Multi-attribute group decision-making with multi-granularity linguistic assessment information: An improved approach based on deviation and TOPSIS

Sen Liu, Tung Sun Chan, Wenxue Ran

Research output: Journal article publicationJournal articleAcademic researchpeer-review

52 Citations (Scopus)

Abstract

With respect to group decision-making problems with multi-granularity linguistic assessment information, a new approach is proposed. Firstly, the computational formulae are given in order to transform and unify the multi-granularity linguistic comparison matrices. Secondly, the method of standard and mean deviation is applied to determine the unknown attribute weights, and the weights of the decision makers will be determined by using the extended TOPSIS (technique for order preference by similarity to an ideal solution) method. Finally, based on the LWAA (linguistic weighted arithmetic averaging) operator, information on the preference provided by each decision maker is aggregated into the comprehensive evaluation value of each alternative, and the most desirable alternative is selected. The proposed approach expands the research in multi-attribute group decision-making with multi-granularity linguistic assessment information by both considering the weights of the attributes and decision makers, and objective weighting for them. A numerical example is given to illustrate the practicability and usefulness of the proposed approach.
Original languageEnglish
Pages (from-to)10129-10140
Number of pages12
JournalApplied Mathematical Modelling
Volume37
Issue number24
DOIs
Publication statusPublished - 15 Dec 2013

Keywords

  • Multi-attribute group decision-making
  • Multi-granularity
  • Standard and mean deviation
  • TOPSIS

ASJC Scopus subject areas

  • Modelling and Simulation
  • Applied Mathematics

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