Skip to main content

Advertisement

SpringerLink
Log in

Comprehensive Evaluation of Non-waste Cities Based on Two-Tuple Mixed Correlation Degree

  • Published:
International Journal of Fuzzy Systems Aims and scope Submit manuscript

Abstract

Under the background of vigorously promoting the construction of ecological civilization, the importance of constructing the “non-waste cities” becomes increasingly prominent. Taking 39 cities in the Yangtze River Economic Zone as the research objects, this paper establishes a new evaluation index system for “non-waste cities,” and proposes a multi-source, heterogeneous and multi-attribute decision-making method for the comprehensive evaluation of “non-waste cities.” Specifically, the evaluation index system of “non-waste cities” is constructed from four aspects: economic level, environmental pollution, resource consumption, and waste utilization. Considering that there is the characteristic of multi-source heterogeneity for the attribute values (that is, real numbers, interval numbers, and fuzzy linguistic variables coexist), the multi-source heterogeneous data are uniformly converted into two-tuples, and then a new two-tuple entropy weight method is proposed to determine the weights of evaluation attributes. Moreover, combining the traditional grey relational analysis method with TOPSIS, a multi-source, heterogeneous and multi-attribute decision-making method based on two-tuple mixed correlation degree (TTMCD-MSHMADM) is proposed to evaluate the “non-waste cities,” and an empirical analysis is made for the 39 cities in the Yangtze River Economic Zone. The result gives a theoretical basis for the formulation of sustainable economic development policies in the Yangtze River Economic Zone, and provides a decision reference for selecting the demonstration cities of “non-waste cities.”

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. General Office of the State Council of China: Construction pilot work plan of “non-waste cities”, Beijing (2019)

  2. Ministry of Ecology and Environment of the People’s Republic of China: A letter on the issuance of guidelines for the preparation of the pilot implementation plan for the construction of “non-waste cities” and the construction index system for “non-waste cities” (Trial implementation), Beijing (2019)

  3. Rao, C.J., Yan, B.J.: Study on the interactive influence between economic growth and environmental pollution. Environ. Sci. Pollut. Res. 27, 39442–39465 (2020)

    Google Scholar 

  4. Mao, S.H., Zhu, M., Wang, X.P., Xiao, X.P.: Grey–Lotka–Volterramodel for the competition and cooperation between third-party online payment systems and online banking in China. Appl. Soft Comput. 95, 106501 (2020)

    Google Scholar 

  5. Xiao, Q.Z., Shan, M.Y., Gao, M.Y., Xiao, X.P., Goh, M.: Parameter optimization for nonlinear grey Bernoulli model on biomass energy consumption prediction. Appl. Soft Comput. 95, 106538 (2020)

    Google Scholar 

  6. Rao, C.J., Lin, H., Liu, M.: Design of comprehensive evaluation index system for P2P credit risk of “three rural” borrowers. Soft. Comput. 24(15), 11493–11509 (2020)

    Google Scholar 

  7. Dou, P.F., Zuo, S.Z., Ren, Y., Huang, W.: Construction of an index system for green cities based on urban classification. Chin. J. Ecol. 38(6), 1937–1948 (2019)

    Google Scholar 

  8. Shi, B.F., Chi, G.T.: Green industry evaluation indicators screening model based on the maximum information content and its application. Syst. Eng. Theory Pract. 34(7), 1799–1810 (2014)

    Google Scholar 

  9. Li, X.H., Zhu, Q.G., Hu, J.Y.: The dynamic evaluation of the economic and social development of cities in the Yangtze River Economic Zone based on the “five-in-one” overall layout. Stat. Inf. Forum 33(7), 74–83 (2018)

    Google Scholar 

  10. Qu, S.J., Zhou, Y.Y., Zhang, Y.L., Wahab, M.I.M., Zhang, G., Ye, Y.Y.: Optimal strategy for a green supply chain considering shipping policy and default risk. Comput. Ind. Eng. 131, 172–186 (2019)

