Skip to main content
SpringerLink
Log in

Sorting of decision-making methods based on their outcomes using dominance-vector hesitant fuzzy-based distance

  • Foundations
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Multi-criteria decision-making (MCDM) techniques have attracted more and more scholars attention for their potential of application in many areas of human action. Although several contributions exist regarding comparative analysis of MCDM techniques, most of them are focused on demonstrating the similarities and differences of these methodologies in obtaining group decisions. However, the existing techniques comparing MCDM methods to investigate the most suitable ranking method for the case study have a critical shortcoming that limits their application to just MCDM methods resulting in total ranking order. This work contributes to reduce this shortcoming by establishing a correspondence between a non-total ranking order and a set of total ranking orders what we will call dominance-vector hesitant fuzzy set.

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

Similar content being viewed by others

References

  • Atanassov KT (1986) Intuitionistic fuzzy sets. Fuzzy Sets Syst 20:87–96

    Article  MATH  Google Scholar 

  • Azgomi MAA (2016) Trust modeling based on a combination of fuzzy analytic hierarchy process and fuzzy VIKOR. Soft Comput 20:399–421

    Article  Google Scholar 

  • Bachmaier C, Brandenburg FJ, Gleibner A, Hofmeier A (2014) On the hardness of maximum rank aggregation problems. J Discrete Algorithms. https://doi.org/10.1016/j.jda.2014.10.002

  • Bedregal B, Reiser R, Bustince H, Lopez-Molina C, Torra V (2014) Aggregating functions for typical hesitant fuzzy elements and the action of automorphisms. Inf Sci 256:82–97

    Article  MATH  Google Scholar 

  • Brans JP, Vincke P (1985) A preference ranking organization method: the PROMETHEE method for MCDM. Manag Sci 31:647–656

    Article  MATH  Google Scholar 

  • Brauers WKM, Zavadskas EK (2006) The MOORA method and its application to privatization in a transition economy. Control Cybern 35:445–469

    MathSciNet  MATH  Google Scholar 

  • Brauers WKM, Zavadskas EK (2010) Project management by MULTIMOORA as an instrument for transition economies. Technol Econ Dev Econ 16:5–24

    Article  Google Scholar 

  • Cabrerizo FJ, Herrera-Viedma E, Pedrycz W (2013) A method based on PSO and granular computing of linguistic information to solve group decision making problems defined in heterogeneous contexts. Eur J Oper Res 230:624–633

    Article  MathSciNet  MATH  Google Scholar 

  • Cabrerizo FJ, Chiclana F, Al-Hmouz R, Morfeq A, Balamash AS, Herrera-Viedma E (2015) Fuzzy decision making and consensus: challenges. J Intell Fuzzy Syst 29:1109–1118

    Article  MathSciNet  MATH  Google Scholar 

  • Cabrerizo FJ, Al-Hmouz R, Morfeq A, Balamash AS, Martinez MA, Herrera-Viedma E (2017) Soft consensus measures in group decision making using unbalanced fuzzy linguistic information. Soft Comput 21:3037–3050

    Article  MATH  Google Scholar 

  • Chakraborty S (2011) Applications of the MOORA method for decision making in manufacturing environment. Int J Adv Manuf Technol 54:1155–1166

    Article  Google Scholar 

  • Chatterjee P, Athawale VM, Chakraborty S (2009) Selection of materials using compromise ranking and outranking methods. Mater Des 30:4043–4053

    Article  Google Scholar 

  • Chitsaz N, Banihabib ME (2015) Comparison of different multi-criteria decision-making models in prioritizing flood management alternatives. Water Resour Manag. https://doi.org/10.1007/s11269-015-0954-6

  • Deng JL (1989) Introduction to grey system. J Grey Syst 1:1–24

    MathSciNet  MATH  Google Scholar 

  • Edwards W, Barron FH (1994) SMARTS and SMARTER: improved simple methods for multiattribute utility measurement. Organ Behav Hum Decis Process 60:306–25

    Article  Google Scholar 

  • Farhadinia B (2013) Information measures for hesitant fuzzy sets and interval-valued hesitant fuzzy sets. Inf Sci 240:129–144

