Smart watch evaluation with integrated hesitant fuzzy linguistic SAW-ARAS technique


MEASUREMENT, vol.153, 2020 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 153
  • Publication Date: 2020
  • Doi Number: 10.1016/j.measurement.2019.107353
  • Journal Name: MEASUREMENT
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Compendex, INSPEC
  • Keywords: Multi criteria decision making, Hesitant fuzzy linguistic term sets, Smart watch, SAW, ARAS, Group decision making, GROUP DECISION-MAKING, TERM SETS, SELECTION, TOPSIS, MANAGEMENT, EXTENSION, SYSTEM, MODEL, METHODOLOGY, FRAMEWORK
  • Galatasaray University Affiliated: Yes


Many organizations use wearable devices to increase their operational efficiency and strengthen their competitive advantage. In such decisions, managers can find it difficult to select the right device for their company. To address the Smart Watch (SW) selection problem, this article introduces an assessment framework established on a Hesitant Fuzzy Linguistic (HFL) Multi-Criteria Decision-Making technique to collectively consider parameters affecting the eventual decision. Hesitant Fuzzy Linguistic Term Sets (HFLTS) are utilized to integrate choices of decision makers into the decision-making procedure, where their thoughts and ideas about a decision problem can be of an uncertain nature, making it hard to express their assessments with crisp numbers. The proposed method handles the partiality in decision-making processes with a Group Decision Making (GDM) approach. This paper first showcases an integrated HFL Simple Additive Weighting (SAW)-HFL Additive Ratio ASsessment (ARAS) method. The framework's functionality is then illustrated in a case study about SW assessment. The originality of the paper is based on its evaluation framework using an integrated SAW-ARAS approach in the hesitant fuzzy environment, its research method and case application in the logistics sector. This approach can guide managers and practitioners for an effective SW selection process. (C) 2019 Elsevier Ltd. All rights reserved.