–80

    MathSciNet  MATH  Google Scholar 

  8. Wang Y, Tao ZW, et al. (2020) Dynamic analysis of oil-water two-phase flow for a multiple-fractured horizontal well with multiple finite-conductivity fractures in triple media carbonate reservoir Zeitschrift für Angewandte Mathematik und Mechanik. https://doi.org/10.1002/zamm.201900046

  9. Wang Y, Tao ZW, et al. (2020) Some novel results of T-periodic solutions for Rayleigh type equation with double deviating arguments. University Politehnica of Bucharest Scientific Bulletin-Series A-Applied Mathematics and Physics 82(1):55– 68

    MathSciNet  Google Scholar 

  10. Hsu WKK, Huang SHS, Tseng WJ (2016) Evaluating the risk of operational safety for dangerous goods in airfreights-a revised risk matrix based on fuzzy ahp. Transp Res Part D: Transp Environ 48:235–247

    Google Scholar 

  11. (2009) I. ISO, Risk management-Principles and guidelines, International Organization for Standardization, Geneva, Switzerland

  12. Xu ZS, Cai XQ (2012) Minimizing group discordance optimization model for deriving expert weights. Group Decis Negot 21:863–875

    Google Scholar 

  13. Cheng D, Zhou ZL, et al. (2018) Deriving heterogeneous experts weights from incomplete linguistic preference relations based on uninorm consistency. Knowl-Based Syst 150:150–165

    Google Scholar 

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

    Google Scholar 

  15. Koksalmis E, Kabak ö (2019) Deriving decision makers’ weights in group decision making: An overview of objective methods. Information Fusion 49:146–160

    Google Scholar 

  16. Liu BS, Shen YH, Chen Y, Chen XH, Wang YM (2015) A two-layer weight determination method for complex multi-attribute large-group decision-making experts in a linguistic environment. Information Fusion 23 (C):156–165

    Google Scholar 

  17. Brito AJ, Almeida ATD, Mota CMM (2010) A multi-criteria model for risk sorting of natural gas pipelines based on ELECTRE TRI integrating Utility Theory. Eur J Oper Res 200:812–821

    MATH  Google Scholar 

  18. Bao C, Wu D, Li J (2018) A knowledge-based risk measure from the fuzzy multicriteria decision-making perspective. IEEE Trans Fuzzy Syst 27:1126–1138

    Google Scholar 

  19. Zhao XF, Lin R, Wei GW (2013) Fuzzy prioritized operators and their application to multiple attribute group decision making. Applied Mathematical Medelling 37:4759–4770

    MathSciNet  MATH  Google Scholar 

  20. Tian DH, et al. (2020) Fuzzy risk assessment based on interval numbers and assessment distributions. International Journal of Fuzzy Systems. https://doi.org/10.1007/s40815-020-00837-6,

  21. Herrera F, MartÍnez L (2000) A 2-tuple fuzzy linguistic representation model for computing with words. IEEE Trans Fuzzy Syst 8:746–752

    Google Scholar 

  22. Wang JH, Hao JY (2006) A new version of 2-tuple fuzzy linguistic representation model for computing with words. IEEE Trans Fuzzy Syst 14:435–445

    Google Scholar 

  23. Rodríguez RM, Martínez L, Herrera F (2012) Hesitant fuzzy linguistic term sets for decision making. IEEE Trans Fuzzy Syst 20:109–119

    Google Scholar 

  24. Zhang G, Dong Y, Xu Y (2014) Consistency and consensus measures for linguistic preference relations based on distribution assessments. Information Fusion 17:46–55

    Google Scholar 

  25. Yager RR (1996) Quantifier guided aggregation using OWA operators. International Journal of Intelligent Systems 11(1):49–73

    Google Scholar 

  26. O’Hagan M (1988) Aggregating template or rule antecedents in real-time expert systems with fuzzy set logic. Asilomar Conference on IEEE Xplore 2:681–689

    Google Scholar 

  27. Xu ZS (2005) An overview of methods for determining owa weights. International Journal of Intelligent Systems 20(8):843–865

    MATH  Google Scholar 

  28. Wei SH, Chen SM (2009) Fuzzy risk analysis based on interval-valued fuzzy numbers. Expert Syst Appl 36(2-part-P1):2285–2299

    Google Scholar 

  29. Chen SM, Hong JA (2014) Multicriteria linguistic decision making based on hesitant fuzzy linguistic term sets and the aggregation of fuzzy sets. Inform Sci 286:63–74

    Google Scholar 

  30. Chen SJ, Chen SM (2007) Fuzzy risk analysis based on the ranking of generalized trapezoidal fuzzy numbers. Appl Intell 26(1):1–11

