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Applied Intelligence

, Volume 49, Issue 10, pp 3587–3605 | Cite as

A normalized projection-based group decision-making method with heterogeneous decision information and application to software development effort assessment

  • Chuan YueEmail author
Article

Abstract

Group decision-making (GDM) is regarded as a main part of modern decision science. The aim of this paper is to develop a GDM method with heterogeneous decision information: real number, interval data and intuitionistic fuzzy number. The basic framework was based on the TOPSIS (technique for order preference by similarity to ideal solution) technique, in which the separations were based on a new normalized projection measure. There were no transformations and aggregations of different information representations in this model. The ranking of alternatives was based on original decision information. An experimental analysis was performed in order to illustrate the practicability, feasibility and validity of method introduced in this paper. In the end of this study, future research directions were suggested.

Keywords

Group decision-making Heterogeneous decision information Normalized projection TOPSIS technique Software development effort assessment 

Notes

Acknowledgment

The author is very grateful to the editors and reviewers for their constructive comments and suggestions that have led to an improved version of this paper. This work was supported in part by the Young Creative Talents Project from Department of Education of Guangdong Province (No. 2016KQNCX064) and Project of Enhancing School with Innovation of Guangdong Ocean University (No. GDOU2017052802).

Disclosure statement

No potential conflict of interest was reported by the author.

