Strategic Weight Manipulation in Multiple Attribute Decision Making in an Incomplete Information Context

Date

2017-08-24

Advisors

Journal Title

Journal ISSN

ISSN

Volume Title

Publisher

IEEE Xplore

Type

Conference

Peer reviewed

Yes

Abstract

In some real-world multiple attribute decision making (MADM) problems, a decision maker can strategically set attribute weights to obtain her/his desired ranking of alternatives, which is called the strategic weight manipulation of the MADM. Sometimes, the attribute weights are given with imprecise or partial information, which is called incomplete information of attribute weights. In this study, we propose the strategic weight manipulation under incomplete information on attributes weights. Then, a series of mixed 0-1 linear programming models (MLPMs) are proposed to derive a strategic weight vector for a desired ranking of an alternative. Finally, a numerical example is used to demonstrate the validity of our models.

Description

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.

Keywords

multiple attribute decision making, strategic weight manipulation, ranking, incomplete information

Citation

Liu, Y. et al. (2017) Strategic Weight Manipulation in Multiple Attribute Decision Making in an Incomplete Information Context. Proceedings of FUZZ-IEEE 2017,

Rights

Research Institute

Institute of Artificial Intelligence (IAI)