A Novel IVPLTS Decision Method Based on Regret Theory and Cobweb Area Model

Joint Authors

Li, Peng
Peng, Huanhuan

Source

Mathematical Problems in Engineering

Issue

Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-10, 10 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2020-12-09

Country of Publication

Egypt

No. of Pages

10

Main Subjects

Civil Engineering

Abstract EN

For the multiple criteria decision-making (MCDM) problem with interval-valued probabilistic linguistic information, we propose a novel method considering the regret theory and cobweb area model.

We first propose a new score function, which can be used to compare different interval-valued probabilistic linguistic term sets (IVPLTSs) and transform the IVPLTSs into crisp numbers.

Some properties of the score function are verified.

Then, we utilize the regret theory to obtain the perceived utilities of decision makers (DMs), which can reflect the DMs’ bounded rationality.

Furthermore, we use the cobweb area model to aggregate decision information.

Finally, a real case of evaluating nursing homes is used to illustrate the effectiveness and features of our method.

American Psychological Association (APA)

Li, Peng& Peng, Huanhuan. 2020. A Novel IVPLTS Decision Method Based on Regret Theory and Cobweb Area Model. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1196100

Modern Language Association (MLA)

Li, Peng& Peng, Huanhuan. A Novel IVPLTS Decision Method Based on Regret Theory and Cobweb Area Model. Mathematical Problems in Engineering No. 2020 (2020), pp.1-10.
https://search.emarefa.net/detail/BIM-1196100

American Medical Association (AMA)

Li, Peng& Peng, Huanhuan. A Novel IVPLTS Decision Method Based on Regret Theory and Cobweb Area Model. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1196100

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1196100