Skip to main content
Log in

A novel interval type-2 fuzzy decision model based on two new versions of relative preference relation-based MABAC and WASPAS methods (with an application in aircraft maintenance planning)

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

In this paper, concerning advantages of relative preference relation (RPR) and interval type-2 fuzzy sets (IT2FSs), a new IT2F-RPR based on multi-attributive border approximation area comparison (MABAC) method is introduced for determining the critical path of production projects under group decision-making process. IT2FSs are more capable than classic fuzzy sets in coping with uncertainty and providing more degree of freedom to model the uncertain conditions. Also, the RPR is better than defuzzification because it does not lose the fuzzy message, and the method avoids pairwise comparisons. In fact, the MABAC method is extended by the RPR for reducing the time complexity. Furthermore, an extended MABAC method is developed under IT2FSs to better address the uncertainty. Weights of decision makers (DMs) are computed by introducing a new IT2F-RPR-based MABAC concept in the proposed new decision model. Also, experts’ opinions are aggregated by using weights of the DMs. Moreover, weights of criteria are determined based on a new extended weighted aggregated sum-product assessment (WASPAS) method by using the DMs or experts’ opinions on the importance of criteria and weights of DMs. As a matter of fact, weights of DMs are determined by a new version of extended MABAC method, and weights of criteria are specified using a new version of WASPAS method for the first time in the literature. Finally, a case study about aircraft maintenance planning is solved to address the calculation process and applicability of the proposed method. Computational results of the presented model are accurate and suitable for real-life situations.

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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Ak MF, Gul M (2018) AHP–TOPSIS integration extended with Pythagorean fuzzy sets for information security risk analysis. Complex Intell Syst. https://doi.org/10.1007/s40747-018-0087-71-14

    Article  Google Scholar 

  2. Amiri M, Antucheviciene J (2016) Evaluation by an area-based method of ranking interval type-2 fuzzy sets (EAMRIT-2F) for multi-criteria group decision-making. Transform Bus Econ 15(3):39

    Google Scholar 

  3. Amiri M, Golozari F (2011) Application of fuzzy multi-attribute decision making in determining the critical path by using time, cost, risk, and quality criteria. Int J Adv Manuf Technol 54(1):393–401

    Google Scholar 

  4. Atli O, Kahraman C (2012) Aircraft maintenance planning using fuzzy critical path analysis. Int J Comput Intell Syst 5(3):553–567

    Google Scholar 

  5. Balin A, Baraçli H (2017) A fuzzy multi-criteria decision making methodology based upon the interval type-2 fuzzy sets for evaluating renewable energy alternatives in Turkey. Technol Econ Dev Econ 23(5):742–763

    Google Scholar 

  6. Birjandi A, Mousavi SM (2019) Fuzzy resource-constrained project scheduling with multiple routes: a heuristic solution. Autom Constr 100:84–102

    Google Scholar 

  7. Büyüközkan G, Göçer F, Feyzioğlu O (2017) Cloud computing technology selection based on interval valued intuitionistic fuzzy COPRAS. In: Advances in fuzzy logic and technology 2017. Springer, Cham, pp 318–329

  8. Castillo O, Cervantes L (2014) Genetic design of optimal type-1 and type-2 fuzzy systems for longitudinal control of an airplane. Intell Autom Soft Comput 20(2):213–227

    Google Scholar 

  9. Castillo O, Melin P (2008) Intelligent systems with interval type-2 fuzzy logic. Int J Innov Comput Inf Control 4(4):771–783

    Google Scholar 

  10. Castillo O, Amador-Angulo L, Castro JR, Garcia-Valdez M (2016) A comparative study of type-1 fuzzy logic systems, interval type-2 fuzzy logic systems and generalized type-2 fuzzy logic systems in control problems. Inf Sci 354:257–274

    Google Scholar 

  11. Castillo O, Cervantes L, Soria J, Sanchez M, Castro JR (2016) A generalized type-2 fuzzy granular approach with applications to aerospace. Inf Sci 354:165–177