    Google Scholar 

  11. Xiao, X.P., Duan, H.M., Wen, J.H.: A novel car-following inertia gray model and its application in forecasting short-term traffic flow. Appl. Math. Model. 87, 546–570 (2020)

    MathSciNet  Google Scholar 

  12. Xiao, Q.Z., Chen, L., Xie, M., Wang, C.: Optimal contract design in sustainable supply chain: Interactive impacts of fairness concern and overconfidence. J. Oper. Res. Soc. (2020). https://doi.org/10.1080/01605682.2020.1727784. (in Press)

    Article  Google Scholar 

  13. Li, D.F., Chen, G.H., Huang, Z.G.: Linear programming method for multiattribute group decision making using IF sets. Inf. Sci. 180(9), 1591–1609 (2010)

    MathSciNet  MATH  Google Scholar 

  14. Liu, P.D., He, L., Yu, X.C.: Generalized hybrid aggregation operators based on the 2-dimension uncertain linguistic information for multiple attribute group decision making. Group Decis. Negot. 25(1), 103–126 (2016)

    Google Scholar 

  15. Gao, Y., Li, D.S.: A consensus model for heterogeneous multi-attribute group decision making with several attribute sets. Expert Syst. Appl. 125, 69–80 (2019)

    Google Scholar 

  16. Zhou, W., Xu, Z.S.: Envelopment analysis, preference fusion, and membership improvement of intuitionistic fuzzy numbers. IEEE Trans. Fuzzy Syst. (2019). https://doi.org/10.1109/TFUZZ.2019.2930483. (in Press)

    Article  Google Scholar 

  17. Wan, S.P., Li, D.F.: Fuzzy LINMAP approach to heterogeneous MADM considering comparisons of alternatives with hesitation degrees. Omega 41(6), 925–940 (2013)

    Google Scholar 

  18. Sanayei, A., Farid, M.S., Yazdankhah, A.: Group decision making process for supplier selection with VIKOR under fuzzy environment. Expert Syst. Appl. 37(1), 24–30 (2010)

    Google Scholar 

  19. Wan, S.P., Xu, J., Dong, J.Y.: Aggregating decision information into interval-valued intuitionistic fuzzy numbers for heterogeneous multi-attribute group decision making. Knowl.-Based Syst. 113, 155–170 (2016)

    Google Scholar 

  20. Zheng, E.Z., Teng, F., Liu, P.D.: Multiple attribute group decision-making method based on neutrosophic number generalized hybrid weighted averaging operator. Neural Comput. Appl. 28(8), 2063–2074 (2017)

    Google Scholar 

  21. Qu, G.H., Zhou, H.S., Qu, W.H., Li, C.H.: Shapley interval-valued dual hesitant fuzzy Choquet integral aggregation operators in multiple attribute decision making. J. Intell. Fuzzy Syst. 34(3), 1–19 (2017)

    Google Scholar 

  22. Wei, G.W.: Interval-valued dual hesitant fuzzy uncertain linguistic aggregation operators in multiple attribute decision making. J. Intell. Fuzzy Syst. 33(3), 1881–1893 (2017)

    MATH  Google Scholar 

  23. He, Z.C., Chan, F.T.S., Jiang, W.: A quantum framework for modelling subjectivity in multi-attribute group decision making. Comput. Ind. Eng. 124, 560–572 (2018)

    Google Scholar 

  24. Sun, B.Z., Ma, W.M., Chen, X.T., Li, X.N.: Heterogeneous multigranulation fuzzy rough set-based multiple attribute group decision making with heterogeneous preference information. Comput. Ind. Eng. 122, 24–38 (2018)

    Google Scholar 

  25. Yu, G.F., Li, D.F., Fei, W.: A novel method for heterogeneous multi-attribute group decision making with preference deviation. Comput. Ind. Eng. 124, 58–64 (2018)

    Google Scholar 

  26. Liang, D.C., Wang, M.W., Xu, Z.S.: Heterogeneous multi-attribute nonadditivity fusion for behavioral three-way decisions in interval type-2 fuzzy environment. Inf. Sci. 496, 242–263 (2019)

