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Determining Attribute Weights Based on Heterogeneous Discriminating Power and Solution Reliability in Evidential Reasoning Approach

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Abstract

In multiple attribute decision analysis (MADA), the determination of attribute weights by using discriminating power (DCP) has become an important research topic. However, the reliability of a solution, which is an important factor in improving decision quality, has not been considered in existing studies on determining attribute weights with DCP. To address this, a method is proposed to objectively determine attribute weights with belief distributions (BDs) by considering three types of heterogeneous DCP (HDCP) and high solution reliability. The three types of HDCP in BDs are constructed to generate three sets of attribute weights in terms of entropy, deviation maximization and criteria importance through intercriteria correlation. The difference of belief degrees on grades and variation of the ordered preference intensity of grades are simultaneously considered in the HDCP. Three rules are proposed based on the majority principle to deal with possible conflicts among the three sets of attribute weights. By incorporating the three rules as constraints, a mixed-integer optimization model with the aim of maximizing the solution reliability is constructed to generate an integrated set of attribute weights. The maximum solution reliability will guarantee that the generated solution is satisfactory for the decision maker. Based on the integrated set of attribute weights, a pair of optimization models is further constructed on the condition that the solution reliability is the largest to determine the minimal and maximal expected utilities of the given alternatives, which will be further applied to generate a solution to the MADA problem. A strategic project selection problem is investigated using the proposed method to verify its applicability and validity.

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References

  1. Abootalebi, S., Hadi-Vencheh, A., Jamshidi, A.: An improvement to determining expert weights in group multiple attribute decision making problem. Group Decis. Negot. 27(2), 215–221 (2018)

    Google Scholar 

  2. Bao, T., Xie, X., Long, P., et al.: MADM method based on prospect theory and evidential reasoning approach with unknown attribute weights under intuitionistic fuzzy environment. Expert Syst. Appl. 88, 305–317 (2017)

    Google Scholar 

  3. Bottomley, P.A., Doyle, J.R.: A comparison of three weight elicitation methods: good, better, and best. Omega 29(6), 553–560 (2001)

    Google Scholar 

  4. Bubboloni, D., Gori, M.: Anonymous and neutral majority rules. Soc. Choice Welfare 43(2), 377–401 (2014)

    MathSciNet  MATH  Google Scholar 

  5. Chen, T.: Interval-valued intuitionistic fuzzy QUALIFLEX method with a likelihood-based comparison approach for multiple criteria decision analysis. Inf. Sci. 261, 149–169 (2014)

    MathSciNet  MATH  Google Scholar 

  6. Deng, H., Yeh, C.H., Willis, R.J.: Inter-company comparison using modified TOPSIS with objective weights. Comput. Oper. Res. 27(10), 963–973 (2000)

    MATH  Google Scholar 

  7. Dong, Y., Liu, Y., Liang, H., et al.: Strategic weight manipulation in multiple attribute decision making. Omega 75, 1–6 (2017)

    Google Scholar 

  8. Doyle, J.R., Green, R.H., Bottomley, P.A.: Judging relative importance: direct rating and point allocation are not equivalent. Organ. Behav. Hum. Decis. Process. 70(1), 65–72 (1997)

    Google Scholar 

  9. Fei, L., Deng, Y.: Multi-criteria decision making in Pythagorean fuzzy environment. Appl. Intel. 1, 25 (2019). https://doi.org/10.1007/s10489-019-01532-2

    Article  Google Scholar 

  10. Figueira, J., Roy, B.: Determining the weights of criteria in the ELECTRE type methods with a revised Simos’ procedure. Eur. J. Oper. Res. 139(2), 317–326 (2002)

    MATH  Google Scholar 

  11. Fu, C., Chang, W., Liu, W., et al.: Data-driven group decision making for diagnosis of thyroid nodule. Sci. China Inf. Sci. 62(11), 212205 (2019)

    MathSciNet  Google Scholar 

  12. Fu, C., Chin, K.S.: Robust evidential reasoning approach with unknown attribute weights. Knowl. Based Syst. 59, 9–20 (2014)

    Google Scholar 

  13. Fu, C., Wang, Y.: An interval difference based evidential reasoning approach with unknown attribute weights and utilities of assessment grades. Comput. Ind. Eng. 81, 109–117 (2015)