    Google Scholar 

  12. Cervantes L, Castillo O (2015) Type-2 fuzzy logic aggregation of multiple fuzzy controllers for airplane flight control. Inf Sci 324:247–256

    Google Scholar 

  13. Cevik Onar S, Oztaysi B, Kahraman C (2014) Strategic decision selection using hesitant fuzzy TOPSIS and interval type-2 fuzzy AHP: a case study. Int J Comput Intell Syst 7(5):1002–1021

    Google Scholar 

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

    Google Scholar 

  15. Chatterjee K, Kar S (2017) Unified Granular-number-based AHP-VIKOR multi-criteria decision framework. Granul Comput 2(3):199–221

    Google Scholar 

  16. Chen SM (1996) A fuzzy reasoning approach for rule-based systems based on fuzzy logics. IEEE Trans Syst Man Cybern Part B (Cybernetics) 26(5):769–778

    MathSciNet  Google Scholar 

  17. Chen SM, Chien CY (2011) Parallelized genetic ant colony systems for solving the traveling salesman problem. Expert Syst Appl 38(4):3873–3883

    MathSciNet  Google Scholar 

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

    Google Scholar 

  19. Chen SM, Huang CM (2003) Generating weighted fuzzy rules from relational database systems for estimating null values using genetic algorithms. IEEE Trans Fuzzy Syst 11(4):495–506

    Google Scholar 

  20. Chen SM, Kao PY (2013) TAIEX forecasting based on fuzzy time series, particle swarm optimization techniques and support vector machines. Inf Sci 247:62–71

    MathSciNet  Google Scholar 

  21. Chen SM, Lee LW (2010) Fuzzy multiple criteria hierarchical group decision-making based on interval type-2 fuzzy sets. IEEE Trans Syst Man Cybern Part A Syst Hum 40(5):1120–1128

    MathSciNet  Google Scholar 

  22. Chen SM, Lin TE, Lee LW (2014) Group decision making using incomplete fuzzy preference relations based on the additive consistency and the order consistency. Inf Sci 259:1–15

    MathSciNet  MATH  Google Scholar 

  23. Chen S-M, Chung N-Y (2006) Forecasting enrollments of students by using fuzzy time series and genetic algorithms. Int J Inf Manag Sci 17(3):1–17

    MATH  Google Scholar 

  24. Das S, Kar S, Pal T (2017) Robust decision making using intuitionistic fuzzy numbers. Granul Comput 2(1):41–54

    Google Scholar 

  25. Deveci M, Canıtez F, Gökaşar I (2018) WASPAS and TOPSIS based interval type-2 fuzzy MCDM method for a selection of a car sharing station. Sustain Cities Soc 41:777–791

    Google Scholar 

  26. Deveci M, Demirel NÇ, Ahmetoğlu E (2017) Airline new route selection based on interval type-2 fuzzy MCDM: a case study of new route between Turkey–North American region destinations. J Air Transp Manag 59:83–99

    Google Scholar 

  27. Dorfeshan Y, Mousavi SM, Vahdani B, Mohagheghi V (2017) Solving critical path problem in project network by a new enhanced MOOSRA approach with interval type-2 fuzzy sets. Int J Eng Trans C Asp 30(9):1352–1361

    Google Scholar 

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

    Google Scholar 

  29. Ghorabaee MK (2016) Developing an MCDM method for robot selection with interval type-2 fuzzy sets. Robot Comput Integr Manuf 37:221–232

    Google Scholar 

  30. Ghorabaee MK, Zavadskas EK, Amiri M, Esmaeili A (2016) Multi-criteria evaluation of green suppliers using an extended WASPAS method with interval type-2 fuzzy sets. J Clean Prod 137:213–229

    Google Scholar 

  31. Gitinavard H, Mousavi SM, Vahdani B (2017) Soft computing-based new interval-valued hesitant fuzzy multi-criteria group assessment method with last aggregation to industrial decision problems. Soft Comput 21:3247–3265

