Strategic weight manipulation in multiple attribute decision making
OmegaPublished 18 March 2017Open access
Yucheng Dong, Yating Liu, Haiming Liang, Francisco Chiclana, Enrique Herrera‐Viedma
Citations230
SJR quartileQ1
SJR score2.31
SNIP2.20
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TL;DR
This paper defines the concept of the ranking range of an alternative in the MADM, and proposes a series of mixed 0–1 linear programming models (MLPMs) to show the process of designing a strategic attribute weight vector.
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. In this paper, we define the concept of the ranking range of an alternative in the MADM, and propose a series of mixed 0-1 linear programming models (MLPMs) to show the process of designing a strategic attribute weight vector. Then, we reveal the conditions to manipulate a strategic attribute weight based on the \nranking range and the proposed MLPMs. Finally, a numerical example with real background is used to demonstrate the validity of our models, and simulation experiments are presented to show the better performance of the ordered weighted averaging operator than the weighted averaging operator in defending against the strategic weight manipulation of the MADM problems.
Keywords
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