A multiattribute GDSS for aiding problem-solving

This study proposes a group decision support system (GDSS) with multiattribute to help solve problems in the real world. The problems are usually characterized as a multiattribute decision making (MADM) for selections, and shall be the responsibility of an expert group. On a regular basis, experts within that group will meet and conduct discussions on the web. After each individual make efforts of judgments, comparisons, and rankings, they shall determine, collectively as a group, the final rankings of all possible alternatives. Furthermore, aimed at insuring the decision quality of the collective decisions, an integrated procedure will be applied to make any modifications as necessary. Based on the geometric aspects of decision quality, the disparity of each individual memberʹs preferences on attribute can be filtered out by the suggested bounded indicators. And then the outliers related to attributesʹ weights will be identified through a different set of consensus indicators, thus, further improving the decision quality while maintaining a quantitative level of consensus. Finally, using a car-selection problem herein, the proposed integrated procedure is implemented on a network-based PC system with web interfaces.