4.7 Article

A continuous interval-valued linguistic ORESTE method for multi-criteria group decision making

Journal

KNOWLEDGE-BASED SYSTEMS
Volume 153, Issue -, Pages 65-77

Publisher

ELSEVIER SCIENCE BV
DOI: 10.1016/j.knosys.2018.04.022

Keywords

Multi-criteria group decision making; Continuous interval-valued linguistic term set; Extended Gaussian distribution-based weighting method; ORESTE; Mobike design

Funding

  1. National Natural Science Foundation of China [71771156, 71501135]
  2. China Postdoctoral Science Foundation [2016T90863, 2016M602698]
  3. Sientific Research Foundation for Excellent Young Scholars at Sichuan University [2016SCU04A23]
  4. International Visiting Program for Excellent Young Scholars of SCU

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Considering that the uncertain linguistic variable (or interval linguistic term) has some limitations in calculation, we extend it to the continuous interval-valued linguistic term set (CIVLTS), which is equivalent to the virtual term set but has its own semantics. It has the advantages of both the uncertain linguistic variable and the virtual term set but overcomes their defenses. It not only can interpret more complex assessments by continuous terms, but also is effective in aggregating the group opinions. We propose some methods to aggregate the individual decision matrices represented by CIVLTSs to the collective matrix. The extended Gaussian distribution-based weighting method is proposed to derive the weights for aggregating the large group opinions. Furthermore, the general ranking method ORESTE, is extended to the CIVL environment and is named as the CIVL-ORESTE method. The proposed method is excellent by no requirements of crisp criterion weights and the objective thresholds. A case study of selecting the optimal innovative sharing bike design for the Mobike sharing bikes is operated to show the practicability of the CIVL-ORESTE method. Finally, we compare the CIVL-ORESTE method with other ranking methods to illustrate the reliability of our method and its advantages. (C) 2018 Published by Elsevier B.V.

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