References

  1. 1.
    Asghari M, Nassiri P, Monazzam MR, Golbabaei F, Arabalibeik H, Shamsipour A, Allahverdy A (2017) Weighting criteria and prioritizing of heat stress indices in surface mining using a Delphi technique and fuzzy AHP-TOPSIS method. J Environ Health Sci Eng 15(1):1CrossRefGoogle Scholar
  2. 2.
    Atanassov KT (1986) Intuitionistic fuzzy sets. Fuzzy Sets Syst 20(1):87–96MathSciNetzbMATHCrossRefGoogle Scholar
  3. 3.
    Baudry G, Macharis C, Vallée T (2018) Range-based multi-actor multi-criteria analysis: A combined method of multi-actor multi-criteria analysis and monte carlo simulation to support participatory decision making under uncertainty. Eur J Oper Res 264(1):257–269MathSciNetzbMATHCrossRefGoogle Scholar
  4. 4.
    Chen X, Zhang HJ, Dong YC (2015) The fusion process with heterogeneous preference structures in group decision making: a survey. Inf Fus 24:72–83CrossRefGoogle Scholar
  5. 5.
    Chiclana F, Herrera F, Herrera-Viedma E (2001) Integrating multiplicative preference relations in a multipurpose decision-making model based on fuzzy preference relations. Fuzzy Sets Syst 122(2):277–291MathSciNetzbMATHCrossRefGoogle Scholar
  6. 6.
    Dey PP, Pramanik S, Giri BC (2016) Neutrosophic soft multi-attribute decision making based on grey relational projection method. Neutrosophic Sets Syst 11:98–106Google Scholar
  7. 7.
    Dong YC, Zhang HJ (2014) Multiperson decision making with different preference representation structures: A direct consensus framework and its properties. Knowl-Based Syst 58:45–57CrossRefGoogle Scholar
  8. 8.
    Fu C, Gao X, Liu M, Liu X, Han L, Jing C (2011) GRAP Grey Risk assessment based on projection in ad hoc networks. J Parallel Distrib Comput 71(9):1249–1260zbMATHCrossRefGoogle Scholar
  9. 9.
    Hryszko J, Madeyski L (2017) Assessment of the software defect prediction cost effectiveness in an industrial project. In: Software Engineering: Challenges and Solutions. Springer, pp 77–90Google Scholar
  10. 10.
    Pu J, Zhang H-y, Wang J-q (2018) A projection-based TODIM method under multi-valued neutrosophic environments and its application in personnel selection. Neural Comput Appl 29(1):221–234CrossRefGoogle Scholar
  11. 11.
    Ju Y, Wang A (2013) Projection method for multiple criteria group decision making with incomplete weight information in linguistic setting. Appl Math Model 37(20):9031–9040MathSciNetzbMATHCrossRefGoogle Scholar
  12. 12.
    Li DF, Wan SP (2014) Fuzzy heterogeneous multiattribute decision making method for outsourcing provider selection. Expert Syst Appl 41(6):3047–3059CrossRefGoogle Scholar
  13. 13.
    Lourenzutti R, Krohling RA (2016) A generalized TOPSIS method for group decision making with heterogeneous information in a dynamic environment. Inf Sci 330:1–18CrossRefGoogle Scholar
  14. 14.
    Ma J, Lu J, Zhang GQ (2010) Decider: A fuzzy multi-criteria group decision support system. Knowl-Based Syst 23(1):23–31CrossRefGoogle Scholar
  15. 15.
    Martínez L., Liu J, Ruan D, Yang JB (2007) Dealing with heterogeneous information in engineering evaluation processes. Inf Sci 177(7):1533–1542CrossRefGoogle Scholar
  16. 16.
    Moore RE, Kearfott RB, Cloud MJ (2009) Introduction to interval analysis. SIAM, PhiladelphiazbMATHCrossRefGoogle Scholar
  17. 17.
    Muriana C, Vizzini G (2017) Project risk management: A deterministic quantitative technique for assessment and mitigation. Int J Proj Manag 35(3):320–340CrossRefGoogle Scholar
  18. 18.
    Özen S, Onay H, Atik T, Solmaz A, ”Ozkınay F, Gökṡen D, Darcan Ṡ (2017) Rapid molecular genetic diagnosis with next-generation sequencing in 46, XY disorders of sex development cases: Efficiency and cost assessment. Horm Res Paediatr 87(2):81–87CrossRefGoogle Scholar
  19. 19.
    Pei Z, Liu J, Hao F, Bin Z (2019) FLM-TOPSIS The fuzzy linguistic multiset TOPSIS method and its application in linguistic decision making. Inf Fusion 45:266–281CrossRefGoogle Scholar
  20. 20.
    Peng C, Du H, Warren Liao T (2017) A research on the cutting database system based on machining features and TOPSIS. Robot Comput Integr Manuf 43:96–104CrossRefGoogle Scholar
  21. 21.
    Pramanik S, Roy R, Roy TK, Smarandache F (2018) Multi attribute decision making strategy on projection and bidirectional projection measures of interval rough neutrosophic sets. Infinite StudyGoogle Scholar
  22. 22.
    Ravasan AZ, Hanafizadeh P, Olfat L, Taghavifard MT (2017) A fuzzy TOPSIS method for selecting an e-banking outsourcing strategy. Int J Enterp Inf Syst (IJEIS) 13(2):34–49CrossRefGoogle Scholar
  23. 23.
    Sun G, Guan X, Yi X, Zhou Z (2018) An innovative TOPSIS approach based on hesitant fuzzy correlation coefficient and its applications. Appl Soft Comput 68:249–267CrossRefGoogle Scholar
  24. 24.
    Szmidt E, Kacprzyk J (2002) Using intuitionistic fuzzy sets in group decision making. Control Cybern 31 (4):1037–1054zbMATHGoogle Scholar
  25. 25.
    Tsao C-Y, Chen T-Y (2016) A projection-based compromising method for multiple criteria decision analysis with interval-valued intuitionistic fuzzy information. Appl Soft Comput 45:207–223CrossRefGoogle Scholar
  26. 26.
    Wan SP, Li DF (2014) Atanassov’s intuitionistic fuzzy programming method for heterogeneous multiattribute group decision making with Atanassov’s intuitionistic fuzzy truth degrees. IEEE Trans Fuzzy Syst 22(2):300–312MathSciNetCrossRefGoogle Scholar
  27. 27.
    Wang J-q, Li K-j, Zhang H-y (2012) Interval-valued intuitionistic fuzzy multi-criteria decision-making approach based on prospect score function. Knowl-Based Syst 27:119–125CrossRefGoogle Scholar
  28. 28.
