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2021 | 16 | 122-139

Article title

The multicriteria group decision making flowsort method under uncertainty

Content

Title variants

Languages of publication

EN

Abstracts

EN
Crisp values are insufficient to model real-life situations and imprecise ideas are frequently represented in multicriteria decision aid analysis. In fact, it is difficult to treat the evaluation criteria precisely and to fix exact preferences rating. The triangular intuitionistic fuzzy numbers succeeded to treat this kind of ambiguity in a great deal of research than other forms of fuzzy representation functions. The field of sorting issues is an active research topic in the multiple criteria decision aid (MCDA). This study extended one of the sorting methods, FLOWSORT, for solving multiple criteria group decision-making problems. This extension described the preferences rating of alternatives as linguistic terms which can be easily expressed in triangular intuitionistic fuzzy numbers. To validate our extension, an illustrative example as well as an empirical comparison with other multi-criteria decision making methods is presented.

Year

Volume

16

Pages

122-139

Physical description

Contributors

  • Research Laboratory “Optimisation, Logistique et Informatique Décisionnelle” (OLID), University of Sfax, Higher Institute of Industrial Management of Sfax, Sfax, Tunisia
  • Research Laboratory “Optimisation, Logistique et Informatique Décisionnelle” (OLID), University of Sfax, Higher Institute of Industrial Management of Sfax, Sfax, Tunisia

