Classification-based strategic weight manipulation in multiple attribute decision making

In multiple attribute decision making (MADM) problems, decision makers may prefer to classify alternatives into several ordered categories, which is called a classification-based MADM problem. In the process of classification-based MADM, attribute weights play a key role in classification of alternatives due to the fact that different attribute weights may lead to different classification results of alternatives. Thus, a manipulator may strategically set attribute weights to obtain his/her desired classification results of alternatives, which is called classification-based strategic weight manipulation (CBSWM). In this paper, we first give the concept of classification range of alternatives. Subsequently, several mixed 0-1 linear programming models (MLPMs) are constructed to obtain the classification range of alternatives and the strategic attribute weights to obtain a manipulator's desired classification results of alternatives. Furthermore, some existence conditions for the strategic attribute weights are provided. Finally, a numerical example is presented to verify the effectiveness of the proposed models, and two simulation experiments are designed to compare the defending performance against CBSWM for the WA and OWA operators.

Automated summary

This paper investigates the classification-based multiple attribute decision making (MADM) problem and the strategic weight manipulation. By constructing mixed linear programming models, the classification range of alternatives and the strategic attribute weights are analyzed. Moreover, some existence conditions for the strategic attribute weights are provided. Finally, numerical example and simulation experiments are conducted to verify the effectiveness and defending performance of the proposed models.