    Le W, Zhang H-y, Wang J-q, Li Lin (2018) Picture fuzzy normalized projection-based VIKOR method for the risk evaluation of construction project. Appl Soft Comput 64:216–226Google Scholar
  29. 29.
    Wei G, Alsaadi FE, Hayat T, Alsaedi A (2018) Projection models for multiple attribute decision making with picture fuzzy information. Int J Mach Learn Cybern 9:713–719CrossRefGoogle Scholar
  30. 30.
    Wei G, Alsaadi FE, Hayat T, Alsaedi A (2018) Projection models for multiple attribute decision making with picture fuzzy information. Int J Mach Learn Cybern 9(4):713–719CrossRefGoogle Scholar
  31. 31.
    Wu B, Zong L, Yan X, Guedes Soares C (2018) Incorporating evidential reasoning and TOPSIS into group decision-making under uncertainty for handling ship without command. Ocean Eng 164:590–603CrossRefGoogle Scholar
  32. 32.
    Wu H, Xu Z, Ren P, Liao H (2018) Hesitant fuzzy linguistic projection model to multi-criteria decision making for hospital decision support systems. Comput Ind Eng 115:449–458CrossRefGoogle Scholar
  33. 33.
    Xu GL, Liu F (2013) An approach to group decision making based on interval multiplicative and fuzzy preference relations by using projection. Appl Math Model 37(6):3929–3943MathSciNetzbMATHCrossRefGoogle Scholar
  34. 34.
    Xu ZS (2008) Dependent uncertain ordered weighted aggregation operators. Inf Fusion 9(2):310–316CrossRefGoogle Scholar
  35. 35.
    Xu ZS (2009) A method based on the dynamic weighted geometric aggregation operator for dynamic hybrid multi-attribute group decision making. Int J Uncertainty Fuzziness Knowledge Based Syst 17(01):15–33zbMATHCrossRefGoogle Scholar
  36. 36.
    Xu ZS, Cai XQ (2010) Recent advances in intuitionistic fuzzy information aggregation. Fuzzy Optim Decis Making 9(4):359–381MathSciNetzbMATHCrossRefGoogle Scholar
  37. 37.
    Xu ZS, Hu H (2010) Projection models for intuitionistic fuzzy multiple attribute decision making. Int J Inf Technol Decis Making 9(2):267–280zbMATHCrossRefGoogle Scholar
  38. 38.
    Xu ZS, Yager RR (2006) Some geometric aggregation operators based on intuitionistic fuzzy sets. Int J Gen Syst 35(4):417–433MathSciNetzbMATHCrossRefGoogle Scholar
  39. 39.
    Yue C (2016) A geometric approach for ranking interval-valued intuitionistic fuzzy numbers with an application to group decision-making. Comput Ind Eng 102:233–245CrossRefGoogle Scholar
  40. 40.
    Yue C (2017) Entropy-based weights on decision makers in group decision-making setting with hybrid preference representations. Appl Soft Comput 60:737–749CrossRefGoogle Scholar
  41. 41.
    Yue C (2017) Two normalized projection models and application to group decision-making. J Intell Fuzzy Syst 32(6):4389–4402zbMATHCrossRefGoogle Scholar
  42. 42.
    Yue C (2018) An interval-valued intuitionistic fuzzy projection-based approach and application to evaluating knowledge transfer effectiveness. Neural Comput & Applic.  https://doi.org/10.1007/s00521-018-3571-5
  43. 43.
    Yue C (2018) Normalized projection approach to group decision-making with hybrid decision information. Int J Mach Learn Cybern 9(8):1365–1375CrossRefGoogle Scholar
  44. 44.
    Yue C (2018) A novel approach to interval comparison and application to software quality evaluation. J Exper Theor Artif Intell 30(5):583–602Google Scholar
  45. 45.
    Yue C (2018) A projection-based approach to software quality evaluation from the users’ perspectives. Int J Mach Learn Cybern.  https://doi.org/10.1007/s13042-018-0873-y
  46. 46.
    Yue C (2019) An intuitionistic fuzzy projection-based approach and application to software quality evaluation. Soft Computing.  https://doi.org/10.1007/s00500-019-03923-6
  47. 47.
    Yue Z (2012) Application of the projection method to determine weights of decision makers for group decision making. Sci Iran 19(3):872–878CrossRefGoogle Scholar
  48. 48.
    Yue ZL (2012) Approach to group decision making based on determining the weights of experts by using projection method. Appl Math Model 36(7):2900–2910MathSciNetzbMATHCrossRefGoogle Scholar
  49. 49.
    Yue ZL (2013) An intuitionistic fuzzy projection-based approach for partner selection. Appl Math Model 37 (23):9538–9551MathSciNetzbMATHCrossRefGoogle Scholar
  50. 50.
    Yue ZL, Jia YY (2015) A group decision making model with hybrid intuitionistic fuzzy information. Comput Ind Eng 87:202–212CrossRefGoogle Scholar
  51. 51.
    Yue ZL, Jia YY (2017) A direct projection-based group decision-making methodology with crisp values and interval data. Soft Comput 21(9):2395–2405zbMATHCrossRefGoogle Scholar
  52. 52.
    Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353zbMATHCrossRefGoogle Scholar
  53. 53.
    Zeng S, Baležentis T, Chen, J, Luo G (2013) A projection method for multiple attribute group decision making with intuitionistic fuzzy information. Informatica 24(3):485–503MathSciNetzbMATHGoogle Scholar
  54. 54.
    Zhang XL, Xu ZS, Wang H (2015) Heterogeneous multiple criteria group decision making with incomplete weight information: A deviation modeling approach. Inf Fusion 25:49–62CrossRefGoogle Scholar
  55. 55.
    Zhang Z, Guo CH (2014) An approach to group decision making with heterogeneous incomplete uncertain preference relations. Comput Ind Eng 71:27–36CrossRefGoogle Scholar
  56. 56.
    Zheng G, Jing Y, Huang H, Gao Y (2010) Application of improved grey relational projection method to evaluate sustainable building envelope performance. Appl Energy 87(2):710– 720CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.College of Mathematics and Computer ScienceGuangdong Ocean UniversityZhanjiangChina

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