References

  • Araz C., Ozkarahan I. (2007), Supplier Evaluation and Management System for Strategic Sourcing Based on a New Multicriteria Sorting Procedure, International Journal of Production Economics, 106, 585-606.
  • Assche D.V., De Smet Y. (2016), FlowSort Parameters Elicitation Based on Categorization Examples, Int. J. Multicriteria Decision Making, 6, 191-210.
  • Atanassov K.T. (1986), Intuitionistic Fuzzy Sets, Fuzzy Sets and Systems, 20, 87-96.
  • Banerjee S. (2012), Arithmetic Operations on Generalized Trapezoidal Fuzzy Number and Its Applications, Turkish Journal of Fuzzy Systems, 3, 16-44.
  • Brans J.P., Mareschal P., Vincke P. (1984), PROMETHEE: A New Family of Outranking Methods in Multicriteria Analysis, Operational Research, 3, 477-490.
  • Brans J.-P., Mareschal B. (2005), Promethee Methods [in:] J. Figueira, S. Greco, M. Ehrogott (eds.), Multiple Criteria Decision Analysis: State of the Art Surveys, Springer, 163-186.
  • Campos A.C., Mareschal B., Almeida A. (2015), Fuzzy FlowSort: An Integration of the FlowSort Method and Fuzzy Set Theory for Decision Making on the Basis of Inaccurate Quantitative Data, Information Sciences, 293, 115-123.
  • Chen S.J., Hwang C.L. (1992), Fuzzy Multiple Attribute Decision Making: Methods and Applications, Springer-Verlag, Berlin.
  • Chen T.Y. (2015), The Inclusion-based TOPSIS Method with Interval-valued Intuitionistic Fuzzy Sets for Multiple Criteria Group Decision Making, Applied Soft Computing, 26, 57-73.
  • Fernandez E., Navarro J. (2011), A New Approach to Multi-criteria Sorting Based on Fuzzy Outranking Relations: The THESEUS Method, European Journal of Operational Research, 213, 405-413.
  • Figueira J., de Smet Y., Brans J.P. (2004), MCDA Methods for Sorting and Clustering Problems: Promethee TRI and Promethee CLUSTER, Université Libre de Bruxelles, Service de Mathématiques de la Gestion Working Paper.
  • Gani A., Abbas S. (2014), A New Average Method for Solving Intuitionistic Fuzzy Transportation Problem, International Journal of Pure and Applied Mathematics, 93, 491-499.
  • Gautam S.S., Singh A., Singh S.R. (2016), TOPSIS for Multi Criteria Decision Making in Intuitionistic Fuzzy Environment, International Journal of Computer Applications, 156(8), 0975-8887.
  • Govindan K., Jepsen M.B. (2016), Supplier Risk Assessment Based on Trapezoidal Intuitionistic Fuzzy Numbers and ELECTRE TRI-C: A Case Illustration Involving Service Suppliers, Journal of the Operational Research Society, 67, 339-376.
  • Hwang C.L., Yoon K. (1981), Multiple Attributes Decision Making Methods and Applications, Springer, Berlin, Heidelberg.
  • Janssen P., Nemery P. (2012), An Extension of the FlowSort Sorting Method to Deal with Imprecision, Quarterly Journal of Operations Research, 11, 171-193.
  • Li D.F., Nan J.X., Zhang M.J. (2012), A Ranking Method of Triangular Intuitionistic Fuzzy Numbers and Application to Decision Making, International Journal of Computational Intelligence Systems, 3, 522-530.
  • Liu P., Qin X. (2017), Maclaurin Symmetric Mean Operators of Linguistic Intuitionistic Fuzzy Numbers and Their Application to Multiple-attribute Decision-Making, Journal of Experimental & Theoretical Artificial Intelligence, 29, 1173-1202.
  • Lolli F., Ishizaka A., Gamberini R., Rimini B., Messori M. (2015), FlowSort-GDSS – A Novel Group Multi-Criteria Decision Support System for Sorting Problems with Application to FMEA, Expert Systems with Applications, 42(17-18), 6342-6349.
  • Nemery P., Campos A.C.S.M., Mareschal B., de Almeida A.T. (2015), Addendum on: Fuzzy FlowSort: An Integration of the FlowSort Method and Fuzzy Set Theory for Decision Making on the Basis of Inaccurate Quantitative Data, Information Sciences, 315, 54-55.
  • Nemery P., Lamboray C. (2007), FlowSort: A Flow-based Sorting Method with Limiting or Central Profiles, TOP (J. Span. Soc. Stat. Oper. Res.), 16, 90-113.
  • Park J.H., Cho H.J., Kwun Y.C. (2011), Extension of the VIKOR Method for Group Decision Making with Interval-valued Intuitionistic Fuzzy Information, Fuzzy Optimization and Decision Making, 10, 233-253.
  • Pelissari R., Oliveira M.C., Amor S.B., Abackerli A.J. (2019), A New FlowSort-based Method to Deal with Information Imperfections in Sorting Decision-making Problems, European Journal of Operational Research, 276(1), 235-246.
  • Remadi F.D., Frikha H.M. (2019), The FlowSort for Multi Criteria Decision Making in Intuitionistic Fuzzy Environment, Control, Decision and Information Technologies (CoDIT), Paris.
  • Roy B. (1985), Méthodologie Multicritère d’aide à la décision, Economica, Paris.
  • Sengupta A., Pal T.K. (2009), On Comparing Interval Numbers: A Study on Existing Ideas, Stud. Fuzziness Soft Comput., 238, 25-37.
  • Shen F., Xu J., Xu Z. (2016), An Outranking Sorting Method for Multi-criteria Group Decision Making Using Intuitionistic Fuzzy Sets, Information Sciences, 338-353.
  • Wang J.Q., Han Z.Q., Zhang H. (2012), Multi-Criteria Group Decision-Making Method Based on Intuitionistic Interval Fuzzy Information, Group Decision and Negotiation, 23, 715-733.
  • Yu W. (1992), ELECTRE TRI. Aspects méthodologiques et guide d’utilisation, Document du LAMSADE, Université Paris-Dauphin.
  • Zadeh L.A. (1965), Fuzzy Sets, Information and Control, 8, 338-353.
  • Zhang X., Jin F., Liu P. (2013), A Grey Relational Projection Method for Multi-attribute Decision Making Based on Intuitionistic Trapezoidal Fuzzy Number, Applied Mathematical Modelling, 37, 3467-3477.
  • Zopounidis C., Doumpos M. (2002), Multicriteria Classification and Sorting Methods: A Literature Review, European Journal of Operational Research, 138, 229-246.

Document Type

Publication order reference

Identifiers

ISSN
2084-1531

YADDA identifier

bwmeta1.element.cejsh-f069249a-424a-4197-b7c8-a20aeb0ee737
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