    Google Scholar 

  27. Yu, G.F., Fei, W., Li, D.F.: A compromise-typed variable weight decision method for hybrid multi-attribute decision making. IEEE Trans. Fuzzy Syst. 27(5), 861–872 (2019)

    Google Scholar 

  28. Krishankumar, R., Subrajaa, L.S., Ravichandran, K.S., Kar, S., Saeid, A.B.: A framework for multi-attribute group decision-making using double hierarchy hesitant fuzzy linguistic term set. Int. J. Fuzzy Syst. 21(4), 1130–1143 (2019)

    MathSciNet  Google Scholar 

  29. Liu, C.F., Luo, Y.S.: New aggregation operators of single-valued neutrosophic hesitant fuzzy set and their application in multi-attribute decision making. Pattern Anal. Appl. 22(2), 417–427 (2019)

    MathSciNet  Google Scholar 

  30. Wang, F., Du, D.Y., Zhao, J.: A novel SIR Choquet method for multiple attributes group decision-making with interval grey linguistic. Int. J. Fuzzy Syst. 21(6), 1771–1785 (2019)

    Google Scholar 

  31. Peng, J.J., Tian, C., Zhang, W.Y., Zhang, S., Wang, J.Q.: An integrated multi-criteria decision-making framework for sustainable supplier selection under picture fuzzy environment. Technol. Econ. Dev. Econ. 26(3), 573–598 (2020)

    Google Scholar 

  32. Tian, C., Peng, J.J., Zhang, W.Y., Zhang, S.J., Wang, Q.: Tourism environmental impact assessment based on improved AHP and picture fuzzy PROMETHEE II methods. Technol. Econ. Dev. Econ. 26(2), 355–378 (2020)

    Google Scholar 

  33. Zeng, S.Z., Chen, S.M., Fan, K.Y.: Interval-valued intuitionistic fuzzy multiple attribute decision making based on nonlinear programming methodology and TOPSIS method. Inf. Sci. 506, 424–442 (2020)

    Google Scholar 

  34. Enginoğlu, S., Memiş, S., Karaaslan, F.: A new approach to group decision-making method based on TOPSIS under fuzzy soft environment. J. New Results Sci. 8(2), 42–52 (2019)

    Google Scholar 

  35. Sun, B.Z., Zhou, X.M., Lin, N.N.: Diversified binary relation-based fuzzy multigranulation rough set over two universes and application to multiple attribute group decision making. Inf. Fusion 55, 91–104 (2020)

    Google Scholar 

  36. Wan, S.P., Zou, W.C., Dong, J.Y.: Prospect theory based method for heterogeneous group decision making with hybrid truth degrees of alternative comparisons. Comput. Ind. Eng. 141, 106285 (2020)

    Google Scholar 

  37. Sun, Y., Giles, C.L.: Popularity weighted ranking for academic digital libraries. Lect. Notes Comput. Sci. 4425, 605–612 (2007)

    Google Scholar 

  38. Radlinski, F., Craswell, N.: Comparing the sensitivity of information retrieval metrics. In: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 667–674 (2010)

  39. Ouadah, A., Hadjali, A., Nader, F., et al.: SEFAP: an efficient approach for ranking skyline web services. J. Ambient Intell. Humaniz. Comput. 10, 709–725 (2019)

    Google Scholar 

  40. Herrera, F., Martínez, L.: A 2-tuple fuzzy linguistic representation model for computing with words. IEEE Trans. Fuzzy Syst. 8(6), 746–752 (2000)

    Google Scholar 

  41. Rao, C.J., Xiao, X.P., Goh, M., Zheng, J.J., Wen, J.H.: Compound mechanism design of supplier selection based on multi-attribute auction and risk management of supply chain. Comput. Ind. Eng. 105, 63–75 (2017)

    Google Scholar 

  42. Rao, C.J., Goh, M., Zheng, J.J.: Decision mechanism for supplier selection under sustainability. Int. J. Inf. Technol. Decis. Mak. 16(1), 87–115 (2017)

