Skip to main content
Log in

D-WASPAS: Addressing Social Cognition in Uncertain Decision-Making with an Application to a Sustainable Project Portfolio Problem

  • Published:
Cognitive Computation Aims and scope Submit manuscript

Abstract

Decision-making is an interdisciplinary area that has roots in mathematics, economics, and social science. Multiple-criteria group decision-making (MCGDM) is one of the most applicable areas of decision-making. Social cognition is involved in group decision-making. Therefore, it is necessary to address how decision makers (DMs) process and apply judgments and information during the process. In recent years, many approaches have been applied to MCGDM. As an important aspect of this process, uncertainty has led to the application of fuzzy sets. However, utilizing various decision-making approaches can result in different results and confusion among DMs. Moreover, using classic fuzzy sets and expressing degrees of belonging by crisp values has proven to be inadequate for uncertain decision-making environments. This paper presents a novel MCGDM approach, double-weighted aggregated sum product assessment (D-WASPAS), under interval-valued Pythagorean fuzzy (IVPF) uncertainty. The proposed approach applies knowledge measures to address the objective weights of criteria. Then, subjective and objective weights of criteria are aggregated to create a more appropriate weight. This approach considers three decision-making methods. In the first, an IVPF-ARAS (additive ratio assessment) method is extended to rank the alternatives. In the second, an IVPF-EDAS (evaluation based on distance from average solution) method is developed to rank the alternatives. In the third, a novel IVPF-COADAP (complex adequate appraisal) method is utilized for a third ranking. To aggregate the results, two steps are carried out using the WASPAS method. First, the results of the ranking approaches are aggregated. This process starts with computing the objective weights of the ranking approaches and aggregating the outcome with the subjective weights of the approaches. Then, the WASPAS method is applied to aggregate the obtained rankings and obtain a set of rankings for each DM. The second aggregation is utilized to aggregate the results for the DMs and reach a final set of rankings. Similarly, the subjective and objective weights of the DMs are applied in the WASPAS to aggregate the results. It should be noted that since the WASPAS method is utilized twice to aggregate the results, this approach is called D-WASPAS. A case study of the application of the proposed method shows that it is applicable to many multiple-criteria analysis and decision-making processes. Moreover, the results are more reliable because various decision-making methods are taken into consideration, and it is a last-aggregation process. Double-weighted aggregated sum product assessment offers a novel decision-making framework that is applicable in real-world decision-making situations. The proposed method is based on interval-valued Pythagorean fuzzy sets (IVPFSs), which would be especially applicable to uncertain situations. Also, it would enhance calculations of the process by offering more flexibility in dealing with uncertainty. Consequently, introducing this new decision-making framework and applying extended fuzzy sets would make the proposed method more widely applicable. The last-aggregation nature of this method avoids loss of cognitive information and assigning weights to the DMs, and the different ranking methods address the social cognition that leads to the judgments expressed and the final decisions.

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

Access this article

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
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Amaral TM, Costa AP. Improving decision-making and management of hospital resources: an application of the PROMETHEE II method in an emergency department. Operations Research for Health Care. 2014;3(1):1–6.

    Article  Google Scholar 

  2. Antucheviciene J, Tavana M, Nilashi M, Bausys R. Managing information uncertainty and complexity in decision-making. Complexity. 2017;2017:1–3.

    Article  Google Scholar 

  3. Atanassov, K. T. (1983). Intuitionistic fuzzy sets in: V. Sgurev, Ed., VII ITKR’s Session, Sofia, (Central Sci. and Techn. Library, Bulg. Academy of Sciences, 1984).

  4. Baykasoğlu A, Gölcük İ. Development of an interval type-2 fuzzy sets based hierarchical MADM model by combining DEMATEL and TOPSIS. Expert Syst Appl. 2017;70:37–51.

    Article  Google Scholar 

  5. Behzadian M, Otaghsara SK, Yazdani M, Ignatius J. A state-of the-art survey of TOPSIS applications. Expert Syst Appl. 2012;39(17):13051–69.

    Article  Google Scholar 

  6. Biswas A, Sarkar B. Pythagorean fuzzy multicriteria group decision making through similarity measure based on point operators. Int J Intell Syst. 2018;33(8):1731–44.

