DSS research and practice in perspective
Proceedings of the conference on First specialized conference on decision support systems
Computers and Operations Research
Supporting facilitation in group support systems: techniques for analyzing consensus relevant data
Decision Support Systems
Information acquisition in group decision making
Information and Management
Extensions of the TOPSIS for group decision-making under fuzzy environment
Fuzzy Sets and Systems
Fuzzy Multiple Attribute Decision Making: Methods and Applications
Fuzzy Multiple Attribute Decision Making: Methods and Applications
Past, present, and future of decision support technology
Decision Support Systems - Special issue: Decision support systems: Directions for the next decade
Group decision making using the analytic hierarchy process
Mathematical and Computer Modelling: An International Journal
Recruitment and selection processes through an effective GDSS
Computers & Mathematics with Applications
A hybrid MCDM model for strategic vendor selection
Mathematical and Computer Modelling: An International Journal
An extension of TOPSIS for group decision making
Mathematical and Computer Modelling: An International Journal
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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 member's preferences on attribute can be filtered out by the suggested bounded indicators. And then the outliers related to attributes' 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.