A new group ranking approach for ordinal preferences based on group maximum consensus sequences

Group ranking problems involve aggregating individual rankings to generate group ranking which represents consolidated group preference. Group ranking problems are commonly applied in real-world decision-making problems; however, supporting a group decision-making process is difficult due to the existence of multiple decision-makers, each with his/her own opinions. Hence, determining how to best aid the group ranking process is an important consideration. This study aims to determine a total ranking list which meets group consensus preferences for group ranking problems. A new group consensus mining approach based on the concept of tournament matrices and directed graphs is first developed; an optimization model involving maximum consensus sequences is then constructed to achieve a total ranking list. Compared to previous methods, the proposed approach can generate a total ranking list involving group consensus preferences. It can also determine maximum consensus sequences without the need for tedious candidate generation processes, while also providing flexibility in solving ranking problems using different input preferences that vary in format and completeness. In addition, consensus levels are adjustable.

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