Multi-attribute group decision-making under probabilistic uncertain linguistic environment

Mingwei Lin, Zeshui Xu, Yuling Zhai and Zhiqiang Yao

Journal of the Operational Research Society, 2018, vol. 69, issue 2, 157-170

Abstract: Existing decision-making methods cannot work under the probabilistic uncertain linguistic environment where the decision makers give different uncertain linguistic terms as their assessments and the weights of assessments are different. In this paper, a novel concept called probabilistic uncertain linguistic term set is proposed, which is composed of some possible uncertain linguistic terms associated with the corresponding probabilities. Then, the normalization process, comparison method, basic operations, and aggregation operators are studied for probabilistic uncertain linguistic term sets. After that, an extended technique for order preference by similarity to an ideal solution method and an aggregation-based method are developed to rank the alternatives and then select the best one for multi-attribute group decision-making with probabilistic uncertain linguistic information. Finally, a practical case concerning the selection of Cloud storage services is shown to illustrate the applicability of probabilistic uncertain linguistic term sets.

Date: 2018
References: Add references at CitEc
Citations: View citations in EconPapers (12)

Downloads: (external link)
http://hdl.handle.net/10.1057/s41274-017-0182-y (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tjorxx:v:69:y:2018:i:2:p:157-170

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tjor20

DOI: 10.1057/s41274-017-0182-y

Access Statistics for this article

Journal of the Operational Research Society is currently edited by Tom Archibald

More articles in Journal of the Operational Research Society from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().