    MATH  Google Scholar 

  32. Gitinavard H, Mousavi SM, Vahdani B (2017) Soft computing based on hierarchical evaluation approach and criteria interdependencies for energy decision-making problems: a case study. Energy 118:556–577

    Google Scholar 

  33. Gitinavard H, Mousavi SM, Vahdani B, Siadat A (2016) A distance-based decision model in interval-valued hesitant fuzzy setting for industrial selection problems. Sci Iran E 23(4):1928–1940

    Google Scholar 

  34. Haghighi MH, Mousavi SM, Antucheviciene J, Mohagheghi V (2019) A new analytical methodology to handle time-cost trade-off problem with considering quality loss cost under interval-valued fuzzy uncertainty. Technol Econ Dev Econ 25(2):277–299

    Google Scholar 

  35. Haghighi MH, Mousavi SM, Mohagheghi V (2019) A new soft computing model based on linear assignment and linear programming technique for multidimensional analysis of preference with interval type-2 fuzzy sets. Appl Soft Comput 77:780–796

    Google Scholar 

  36. Horng YJ, Chen SM, Chang YC, Lee CH (2005) A new method for fuzzy information retrieval based on fuzzy hierarchical clustering and fuzzy inference techniques. IEEE Trans Fuzzy Syst 13(2):216–228

    Google Scholar 

  37. Jayagowri P, Geetharamani G (2015) Using metric distance ranking method to find intuitionistic fuzzy critical path. J Appl Math 2015:1–12

    MathSciNet  Google Scholar 

  38. Jiang Y, Xu Z, Shu Y (2017) Interval-valued intuitionistic multiplicative aggregation in group decision making. Granul Comput 2(4):387–407

    Google Scholar 

  39. Joshi BP (2018) Moderator intuitionistic fuzzy sets with applications in multi-criteria decision-making. Granul Comput 3(1):61–73

    Google Scholar 

  40. Kiliç M, Kaya İ (2015) Investment project evaluation by a decision making methodology based on type-2 fuzzy sets. Appl Soft Comput 27:399–410

    Google Scholar 

  41. Lee HS (2005) A fuzzy multi-criteria decision making model for the selection of the distribution center. In: Advances in natural computation, p 439

  42. Lee LW, Chen SM (2008) Fuzzy multiple attributes group decision-making based on the extension of TOPSIS method and interval type-2 fuzzy sets. In: 2008 IEEE international conference on machine learning and cybernetics, vol 6, pp 3260–3265

  43. Liu N, Meng S (2018) Approaches to the selection of cold chain logistics enterprises under hesitant fuzzy environment based on decision distance measures. Granul Comput 3(1):27–38

    Google Scholar 

  44. Liu P, You X (2017) Probabilistic linguistic TODIM approach for multiple attribute decision-making. Granul Comput 2(4):333–342

    MathSciNet  Google Scholar 

  45. Madhuri KU, Chandan K (2016) Applying the fuzzy critical path method to manufacturing tugboat. Int J Adv Res Ideas Innov Technol 2:1–3

    Google Scholar 

  46. Mehlawat MK, Gupta P (2016) A new fuzzy group multi-criteria decision making method with an application to the critical path selection. Int J Adv Manuf Technol 83(5–8):1281–1296

    Google Scholar 

  47. Mendel JM (2009) On answering the question “Where do I start in order to solve a new problem involving interval type-2 fuzzy sets?”. Inf Sci 179(19):3418–3431

    MathSciNet  MATH  Google Scholar 

  48. Mendel JM (2016) A comparison of three approaches for estimating (synthesizing) an interval type-2 fuzzy set model of a linguistic term for computing with words. Granul Comput 1(1):59–69

    Google Scholar 

  49. Mendel JM, John RI, Liu F (2006) Interval type-2 fuzzy logic systems made simple. IEEE Trans Fuzzy Syst 14(6):808–821