    Article  Google Scholar 

  7. Büyüközkan, G., & Göçer, F. (2017). An extension of ARAS methodology based on interval valued intuitionistic fuzzy group decision making for digital supply chain. In 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), (pp. 1-6).

  8. Büyüközkan G, Güleryüz S. Multi criteria group decision making approach for smart phone selection using intuitionistic fuzzy TOPSIS. Int J Comput Intell Syst. 2016;9(4):709–25.

    Article  Google Scholar 

  9. Ceballos B, Lamata MT, Pelta DA. Fuzzy multicriteria decision-making methods: a comparative analysis. Int J Intell Syst. 2017;32(7):722–38.

    Article  Google Scholar 

  10. Celik E, Gul M, Aydin N, Gumus AT, Guneri AF. A comprehensive review of multi criteria decision making approaches based on interval type-2 fuzzy sets. Knowl-Based Syst. 2015;85:329–41.

    Article  Google Scholar 

  11. Chakraborty S, Zavadskas EK. Applications of WASPAS method in manufacturing decision making. Informatica. 2014;25(1):1–20.

    Article  Google Scholar 

  12. Chen SM, Han WH. A new multiattribute decision making method based on multiplication operations of interval-valued intuitionistic fuzzy values and linear programming methodology. Inf Sci. 2018;429:421–32.

    Article  Google Scholar 

  13. Das S, Dutta B, Guha D. Weight computation of criteria in a decision-making problem by knowledge measure with intuitionistic fuzzy set and interval-valued intuitionistic fuzzy set. Soft Comput. 2016;20(9):3421–42.

    Article  Google Scholar 

  14. Davoudabadi R, Mousavi SM, Šaparauskas J, Gitinavard H. Solving construction project selection problem by a new uncertain weighting and ranking based on compromise solution with linear assignment approach. J Civ Eng Manag. 2019;25(3):241–51.

    Article  Google Scholar 

  15. Deng H. Comparing and ranking fuzzy numbers using ideal solutions. Appl Math Model. 2014;38(5):1638–46.

    Article  Google Scholar 

  16. Dorfeshan Y, Mousavi SM, Mohagheghi V, Vahdani B. Selecting project-critical path by a new interval type-2 fuzzy decision methodology based on MULTIMOORA, MOOSRA and TPOP methods. Comput Ind Eng. 2018;120:160–78.

    Article  Google Scholar 

  17. Farhadinia B, Xu Z. Distance and aggregation-based methodologies for hesitant fuzzy decision making. Cogn Comput. 2017;9(1):81–94.

    Article  Google Scholar 

  18. Foroozesh N, Tavakkoli-Moghaddam R, Mousavi SM. A novel group decision model based on mean–variance–skewness concepts and interval-valued fuzzy sets for a selection problem of the sustainable warehouse location under uncertainty. Neural Comput & Applic. 2018;30:3277–93.

    Article  Google Scholar 

  19. Foroozesh N, Tavakkoli-Moghaddam R, Mousavi SM. Sustainable-supplier selection for manufacturing services: a new failure mode and effects analysis model based on interval-valued fuzzy group decision-making. Int J Adv Manuf Technol. 2018;95(9–12):3609–29.

    Article  Google Scholar 

  20. Foroozesh N, Tavakkoli-Moghaddam R, Mousavi SM. An interval-valued fuzzy statistical group decision making approach with new evaluating indices for sustainable supplier selection problem. J Intell Fuzzy Syst. 2019;36:1855–66.

    Article  Google Scholar 

  21. Frith CD, Singer T. The role of social cognition in decision making. Phil Trans R Soc B: Biol Sci. 2008;363(1511):3875–86.

    Article  Google Scholar 

  22. Garg, H. (2018). Generalised Pythagorean fuzzy geometric interactive aggregation operators using Einstein operations and their application to decision making. J Exp Theor Artif Intell. Article in press. DOI: https://doi.org/10.1080/0952813X.2018.1467497, 30, 763, 794.

  23. Gitinavard H, Mousavi SM, Vahdani B. Soft computing based on hierarchical evaluation approach and criteria interdependencies for energy decision-making problems: a case study. Energy. 2017;118:556–77.

