A new distance-based decision model in interval-valued hesitant fuzzy setting for industrial selection problems

Authors

1 School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran

2 Department of Industrial Engineering, Faculty of Engineering, Shahed University, Tehran, Iran

3 Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran

4 Laboratoire de Conception, Fabrication Commande, Arts et Métier Paris Tech, Centre de Metz, Metz, France

Abstract

In classical group decision-making (GDM) analysis, ratings of potential alternatives and weights of criteria or decision makers (DMs) are known precisely. However, DMs for dealing with uncertain situations can define their opinions in linguistic variables based on fuzzy sets in industrial selection problems. In this respect, an interval-valued hesitant fuzzy set (IVHFS) is the suitable and capable theory that could help the DMs by assigning some interval-valued membership degrees for a candidate or option under a set. This paper introduces a novel interval-valued hesitant fuzzy distance-based group decision (IVHF-DBGD) model by a group of DMs, in which the best potential alternative can be appraised and selected among the conflicting criteria. In the proposed IVHF-DBGD model, the weight of each criterion is determined by extended IVHF-entropy method along with the DMs’ opinions about the criteria’ weights. Also, the weight of each DM is computed by a new IVHF-order preference method with the relative closeness. Moreover, this paper introduces a new IVHF-collective index to discriminate among potential alternatives in the selection process. Finally, the computational results with a robot selection from the literature indicate that the proposed IVHF-DBGD model is the suitable group decision-making tool for industrial selection problems.

Keywords

Scientia Iranica
Volume 23, Issue 4 - Serial Number 4
Transactions on Industrial Engineering (E)
August 2016
Pages 1928-1940

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History

  • Receive Date: 27 January 2016
  • Revise Date: 26 December 2024
  • Accept Date: 27 July 2017

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