    Google Scholar 

  50. Meng S, Liu N, He Y (2017) GIFIHIA operator and its application to the selection of cold chain logistics enterprises. Granul Comput 2(3):187–197

    Google Scholar 

  51. Mohagheghi V, Mousavi SM, Vahdani B (2017) 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 61:527–535

    Google Scholar 

  52. Mohagheghi V, Mousavi SM, Vahdani B (2017) Analyzing project cash flow by a new interval type-2 fuzzy model with an application to construction industry. Neural Comput Appl 28:3393–3411

    Google Scholar 

  53. Mohagheghi V, Mousavi SM, Vahdani B, Shahriari MR (2017) R&D project evaluation and project portfolio selection by a new interval type-2 fuzzy optimization approach. Neural Comput Appl 28:3869–3888

    Google Scholar 

  54. Moradi N, Mousavi SM, Vahdani B (2017) An earned value model with risk analysis for project management under uncertain conditions. J Intell Fuzzy Syst 32:97–113

    MATH  Google Scholar 

  55. Moradi N, Mousavi SM, Vahdani B (2018) An interval type-2 fuzzy model for project-earned value analysis under uncertainty. J Multiple Valued Log Soft Comput 30(1):79–103

    MATH  Google Scholar 

  56. Ontiveros-Robles E, Melin P, Castillo O (2018) Comparative analysis of noise robustness of type 2 fuzzy logic controllers. Kybernetika 54(1):175–201

    MathSciNet  MATH  Google Scholar 

  57. Özkan B, Kaya İ, Cebeci U, Başlıgil H (2015) A hybrid multicriteria decision making methodology based on type-2 fuzzy sets for selection among energy storage alternatives. Int J Comput Intell Syst 8(5):914–927

    Google Scholar 

  58. Oztaysi B, Onar SC, Kahraman C (2017) Prioritization of business analytics projects using interval type-2 fuzzy AHP. In: Advances in fuzzy logic and technology 2017. Springer, Cham, pp 106–117

  59. Pamučar D, Ćirović G (2015) The selection of transport and handling resources in logistics centers using multi-attributive border approximation area comparison (MABAC). Expert Syst Appl 42(6):3016–3028

    Google Scholar 

  60. Pinar AJ, Anderson DT, Havens TC, Zare A, Adeyeba T (2017) Measures of the Shapley index for learning lower complexity fuzzy integrals. Granul Comput 2(4):303–319

    Google Scholar 

  61. Qin J (2017) Interval type-2 fuzzy Hamy mean operators and their application in multiple criteria decision making. Granul Comput 2(4):249–269

    Google Scholar 

  62. Roghanian E, Rahimi J, Ansari A (2010) Comparison of first aggregation and last aggregation in fuzzy group TOPSIS. Appl Math Model 34(12):3754–3766

    MathSciNet  MATH  Google Scholar 

  63. RoyJ, Ranjan A, Debnath A (2016) An extended multi attributive border approximation area comparison using interval type-2 trapezoidal fuzzy numbers. arXiv preprint arXiv:1607.01254

  64. San Cristobal JR (2012) Critical path definition using multicriteria decision making: PROMETHEE method. J Manag Eng 29(2):158–163

    Google Scholar 

  65. Sanchez MA, Castillo O, Castro JR (2015) Generalized type-2 fuzzy systems for controlling a mobile robot and a performance comparison with interval type-2 and type-1 fuzzy systems. Expert Syst Appl 42(14):5904–5914

    Google Scholar 

  66. Sanchez MA, Castillo O, Castro JR (2015) Information granule formation via the concept of uncertainty-based information with interval type-2 fuzzy sets representation and Takagi–Sugeno–Kang consequents optimized with Cuckoo search. Appl Soft Comput 27:602–609

    Google Scholar 

  67. Tsai PW, Pan JS, Chen SM, Liao BY (2012) Enhanced parallel cat swarm optimization based on the Taguchi method. Expert Syst Appl 39(7):6309–6319