    Google Scholar 

  14. Fu, C., Xu, D.L.: Determining attribute weights to improve solution reliability and its application to selecting leading industries. Ann. Oper. Res. 245(1–2), 401–426 (2016)

    MathSciNet  MATH  Google Scholar 

  15. Fu, C., Xu, D.L., Xue, M.: Determining attribute weights for multiple attribute decision analysis with discriminating power in belief distributions. Knowl. Based Syst. 143, 127–141 (2018)

    Google Scholar 

  16. Garg, H.: Generalized intuitionistic fuzzy entropy-based approach for solving multi-attribute decision-making problems with unknown attribute weights. Proc. Natl. Acad. Sci. India A 89(1), 129–139 (2019)

    MathSciNet  Google Scholar 

  17. Hatefi, M.A.: Indifference threshold-based attribute ratio analysis: a method for assigning the weights to the attributes in multiple attribute decision making. Appl. Soft Comput. 74, 643–651 (2019)

    Google Scholar 

  18. Horsky, D., Rao, M.R.: Estimation of attribute weights from preference comparisons. Manag. Sci. 30(7), 801–822 (1984)

    MathSciNet  MATH  Google Scholar 

  19. Khan, M.S.A., Ali, A., Abdullah, S., Amin, F., Hussain, F.: New extension of TOPSIS method based on pythagorean hesitant fuzzy sets with incomplete weight information. J. Intell Fuzzy Syst. 35(5), 5435–5448 (2018)

    Google Scholar 

  20. Liang, W., Wang, Y.M.: Interval-valued hesitant fuzzy stochastic decision-making method based on regret theory. Int. J. Fuzzy Syst. 22(4), 1091–1103 (2020)

    Google Scholar 

  21. Li, B., Zhang, Y., Xu, Z.: The medical treatment service matching based on the probabilistic linguistic term sets with unknown attribute weights. Int. J Fuzzy Syst. 22, 1487–1505 (2020)

    Google Scholar 

  22. Liu, P., Liu, X., Ma, G., et al.: A multi-attribute group decision-making method based on linguistic intuitionistic fuzzy numbers and dempster-shafer evidence theory. Int. J. Inf. Tech. Decis. 19(02), 499–524 (2020)

    Google Scholar 

  23. Liu, Y., Dong, Y., Liang, H., Chiclana, F., Herrera-Viedma, E.: Multiple attribute strategic weight manipulation with minimum cost in a group decision making context with interval attribute weights information. IEEE Trans. Syst. Man Cycle B 49(10), 1981–1992 (2019)

    Google Scholar 

  24. Li, Y.Z., et al.: Optimal power system dispatch with wind power integrated using nonlinear interval optimization and evidential reasoning approach. IEEE Trans. Power Syst. (2016). https://doi.org/10.1109/TPWRS.2015.2449667

    Article  Google Scholar 

  25. Pang, J., Guan, X., Liang, J., et al.: Multi-attribute group decision-making method based on multi-granulation weights and three-way decisions. Int. J. Approx. Reason 117, 122–147 (2020)

    MathSciNet  MATH  Google Scholar 

  26. Ng, C.Y.: Evidential reasoning-based fuzzy AHP approach for the evaluation of design alternatives’ environmental performances. Appl. Soft Comput. 46(C), 381–397 (2016)

    Google Scholar 

  27. Pei, Z.: Rational decision making models with incomplete weight information for production line assessment. Inf. Sci. 222, 696–716 (2013)

    MathSciNet  MATH  Google Scholar 

  28. Pena, J., Nápoles, G., Salgueiro, Y.: Explicit methods for attribute weighting in multi-attribute decision-making: a review study. Artif. Intell. Rev. 8, 1–26 (2019)

    Google Scholar 

  29. Perolat, J., Couso, I., Loquin, K., Strauss, O.: Generalizing the Wilcoxon rank-sum test for interval data. Int. J. Approx. Reason 56, 108–121 (2015)

