Browsing by Author "Liu, Yating"

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    Multiple attribute strategic weight manipulation with minimum cost in a group decision making context with interval attribute weights information
    (IEEE, 2018-10-26) Liu, Yating; Dong, Yucheng; Liang, Haiming; Chiclana, Francisco; Herrera-Viedma, Enrique
    In multiple attribute decision making (MADM), strategic weight manipulation is understood as a deliberate manipulation of attribute weights setting to achieve a desired ranking of alternatives. In this paper, we study the strategic weight manipulation in a group decision making context with interval attribute weights information. In group decision making, the revision of the decision makers’ original attribute weights information implies a cost (the difference between the original information and the revised one). Driven by a desire to minimize the cost, we propose the minimum cost strategic weight manipulation model, which is achieved via optimization approaches, with the 0-1 mixed linear programming model being proved appropriate in this context. Meanwhile, some desired properties to manipulate a strategic attribute weight based on the ranking range under interval attribute weights information are proposed. Finally, numerical analysis and simulation experiments are provided with a two-fold aim: (1) to verify the validity of the proposed models, and (2) to show the effects of interval attribute weights information and the unit cost, respectively, on the cost to manipulate strategic weights in the MADM in a group decision context.
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    Strategic weight manipulation in multiple attribute decision making
    (Elsevier, 2017-03-18) Dong, Yucheng; Liu, Yating; Liang, Haiming; Chiclana, Francisco; Herrera-Viedma, Enrique
    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.
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    Strategic Weight Manipulation in Multiple Attribute Decision Making in an Incomplete Information Context
    (IEEE Xplore, 2017-08-24) Liu, Yating; Dong, Yucheng; Chiclana, Francisco; Cabrerizo, Francisco Javier; Herrera-Viedma, Enrique
    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.