    Article  Google Scholar 

  24. Guo S, Zhao H. Fuzzy best-worst multi-criteria decision-making method and its applications. Knowl-Based Syst. 2017;121:23–31.

    Article  Google Scholar 

  25. Hajighasemi Z, Mousavi SM. A new approach in failure modes and effects analysis based on compromise solution by considering objective and subjective weights with interval-valued intuitionistic fuzzy sets. Iran J Fuzzy Syst. 2018;15(1):139–61.

    Google Scholar 

  26. Kahraman C, Oztaysi B, Onar SC. Photovoltaics type selection using an intuitionistic fuzzy projection model-based approach. J Multiple-Valued Logic Soft Comput. 2018;30:1–20.

    Google Scholar 

  27. Kannan D, de Sousa Jabbour ABL, Jabbour CJC. Selecting green suppliers based on GSCM practices: using fuzzy TOPSIS applied to a Brazilian electronics company. Eur J Oper Res. 2014;233(2):432–47.

    Article  Google Scholar 

  28. Keshavarz-Ghorabaee M, Zavadskas EK, Olfat L, Turskis Z. Multi-criteria inventory classification using a new method of evaluation based on distance from average solution (EDAS). Informatica. 2015;26(3):435–51.

    Article  Google Scholar 

  29. Keshavarz-Ghorabaee M, Zavadskas EK, Amiri M, Turskis Z. Extended EDAS method for fuzzy multi-criteria decision-making: an application to supplier selection. Int J Comput Commun Control. 2016;11(3):358–71.

    Article  Google Scholar 

  30. Lashgari S, Antuchevičienė J, Delavari A, Kheirkhah O. Using QSPM and WASPAS methods for determining outsourcing strategies. J Bus Econ Manag. 2014;15(4):729–43.

    Article  Google Scholar 

  31. Li X, Chen X. D-intuitionistic hesitant fuzzy sets and their application in multiple attribute decision making. Cogn Comput. 2018;10(3):496–505.

    Article  Google Scholar 

  32. Li J, Wang JQ. Multi-criteria outranking methods with hesitant probabilistic fuzzy sets. Cogn Comput. 2017;9(5):611–25.

    Article  Google Scholar 

  33. Li D, Zeng W. Distance measure of Pythagorean fuzzy sets. Int J Intell Syst. 2018;33(2):348–61.

    Article  Google Scholar 

  34. Liu P, Li H. Interval-valued intuitionistic fuzzy power Bonferroni aggregation operators and their application to group decision making. Cogn Comput. 2017;9(4):494–512.

    Article  Google Scholar 

  35. Liu P, Qin X. A new decision-making method based on interval-valued linguistic intuitionistic fuzzy information. Cogn Comput. 2019;11(1):125–44.

    Article  Google Scholar 

  36. MacCrimmon, K.R. (1968). Decision Makingamong Multipleattribute Alternatives: A Survey and Consolidated Approach; RAND Memorandum, RM-4823-ARPA; RAND Corporation: Santa Monica, CA, USA.

  37. Miller DW, Starr MK. Executive decisions and operations research. Englewood Cliffs: Prentice-Hall; 1969.

    Google Scholar 

  38. Mohagheghi, V., Mousavi, S. M., & Siadat, A. (2016a). Best product end-of-life scenario selection by a new decision-making process under Atanassov fuzzy uncertainty. In 2016 IEEE International Conference on Management of Innovation and Technology (ICMIT), (pp. 313-317).

  39. Mohagheghi V, Mousavi SM, Vahdani B. A new multi-objective optimization approach for sustainable project portfolio selection: a real world application under interval-valued fuzzy environment. Iran J Fuzzy Syst. 2016;13(6):41–68.

    Google Scholar 

  40. Mohagheghi V, Mousavi SM, Vahdani B. Enhancing decision-making flexibility by introducing a new last aggregation evaluating approach based on multi-criteria group decision making and Pythagorean fuzzy sets. Appl Soft Comput. 2017;61:527–35.

    Article  Google Scholar 

  41. Mohagheghi V, Mousavi SM, Vahdani B, Siadat A. A mathematical modeling approach for high and new technology-project portfolio selection under uncertain environments. J Intell Fuzzy Syst. 2017;32(6):4069–79.