    Google Scholar 

  43. Rao, C.J., Goh, M., Zhao, Y., Zheng, J.J.: Location selection of city logistics centers under sustainability. Transp. Res. Part D Transp. Environ. 36, 29–44 (2015)

    Google Scholar 

  44. Geng, X.L., Qiu, H.Q., Gong, X.M.: An extended 2-tuple linguistic DEA for solving MAGDM problems considering the influence relationships among attributes. Comput. Ind. Eng. 112, 135–146 (2017)

    Google Scholar 

  45. Rao, C.J., Liu, M., Goh, M., Wen, J.H.: 2-stage modified random forest model for credit risk assessment of P2P network lending to “Three Rurals” borrowers. Appl. Soft Comput. 95, 106570 (2020)

    Google Scholar 

  46. Wei, G.W.: 2-tuple intuitionistic fuzzy linguistic aggregation operators in multiple attribute decision making. Iran. J. Fuzzy Syst. 16(4), 159–174 (2019)

    MathSciNet  MATH  Google Scholar 

  47. Wang, L.D., Wang, Y.J.: Group decision-making approach based on generalized grey linguistic 2-tuple aggregation operators. Complexity 2301252 (2018)

  48. Rao, C.J., Zheng, J.J., Wang, C., Xiao, X.P.: A hybrid multi-attribute group decision making method based on grey linguistic 2-tuple. Iran. J. Fuzzy Syst. 13(2), 37–59 (2016)

    MathSciNet  MATH  Google Scholar 

  49. Wang, L.D., Wang, Y.J., Pedrycz, W.: Hesitant 2-tuple linguistic Bonferroni operators and their utilization in group decision making. Appl. Soft Comput. 77, 653–664 (2019)

    Google Scholar 

  50. Liu, P.D., Chen, S.M.: Multi-attribute group decision making based on intuitionistic 2-tuple linguistic information. Inf. Sci. 430–431, 599–619 (2018)

    MATH  Google Scholar 

  51. Xiao, Q.Z., Shan, M.Y., Xiao, X.P., Rao, C.J.: Evaluation model of industrial operation quality under multi-source heterogeneous data information. Int. J. Fuzzy Syst. 22(2), 522–547 (2020)

    Google Scholar 

  52. National Bureau of Statistics of China. China Statistical Yearbook. http://www.stats.gov.cn/tjsj/ndsj/. Accessed 28 Mar 2020

  53. EPS China data. EPS statistical data platform. https://www.epsnet.com.cn/index.html#/Home. Accessed 28 Mar 2020

  54. Martinez, L., Herrera, F.: An overview on the 2-tuple linguistic model for computing with words in decision making: Extensions, applications and challenges. Inf. Sci. 207, 1–18 (2012)

    MathSciNet  Google Scholar 

  55. Li, P., Rao, C.J., Goh, M., Yang, Z.Q.: Pricing strategies and profit coordination under a double echelon green supply chain. J. Clean. Prod. 278, 123694 (2021)

    Google Scholar 

  56. Herrera, F.: A model based on linguistic 2-tuple for dealing with multi-granularity hierarchical linguistic contexts in multi-expert decision-making. IEEE Trans. on Syst. Man Cybern. Part B Cybern. 31(2), 227–234 (2001)

    Google Scholar 

  57. Herrera, F., Martinez, L., Sanchez, P.J.: Managing non-homogeneous information in group decision making. Eur. J. Oper. Res. 166(11), 115–132 (2005)

    MATH  Google Scholar 

  58. Li, L., Liu, F., Li, C.B.: Customer satisfaction evaluation method for customized product development using entropy weight and Analytic Hierarchy Process. Comput. Ind. Eng. 77, 80–87 (2014)

    Google Scholar 

  59. Ye, J.: Multicriteria fuzzy decision-making method using entropy weights-based correlation coefficients of interval-valued intuitionistic fuzzy sets. Appl. Math. Model. 34(12), 3864–3870 (2010)