    Article  MathSciNet  MATH  Google Scholar 

  • Farhadinia B (2014a) Distance and similarity measures for higher order hesitant fuzzy sets. Knowl Based Syst 55:43–48

    Article  MATH  Google Scholar 

  • Farhadinia B (2014b) A series of score functions for hesitant fuzzy sets. Inf Sci 277:102–110

    Article  MathSciNet  MATH  Google Scholar 

  • Farhadinia B (2014c) Fuzzy multicriteria decision-making method based on a family of novel measured functions under vague environment. J Intell Fuzzy Syst 27:2797–2808

    MathSciNet  MATH  Google Scholar 

  • Farhadinia B (2014d) Multicriteria decision making method based on the higher order hesitant fuzzy soft set. Int Sch Res Not Article ID 873454

  • Ho W (2008) Integrated analytic hierarchy process and its applications—a literature review. Eur J Oper Res 186:211–28

    Article  MathSciNet  MATH  Google Scholar 

  • Hwang CL, Yoon K (1981) Multiple attribute decision making: methods and applications. Springer, New York

    Book  MATH  Google Scholar 

  • Jahan A, Ismail MY, Sapuan SM, Mustapha F (2010) Material screening and choosing methods—a review. Mater Des 31:696–705

    Article  Google Scholar 

  • Jahan A, Ismail MY, Shuib S, Norfazidah D, Edwards KL (2011) An aggregation technique for optimal decision-making in materials selection. Mater Des 32:4918–4924

    Article  Google Scholar 

  • Kahraman C, Onar SC, Oztaysi B (2015) Fuzzy multicriteria decision-making: a literature review. Int J Comput Intell Syst 8:637–666

    Article  MATH  Google Scholar 

  • Kumar R, Vassilvitskii S (2010) Generalized distance between rankings. In: Proceedings of the 19th international conference on World Wide Web, pp 571–580

  • Liu W, Dong Y, Chiclana F, Cabrerizo FJ, Herrera-Viedma E (2017) Group decision-making based on heterogeneous preference relations with self-confidence. Fuzzy Optim Decis Mak 16:429–447

    Article  MathSciNet  MATH  Google Scholar 

  • MacCrimon KR (1968) Decision marking among multiple-attribute alternatives: a survey and consolidated approach. In: RAND memorandum, RM-4823- ARPA. The Rand Corporation, Santa Monica, CA

  • Mardani A, Jusoh A, Zavadskas EK (1994) Fuzzy multiple criteria decision-making techniques and applications—two decades review from 1994 to 2014. Expert Syst Appl 42(2015):412–4148

    Google Scholar 

  • May KO (1952) A set of independent necessary and sufficient conditions for simple majority decision. Econometrica 20:680–684

    Article  MathSciNet  MATH  Google Scholar 

  • Mizumoto M, Tanaka K (1976) Some properties of fuzzy sets of type-2. Inform Control 31:312–340

    Article  MathSciNet  MATH  Google Scholar 

  • Opricovic S (1998) Visekriterijumska optimizacija u gradevinarstvu—multi-criteria optimization of civil engineering systems. Faculty of Civil Engineering, Belgrade (in Serbian)

  • Peng DH, Gao ChY, Gao ZhF (2013) Generalized hesitant fuzzy synergetic weighted distance measures and their application to multiple criteria decision making. Appl Math Model 37:5837–5850

    Article  MathSciNet  MATH  Google Scholar 

  • Perez IJ, Cabrerizo FJ, Herrera-Viedma E (2010) A mobile decision support system for dynamic group decision making problems. IEEE Trans Syst Man Cybern A Syst Hum 40:1244–1256

    Article  Google Scholar 

  • Perez LG, Mata F, Chiclana F, Kou G, Herrera-Viedma E (2016) Modelling influence in group decision making. Soft Comput 20:1653–1665

    Article  Google Scholar 

  • Pomerol JC, Barba-Romero S (2000) Multicriterion decision in management: principles and practice. Springer, Netherlands

    Book  MATH  Google Scholar 

  • Rodriguez RM, Martinez L, Torra V, Xu ZS, Herrera F (2014) Hesitant fuzzy sets: state of the art and future directions. Int J Intell Syst 29:495–524