    Article  Google Scholar 

  42. Mousavi SM. A new interval-valued hesitant fuzzy-pairwise comparison-compromise solution methodology: an application to cross-docking location planning. Neural Comput & Applic. 2019; 31(9): 5159–5173

  43. Opricovic S. Multicriteria optimization of civil engineering systems. Faculty Civil Eng Belgrade. 1998;2(1):5–21.

    Google Scholar 

  44. Oz, N. E., Mete, S., Serin, F., & Gul, M. (2018). Risk assessment for clearing and grading process of a natural gas pipeline project: an extended TOPSIS model with Pythagorean fuzzy sets for prioritizing hazards. Human and Ecological Risk Assessment: An International Journal, 1–18. Article in press, DOI: https://doi.org/10.1080/10807039.2018.1495057.

  45. Peng X, Selvachandran G (2017). Pythagorean fuzzy set: state of the art and future directions. Artif Intell Rev 1–55. Article in press. DOI: https://doi.org/10.1007/s10462-017-9596-9.

  46. Peng, H. G., & Wang, J. Q. (2018). Outranking decision-making method with Z-number cognitive information. Cognitive Computation, 1–17. Article in press, DOI: https://doi.org/10.1007/s12559-018-9556-y.

  47. Peng X, Yang Y. Some results for Pythagorean fuzzy sets. Int J Intell Syst. 2015;30(11):1133–60.

    Article  Google Scholar 

  48. Peng X, Yuan H, Yang Y. Pythagorean fuzzy information measures and their applications. Int J Intell Syst. 2017;32(10):991–1029.

    Article  Google Scholar 

  49. Qin J, Liu X, Pedrycz W. An extended TODIM multi-criteria group decision making method for green supplier selection in interval type-2 fuzzy environment. Eur J Oper Res. 2017;258(2):626–38.

    Article  Google Scholar 

  50. Suder A, Kahraman C. Multiattribute evaluation of organic and inorganic agricultural food investments using fuzzy TOPSIS. Technol Econ Dev Econ. 2018;24(3):844–58.

    Article  Google Scholar 

  51. Tang X, Wei G. Multiple attribute decision-making with dual hesitant Pythagorean fuzzy information. Cogn Comput. 2019;11(2):193–211.

    Article  Google Scholar 

  52. Tao, Z., Han, B., & Chen, H. (2018). On intuitionistic fuzzy copula aggregation operators in multiple-attribute decision making. Cogn Comput, 1–15. Article in Press. DOI: https://doi.org/10.1007/s12559-018-9545-1.

  53. Taylan O, Bafail AO, Abdulaal RM, Kabli MR. Construction projects selection and risk assessment by fuzzy AHP and fuzzy TOPSIS methodologies. Appl Soft Comput. 2014;17:105–16.

    Article  Google Scholar 

  54. Triantaphyllou E, Mann SH. An examination of the effectiveness of multi-dimensional decision-making methods: a decision-making paradox. Decis Support Syst. 1989;5(3):303–12.

    Article  Google Scholar 

  55. Turanoglu Bekar E, Cakmakci M, Kahraman C. Fuzzy COPRAS method for performance measurement in total productive maintenance: a comparative analysis. J Bus Econ Manag. 2016;17(5):663–84.

    Article  Google Scholar 

  56. Turskis Z, Zavadskas EK. A new fuzzy additive ratio assessment method (ARAS-F). Case study: the analysis of fuzzy multiple criteria in order to select the logistic centers location. Transport. 2010a;25(4):423–32.

    Article  Google Scholar 

  57. Turskis Z, Zavadskas EK. A novel method for multiple criteria analysis: grey additive ratio assessment (ARAS-G) method. Informatica. 2010b;21(4):597–610.

    Article  Google Scholar 

  58. Turskis Z, Zavadskas EK, Antucheviciene J, Kosareva N. A hybrid model based on fuzzy AHP and fuzzy WASPAS for construction site selection. Int J Comput Commun Control. 2015;10(6):113–28.

    Article  Google Scholar 

  59. Wei G. Pythagorean fuzzy interaction aggregation operators and their application to multiple attribute decision making. J Intell Fuzzy Syst. 2017;33(4):2119–32.