    Google Scholar 

  68. Tsai PW, Pan JS, Chen SM, Liao BY, Hao SP (2008) Parallel cat swarm optimization. In 2008 IEEE international conference on Machine learning and cybernetics, vol 6, pp 3328–3333

  69. Wang C, Fu X, Meng S, He Y (2017) Multi-attribute decision-making based on the SPIFGIA operators. Granul Comput 2(4):321–331

    Google Scholar 

  70. Wang YJ (2014) A criteria weighting approach by combining fuzzy quality function deployment with relative preference relation. Appl Soft Comput 14:419–430

    Google Scholar 

  71. Wang YJ (2015) Ranking triangle and trapezoidal fuzzy numbers based on the relative preference relation. Appl Math Model 39(2):586–599

    MathSciNet  MATH  Google Scholar 

  72. Xu Z, Gou X (2017) An overview of interval-valued intuitionistic fuzzy information aggregations and applications. Granul Comput 2(1):13–39

    Google Scholar 

  73. Xu Z, Wang H (2016) Managing multi-granularity linguistic information in qualitative group decision making: an overview. Granul Comput 1(1):21–35

    Google Scholar 

  74. Xue YX, You JX, Lai XD, Liu HC (2016) An interval-valued intuitionistic fuzzy MABAC approach for material selection with incomplete weight information. Appl Soft Comput 38:703–713

    Google Scholar 

  75. Yang Q, Du PA, Wang Y, Liang B (2018) Developing a rough set based approach for group decision making based on determining weights of decision makers with interval numbers. Oper Res 18(3):757–779

    Google Scholar 

  76. Yeh TM, Pai FY, Liao CW (2014) Using a hybrid MCDM methodology to identify critical factors in new product development. Neural Comput Appl 24(3–4):957–971

    Google Scholar 

  77. Yu SM, Wang J, Wang JQ (2017) An interval type-2 fuzzy likelihood-based MABAC approach and its application in selecting hotels on a tourism website. Int J Fuzzy Syst 19(1):47–61

    MathSciNet  Google Scholar 

  78. Yue Z (2012) Extension of TOPSIS to determine weight of decision maker for group decision making problems with uncertain information. Expert Syst Appl 39(7):6343–6350

    Google Scholar 

  79. Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353

    MATH  Google Scholar 

  80. Zadeh LA (1975) The concept of a linguistic variable and its application to approximate reasoning-I. Inf Sci 8(3):199–249

    MathSciNet  MATH  Google Scholar 

  81. Zammori FA, Braglia M, Frosolini M (2009) A fuzzy multi-criteria approach for critical path definition. Int J Project Manag 27(3):278–291

    Google Scholar 

  82. Zavadskas EK, Turskis Z, Antucheviciene J, Zakarevicius A (2012) Optimization of weighted aggregated sum product assessment. Elektronika ir elektrotechnika 122(6):3–6

    Google Scholar 

  83. Zhou L, Wang Y, Jiang Y (2018) Investment project assessment by a MAGDM method based on the ranking of interval type-2 fuzzy sets. J Intell Fuzzy Syst 35(2):1875–1888

    Google Scholar 

  84. Zimmermann HJ (1991) Fuzzy set theory and its applications, 2nd edn. Kluwer, Boston

    MATH  Google Scholar 

Download references

Acknowledgements

The authors would like to thank anonymous reviewers for valuable comments and suggestions on this study.

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Y. Dorfeshan or S. Meysam Mousavi.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

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

Dorfeshan, Y., Mousavi, S.M. A novel interval type-2 fuzzy decision model based on two new versions of relative preference relation-based MABAC and WASPAS methods (with an application in aircraft maintenance planning). Neural Comput & Applic 32, 3367–3385 (2020). https://doi.org/10.1007/s00521-019-04184-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-019-04184-y

Keywords

Navigation