    Article  Google Scholar 

  • Rodriguez RM, Bedregal B, Bustince H, Dong YC, Farhadinia B, Kahraman C, Martinez L, Torra V, Xu YJ, Xu ZS, Herrera F (2016) A position and perspective analysis of hesitant fuzzy sets on information fusion in decision making. Towards High Qual Prog Inf Fus 29:89–97

    Google Scholar 

  • Roy B (1991) The outranking approach and the foundation of ELECTRE methods. Theory Decis 31:49–73

    Article  MathSciNet  Google Scholar 

  • Saaty TL (1980) Analytic hierarchy process: planning, priority setting, resource allocation. McGraw-Hill, New York

    MATH  Google Scholar 

  • Saaty TL, Ozdemir MS, Shang JS (2015) The rationality of punishment—measuring the severity of crimes: an AHP-based orders-of-magnitude approach. Int J Inf Technol Decis Mak 14:5–16

    Article  Google Scholar 

  • Stanujkic D, Magdalinovic N, Jovanovic R, Stojanovic S (2012) An objective multi-criteria approach to optimization using MOORA method and interval grey numbers. Technol Econ Dev Econ 18:331–363

    Article  MATH  Google Scholar 

  • Stanujkic D, Djordjevic B, Djordjevic M (2013) Comparative analysis of some prominent MCDM methods: a case of ranking Serbian banks. Serb J Manag 8:213–241

    Article  Google Scholar 

  • Torra V (2010) Hesitant fuzzy sets. Int J Intell Syst 25:529–539

    MATH  Google Scholar 

  • Torra V, Narukawa Y (2009) On hesitant fuzzy sets and decision. In: The 18th IEEE international conference on fuzzy systems, Jeju Island, Korea, pp 1378–1382

  • Wade TE (1995) Optimum dielectric selection using a weighted evaluation factor. Semicond Int 18

  • Xia MM, Xu ZS (2011) Hesitant fuzzy information aggregation in decision making. Int J Approx Reason 52:395–407

    Article  MathSciNet  MATH  Google Scholar 

  • Zavadskas EK, Turskis Z (2010) A new additive ratio assessment (ARAS) method in multicriteria decision-making. Technol Econ Dev Econ 16:159–172

    Article  Google Scholar 

  • Zavadskas EK, Kaklauskas A, Sarka V (1994) The new method of multicriteria complex proportional assessment of projects. Technol Econ Dev Econ 1:131–139

    Google Scholar 

  • Zavadskas EK, Turskis Z, Antucheviciene J, Zakarevicius A (2012) Optimization of weighted aggregated sum product assessment. Elektronika Ir Elektrotechnika Electron Electr Eng 122:3–6

    Google Scholar 

  • Zavadskas EK, Turskis Z, Kildiene S (2014) State of art surveys of overviews on MCDM/MADM methods. Technol Econ Dev Econ 20:165–179

    Article  Google Scholar 

  • Zeleny M (1973) Compromise programming. In: Cochrane J, Zeleny LM (eds) Multiple criteria decision making. University of South Carolina Press, Columbia, pp 262–301

    Google Scholar 

Download references

Acknowledgements

The authors respectfully acknowledge the support of Quchan University of Technology under Grant 94/7627 and FEDER funds under Grant TIN2016-75850-R.

Author information

Authors and Affiliations

  1. Department of Mathematics, Quchan University of Technology, Quchan, Iran

    Bahram Farhadinia

  2. Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain

    Enrique Herrera-Viedma

  3. Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, 21589, Saudi Arabia

    Enrique Herrera-Viedma

Authors
  1. Bahram Farhadinia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Enrique Herrera-Viedma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Bahram Farhadinia or Enrique Herrera-Viedma.

Ethics declarations

Conflict of interest

Authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by the authors.

Additional information

Communicated by A. Di Nola.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Farhadinia, B., Herrera-Viedma, E. Sorting of decision-making methods based on their outcomes using dominance-vector hesitant fuzzy-based distance. Soft Comput 23, 1109–1121 (2019). https://doi.org/10.1007/s00500-018-3143-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-018-3143-8

Keywords

Search

Navigation