Selecting firms in University technoparks: A hesitant linguistic fuzzy TOPSIS model for heterogeneous contexts

Francisco J. Estrella, Sezi Cevik Onar, Rosa M. Rodríguez*, Basar Oztaysi, Luis Martínez, Cengiz Kahraman

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

37 Citations (Scopus)

Abstract

A technopark is an innovation center created to enhance the collaboration between the university and firms. Due to its benefits many firms would like to be in it, but only a few can be selected. Therefore, it is usually necessary a complex decision analysis process for its selection that implies multiple conflicting criteria assessed under uncertain circumstances because of the imprecision or hesitation shown by the experts involved that generally implies a heterogeneous decision context. To manage such a complexity, this paper proposes a fuzzy TOPSIS multi-criteria decision making method to cope with previous types of uncertainties by using fuzzy modeling and hesitant fuzzy linguistic term sets that will facilitate the experts elicitation of information in order to obtain accurate, reliable and robust results in the selection process. Eventually, this model will be implemented in a system within FLINTSTONES software for supporting the selection process and applied to a real case study of the Istanbul technical university technopark. A sensitivity analysis is also performed to check the robustness of the given decisions.

Original languageEnglish
Pages (from-to)1155-1172
Number of pages18
JournalJournal of Intelligent and Fuzzy Systems
Volume33
Issue number2
DOIs
Publication statusPublished - 2017

Bibliographical note

Publisher Copyright:
© 2017 - IOS Press and the authors. All rights reserved.

Keywords

  • decision support
  • Hesitant fuzzy linguistic term set
  • heterogeneous information
  • multiple criteria decision making
  • technopark

Fingerprint

Dive into the research topics of 'Selecting firms in University technoparks: A hesitant linguistic fuzzy TOPSIS model for heterogeneous contexts'. Together they form a unique fingerprint.

Cite this