Methodologies and Algorithms for Group-Rankings Decision
Management Science
A hybrid knowledge and model approach for reviewer assignment
Expert Systems with Applications: An International Journal
A Survey on Reviewer Assignment Problem
IEA/AIE '08 Proceedings of the 21st international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: New Frontiers in Applied Artificial Intelligence
Decision support for proposal grouping: A hybrid approach using knowledge rule and genetic algorithm
Expert Systems with Applications: An International Journal
An approach to group ranking decisions in a dynamic environment
Decision Support Systems
Collaborative filtering with ordinal scale-based implicit ratings for mobile music recommendations
Information Sciences: an International Journal
A decision support approach for assigning reviewers to proposals
Expert Systems with Applications: An International Journal
Using Gower Plots and Decision Balls to rank alternatives involving inconsistent preferences
Decision Support Systems
The k-allocation problem and its variants
WAOA'06 Proceedings of the 4th international conference on Approximation and Online Algorithms
Mining consensus preference graphs from users' ranking data
Decision Support Systems
Group-to-group reviewer assignment problem
Computers and Operations Research
Recommendations of closed consensus temporal patterns by group decision making
Knowledge-Based Systems
| Hi-index | 0.01 |
Peer review of research proposals and articles is an essential element in research and development processes worldwide. Here we consider a problem that, to the best of our knowledge, has not been addressed until now: how to assign subsets of proposals to reviewers in scenarios where the reviewers supply their evaluations through ordinal ranking. The solution approach we propose for this assignment problem maximizes the number of proposal pairs that will be evaluated by one or more reviewers. This new approach should facilitate meaningful aggregation of partial rankings of subsets of proposals by multiple reviewers into a consensus ranking. We offer two ways to implement the approach: an integer-programming set-covering model and a heuristic procedure. The effectiveness and efficiency of the two models are tested through an extensive simulation experiment.