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    Strategic weight manipulation in multiple attribute decision making

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    Author's copy of accepted paper. (656.2Kb)
    Date
    2017-03-18
    Author
    Dong, Yucheng;
    Liu, Yating;
    Liang, Haiming;
    Chiclana, Francisco;
    Herrera-Viedma, Enrique
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    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 ranking 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.
    Description
    Citation : Dong, Y. et al. (2017) Strategic weight manipulation in multiple attribute decision making. Omega: The International Journal of Management Science, 75, pp. 154-164
    URI
    http://hdl.handle.net/2086/13492
    DOI
    https://doi.org/10.1016/j.omega.2017.02.008
    Research Group : Centre for Computational Intelligence
    Research Institute : Institute of Artificial Intelligence (IAI)
    Peer Reviewed : Yes
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    • School of Computer Science and Informatics [2970]

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