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Solving the Generalized Assignment Problem: An Optimizing and Heuristic Approach
INFORMS Journal on Computing
Optimal Allocation of Proposals to Reviewers to Facilitate Effective Ranking
Management Science
An organizational decision support system for effective R&D project selection
Decision Support Systems
A stochastic beam search for the berth allocation problem
Decision Support Systems
A hybrid knowledge and model approach for reviewer assignment
Expert Systems with Applications: An International Journal
An algorithm to determine peer-reviewers
Proceedings of the 17th ACM conference on Information and knowledge management
Heuristic-biased stochastic sampling
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
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The Reviewer Assignment Problem is a critical management problem faced by academic journals, conferences, and research funding agencies. Previous relevant literature focuses on performing an optimized assignment of manuscripts (or proposals) to reviewers. In this paper, we study a group-to-group reviewer assignment problem, where manuscripts and reviewers are divided into groups, with groups of reviewers are assigned to groups of manuscripts. We formulate this problem as a multi-objective mixed integer programming model, which is proven NP-hard. An effective two-phase stochastic-biased greedy algorithm is then proposed to solve the problem. Results of comprehensive experiments demonstrate the effectiveness of the algorithm. The approach is applied to a real application, for which the result receive positive and encouraging feedback from users.