    Article  CAS  Google Scholar 

  60. Wei G, Wei Y. Similarity measures of Pythagorean fuzzy sets based on the cosine function and their applications. Int J Intell Syst. 2018;33(3):634–52.

    Article  Google Scholar 

  61. Wei CP, Wang P, Zhang YZ. Entropy, similarity measure of interval-valued intuitionistic fuzzy sets and their applications. Inf Sci. 2011;181(19):4273–86.

    Article  Google Scholar 

  62. Yager RR. Pythagorean membership grades in multicriteria decision making. IEEE Trans Fuzzy Syst. 2014;22(4):958–65.

    Article  Google Scholar 

  63. Yager RR, Abbasov AM. Pythagorean membership grades, complex numbers, and decision making. Int J Intell Syst. 2013;28(5):436–52.

    Article  Google Scholar 

  64. Ye J. Multiple attribute decision-making methods based on the expected value and the similarity measure of hesitant neutrosophic linguistic numbers. Cogn Comput. 2018;10(3):454–63.

    Article  Google Scholar 

  65. Zamani-Sabzi H, King JP, Gard CC, Abudu S. Statistical and analytical comparison of multi-criteria decision-making techniques under fuzzy environment. Oper Res Perspect. 2016;3:92–117.

    Article  Google Scholar 

  66. Zavadskas EK, Turskis Z, Vilutiene T. Multiple criteria analysis of foundation instalment alternatives by applying additive ratio assessment (ARAS) method. Arch Civil Mech Eng. 2010;10(3):123–41.

    Article  Google Scholar 

  67. Zavadskas EK, Antucheviciene J, Saparauskas J, Turskis Z. MCDM methods WASPAS and MULTIMOORA: verification of robustness of methods when assessing alternative solutions. Econom Comput Econom Cybernet Stud Res. 2013;47(2):5–20.

    Google Scholar 

  68. Zavadskas EK, Antucheviciene J, Hajiagha SHR, Hashemi SS. Extension of weighted aggregated sum product assessment with interval-valued intuitionistic fuzzy numbers (WASPAS-IVIF). Appl Soft Comput. 2014a;24:1013–21.

    Article  Google Scholar 

  69. Zavadskas EK, Turskis Z, Kildienė S. State of art surveys of overviews on MCDM/MADM methods. Technol Econ Dev Econ. 2014b;20(1):165–79.

    Article  Google Scholar 

  70. Zavadskas EK, Baušys R, Lazauskas M. Sustainable assessment of alternative sites for the construction of a waste incineration plant by applying WASPAS method with single-valued neutrosophic set. Sustainability. 2015a;7(12):15923–36.

    Article  CAS  Google Scholar 

  71. Zavadskas EK, Turskis Z, Antucheviciene J. Selecting a contractor by using a novel method for multiple attribute analysis: weighted aggregated sum product assessment with grey values (WASPAS-G). Stud Inf Control. 2015b;24(2):141–50.

    Google Scholar 

  72. Zhang X. Multicriteria Pythagorean fuzzy decision analysis: a hierarchical QUALIFLEX approach with the closeness index-based ranking methods. Inf Sci. 2016;330:104–24.

    Article  Google Scholar 

  73. Zopounidis, C., & Pardalos, P.M. (Eds.). (2010). Handbook of multicriteria analysis (Vol. 103). Springer Science & Business Media.

Download references

Acknowledgments

The authors would like to express their appreciation to the editor and anonymous reviewers for their valuable comments and recommendations on this article.

Author information

Authors and Affiliations

Authors

Contributions

The authors of this research confirm the change on authorship based on their contributions in the revised version.

Corresponding author

Correspondence to S. Meysam Mousavi.

Ethics declarations

Conflict of Interest

The authors declare that they have no conflict of interest.

Informed Consent

Informed consent was not required as no human or animals were involved.

Human and Animal Rights

This article does not contain any studies with human or animal subjects performed by any of the authors.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mohagheghi, V., Mousavi, S.M. D-WASPAS: Addressing Social Cognition in Uncertain Decision-Making with an Application to a Sustainable Project Portfolio Problem. Cogn Comput 12, 619–641 (2020). https://doi.org/10.1007/s12559-019-09679-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12559-019-09679-3

Keywords

Navigation