    MathSciNet  MATH  Google Scholar 

  60. Wang, R.H., Nan, G.F., Chen, L., Li, M.Q.: Channel integration choices and pricing strategies for competing dual-channel retailers. IEEE Trans. Eng. Manag. (2020). https://doi.org/10.1109/tem.2020.3007347. (in Press)

    Article  Google Scholar 

  61. Meng, W.J., Wang, C.S., Xing, N.: Gray relational TOPSIS multi-attribute decision making model based on mixed index. Math. Pract. Theory 48(24), 66–74 (2018)

    MATH  Google Scholar 

  62. Mao, S., Kang, Y., Zhang, Y., et al.: Fractional grey model based on non-singular exponential kernel and its application in the prediction of electronic waste precious metal content. ISA Trans. (2020). https://doi.org/10.1016/j.isatra.2020.07.023. (in Press)

    Article  Google Scholar 

  63. Wu, Y.L., Zhou, F., Kong, J.Z.: Innovative design approach for product design based on TRIZ, AD, fuzzy and Grey relational analysis. Comput. Ind. Eng. 140, 106276 (2020)

    Google Scholar 

  64. Rao, C.J., Zhao, Y., Zheng, J.J., Wang, C., Chen, Z.W.: An extended uniform-price auction mechanism of homogeneous divisible goods: supply optimisation and non-strategic bidding. Int. J. Prod. Res. 54(13), 4028–4042 (2016)

    Google Scholar 

  65. Xiao, Q., Gao, M., Xiao, X., Goh, M.: A novel grey Riccati-Bernoulli model and its application for the clean energy consumption prediction. Eng. Appl. Artif. Intell. 95, 103863 (2020)

    Google Scholar 

  66. Tian, C., Peng, J.J., Zhang, S., Zhang, W.Y., Wang, J.Q.: Weighted picture fuzzy aggregation operators and their applications to multi-criteria decision-making problems. Comput. Ind. Eng. 137, 106037 (2019)

    Google Scholar 

  67. Abdel-Basset, M., Saleh, M., Gamal, A., Smarandache, F.: An approach of TOPSIS technique for developing supplier selection with group decision making under type-2 neutrosophic number. Appl. Soft Comput. 77, 438–452 (2019)

    Google Scholar 

  68. Ding, Q.Y., Wang, Y.M.: Intuitionistic fuzzy TOPSIS multi-attribute decision making method based on revised scoring function and entropy weight method. J. Intell. Fuzzy Syst. 36(1), 625–635 (2019)

    Google Scholar 

  69. Zhu, G.N., Hu, J., Ren, H.L.: A fuzzy rough number-based AHP-TOPSIS for design concept evaluation under uncertain environments. Appl. Soft Comput. 91, 106228 (2020)

    Google Scholar 

Download references

Acknowledgements

This work is supported by the National Natural Science Foundation of China (Nos. 72071150, 71671135), the National Undergraduate Innovation and Entrepreneurship Training Program (No. 20191049714021), the 2019 Fundamental Research Funds for the Central Universities (WUT: 2019IB013), and the China Scholarship Council (CSC) scholarship (Grant No. 201906955002).

Author information

Author notes

  1. Congjun Rao and Yawen He contributed equally to this manuscript.

Authors and Affiliations

  1. School of Science, Wuhan University of Technology, Wuhan, 430070, People’s Republic of China

    Congjun Rao & Yawen He

  2. School of Economics, Wuhan University of Technology, Wuhan, 430070, People’s Republic of China

    Xiaolu Wang

Authors
  1. Congjun Rao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yawen He
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiaolu Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yawen He.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rao, C., He, Y. & Wang, X. Comprehensive Evaluation of Non-waste Cities Based on Two-Tuple Mixed Correlation Degree. Int. J. Fuzzy Syst. 23, 369–391 (2021). https://doi.org/10.1007/s40815-020-00975-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40815-020-00975-x

Keywords

Search

Navigation