A life insurance policy selection via hesitant fuzzy linguistic decision making model

ADEM A. , Dagdeviren M.

12th International Conference on Application of Fuzzy Systems and Soft Computing (ICAFS), Vienna, Avusturya, 29 - 30 Ağustos 2016, cilt.102, ss.398-405 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası: 102
  • Doi Numarası: 10.1016/j.procs.2016.09.418
  • Basıldığı Şehir: Vienna
  • Basıldığı Ülke: Avusturya
  • Sayfa Sayıları: ss.398-405


The selection of life insurance policy is generally one of the most important and complicate issue in real life. Because there are a lot of alternatives and criteria related to this subject. As in all types of selection problems, to solve of this, any of multi criteria decision making methods (MCDM) can be used. However, since decision maker couldn't decide superiorities of alternatives and criteria, using classical multi criteria decision making methods to solve of this problem, gained results may not be accurate. Because, expert or decision maker may hesitate between different linguistic term and they need richer expression to express their knowledge. Hesitant fuzzy linguistic model which is novel MCDM methods, come into prominence in this way. Hesitant Fuzzy Linguistic Term Set permit decision maker to express their knowledge more correctly. In this paper we propose a hierarchical hesitant fuzzy linguistic model that contains hesitant linguistic evaluations of multiple experts on multiple criteria for life insurance policy alternatives. In this study, choosing one of the three life insurance policy alternatives has been studied. The main criteria of this problem are company reliability, customer relationship, the scope of insurance, insurance price, easiness of give up insurance. Three alternatives were evaluated based on these main criteria and their sub-criteria used HFLTS by two decision makers. As a result of the study the most suitable alternative has been selected on the basis of preferences of decision-makers. (C) 2016 The Authors. Published by Elsevier B.V.