    Google Scholar 

  31. Hesamian G (2017) Measuring similarity and ordering based on interval type-2 fuzzy numbers. IEEE Trans Fuzzy Syst 25(4):788–798

    MathSciNet  Google Scholar 

  32. Zadeh LA (1965) Fuzzy sets. Information & Control 8:338–353

    MathSciNet  MATH  Google Scholar 

  33. Chen SH (1999) Ranking generalized fuzzy number with graded mean integration representation. In: Proceedings of the eighth international conference of fuzzy sets and systems association world congress, vol 2, pp 551–555

  34. Yager RR (1981) A procedure for ordering fuzzy subsets of the unit interval. Inform Sci 24:143–161

    MathSciNet  MATH  Google Scholar 

  35. Yatsalo B, Martinez L (2018) Fuzzy rank acceptability analysis: a confidence measure of ranking fuzzy numbers. IEEE Trans Fuzzy Syst 26:3579–3593

    Google Scholar 

  36. Brunelli M, Mezei J (2013) How different are ranking methods for fuzzy numbers? A numerical study. Int J Approx Reason 54:627–639

    MathSciNet  MATH  Google Scholar 

  37. Deng HP (2014) Comparing and ranking fuzzy numbers using ideal solutions. Appl Math Model 38:1638–1646

    MathSciNet  MATH  Google Scholar 

  38. Cheng CH (1998) A new approach for ranking fuzzy numbers by distance method. Fuzzy Set Syst 95:307–317

    MathSciNet  MATH  Google Scholar 

  39. Chen SM (1996) New methods for subjective mental workload assessment and fuzzy risk analysis. Cybern Syst 27:449– 472

    MathSciNet  MATH  Google Scholar 

  40. Almeida AT (2005) Multicriteria modelling of repair contract based on utility and electre i method with dependability and service quality criteria. Ann Oper Res 138:113–126

    MathSciNet  MATH  Google Scholar 

  41. Dong YC, Zhang HJ, Zhang GQ (2015) Multi-granular unbalanced linguistic distribution assessments with interval symbolic proportions. Knowl.-Based Syst 82:139–151

    Google Scholar 

  42. Zhang BW, Liang HM, Zhang GQ (2018) The optimization-based aggregation and consensus with minimum-cost in group decision making under incomplete linguistic distribution context. Knowl.-Based Syst 162:92–102

    Google Scholar 

  43. Yager RR, Xu Z (2006) The continuous ordered weighted geometric operator and its application to decision making. Fuzzy Sets & Systems 157(10):1393–1402

    MathSciNet  MATH  Google Scholar 

  44. Yager RR (2004) OWA aggregation over a continuous interval argument with applications to decision making. IEEE Transactions on Systems Man & Cybernetics Part B Cybernetics A Publication of the IEEE Systems Man & Cybernetics Society 34(5):1952–63

    Google Scholar 

  45. Donati F, Canuto E, Carlucci D, Villa A (1983) The c. s. s. approach to attitude reconstitution and raw data treatment. Nat Neurosci 5:1226–1235

    Google Scholar 

  46. Köbberling V, Wakker PP (2005) An index of loss aversion. J Econ Theory 122:119–131

    MathSciNet  MATH  Google Scholar 

  47. Rólczyński T., Forlicz M, Łukasz K (2017) Risk attitude in case of losses or gains-an experimental study. European Journal of Finance 23:1–13

    Google Scholar 

  48. Chen SJ, Chen SM (2003) Fuzzy risk analysis based on similarity measures of generalized fuzzy numbers. IEEE Trans Fuzzy Syst 11(1):45–56

    Google Scholar 

  49. Rouhparvar H, Panahi A (2015) A new definition for defuzzification of generalized fuzzy numbers and its application. Appl Soft Comput 30:577–584

    Google Scholar 

Download references

Acknowledgments

This research is jointly supported by the Youth Science and Technology Innovation Team of Southwest Petroleum University for Nonlinear Systems (No. 2017CXTD02), the Program of Science and Technology of Sichuan Province of China(No. 20QYCX0025)and the Science and Technology Innovation Team of Education Department of Sichuan 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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Authors and Affiliations

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

    Donghong Tian, Chao Min, Lingna Li & Jie Gao

  2. Institute for Artificial Intelligence, Southwest Petroleum University, Chengdu, China

    Chao Min

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  1. Donghong Tian
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  2. Chao Min
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  3. Lingna Li
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Correspondence to Donghong Tian.

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Tian, D., Min, C., Li, L. et al. A MCMEIF-LT model for risk assessment based on linguistic terms and risk attitudes. Appl Intell 50, 3318–3335 (2020). https://doi.org/10.1007/s10489-020-01737-w

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