    MathSciNet  MATH  Google Scholar 

  30. Saaty, T.L.: A scaling method for priorities in hierarchical structures. J. Math. Psychol. 15(3), 234–281 (1977)

    MathSciNet  MATH  Google Scholar 

  31. Shafer, G.A.: Mathematical theory of evidence. Princeton University Press, Princeton (1976)

    MATH  Google Scholar 

  32. Shirland, L.E., Jesse, R.R., et al.: Determining attribute weights using mathematical programming. Omega 31(6), 423–437 (2003)

    Google Scholar 

  33. Suo, M., Zhu, B., Zhang, Y., An, R., Li, S.: Fuzzy bayes risk based on mahalanobis distance and gaussian kernel for weight assignment in labeled multiple attribute decision making. Knowl. Based Syst. 152(15), 26–39 (2018)

    Google Scholar 

  34. Wang, C.Y., Chen, S.M.: A new multiple attribute decision making method based on linear programming methodology and novel score function and novel accuracy function of interval-valued intuitionistic fuzzy values. Inf Sci 438, 145–155 (2018)

    MathSciNet  MATH  Google Scholar 

  35. Wang, Y.M., Chin, K.S.: A linear goal programming priority method for fuzzy analytic hierarchy process and its applications in new product screening. Int. J. Approx. Reason 49(2), 451–465 (2008)

    MATH  Google Scholar 

  36. Wang, Y.M., Chin, K.S.: Fuzzy analytic hierarchy process: A logarithmic fuzzy preference programming methodology. Int. J. Approx. Reason 52(4), 541–553 (2011)

    MATH  Google Scholar 

  37. Wang, Y.M., Luo, Y.: Integration of correlations with standard deviations for determining attribute weights in multiple attribute decision making. Math. Comput. Model 51(1–2), 1–12 (2010)

    MathSciNet  MATH  Google Scholar 

  38. Wang, Y.M., Yang, J.B., Xu, D.L.: Environmental impact assessment using the evidential reasoning approach. Eur. J. Oper. Res. 174(3), 1885–1913 (2006)

    MATH  Google Scholar 

  39. Wan, S., Dong, J.: Interval-valued intuitionistic fuzzy mathematical programming method for hybrid multi-criteria group decision making with interval-valued intuitionistic fuzzy truth degrees. Inf. Fusion 26, 49–65 (2015)

    Google Scholar 

  40. Wan, S.P., Xu, G.L., Wang, F., et al.: A new method for Atanassov’s interval-valued intuitionistic fuzzy MAGDM with incomplete attribute weight information. Inf. Sci. 316, 329–347 (2015)

    MATH  Google Scholar 

  41. Xue, M., Fu, C., Feng, N.P., et al.: Evaluation of supplier performance of high-speed train based on multi-stage multi-criteria decision-making method. Knowl Based Syst 162, 238–251 (2018)

    Google Scholar 

  42. Yalçin, N., Ünlü, U.: A multi-criteria performance analysis of Initial Public Offering (IPO) firms using CRITIC and VIKOR methods. Technol. Econ. Dev. Econ. 24(2), 534–560 (2018)

    Google Scholar 

  43. Yu, V.F., Hu, K.J.: An integrated fuzzy multi-criteria approach for the performance evaluation of multiple manufacturing plants. Comput. Ind. Eng. 58(2), 269–277 (2010)

    Google Scholar 

  44. Zhou, M., Liu, X.B., Chen, Y.W., et al.: Assignment of attribute weights with belief distributions for MADM under uncertainties. Knowl. Based Syst. 189, 105110 (2020)

    Google Scholar 

  45. Zhou, M., Liu, X.B., Yang, J.B., et al.: Evidential reasoning approach with multiple kinds of attributes and entropy-based weight assignment. Knowl. Based Syst. 163, 358–375 (2019)

    Google Scholar 

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Acknowledgements

This research is supported by the National Natural Science Foundation of China (Grant Nos. 72001063 and 71571060) and by the Fundamental Research Funds for the Central Universities (JZ2020HGTA0082).

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Correspondence to Min Xue.

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Liu, Y., Xue, M. Determining Attribute Weights Based on Heterogeneous Discriminating Power and Solution Reliability in Evidential Reasoning Approach. Int. J. Fuzzy Syst. 23, 2235–2251 (2021). https://doi.org/10.1007/s40815-021-01092-z

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  • DOI: https://doi.org/10.1007/s40815-021-01092-z

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