A new hesitant fuzzy KEMIRA approach: An application to adoption of autonomous vehicles


Çevik Onar S., Kahraman C., Öztayşi B.

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, vol.42, no.1, pp.109-120, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 42 Issue: 1
  • Publication Date: 2022
  • Doi Number: 10.3233/jifs-219179
  • Journal Name: JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, zbMATH, Civil Engineering Abstracts
  • Page Numbers: pp.109-120
  • Keywords: Autonomous vehicle adoption, KEMIRA, hesitant fuzzy sets, HFLTS, DECISION-MAKING, SET, QFD
  • Istanbul Technical University Affiliated: Yes

Abstract

Autonomous vehicles are one of the emergent advances of the new technology era that has the prospective to redesign transportation structures. Understanding and measuring the limitations of adopting autonomous vehicles and selecting the best autonomous vehicle based on different aspects is crucial for enhancing the adoption process. Defining the criteria and the appropriate evaluation methodology is very important for selecting the best autonomous vehicles. However, this selection process is a human judgment-based process where both benefit and cost criteria with imprecise linguistic assessments should be considered. The KEmeny Median Indicator Ranks Accordance (KEMIRA) method is a method that enables ranking the benefit and cost criteria independently. In this paper, a new KEMIRA method based on hesitant fuzzy linguistic term sets is defined. Hesitant Fuzzy Linguistic Term Sets (HFLTS) are newly utilized to represent the hesitancy of the decision-makers. The proposed new KEMIRA is approach the first study that defines the alternative scores and weights of the criteria via HFLTS. The computational steps of the new model are applied to autonomous vehicle selection. A real application is employed to show the applicability of the new KEMIRA method.