A hybrid grouping genetic algorithm for reviewer group construction problem

Authors:
Yuan Chen;Zhi-Ping Fan;Jian Ma;Shuo Zeng
Affiliations:
Department of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai, PR China;Department of Management Science and Engineering, School of Business Administration, Northeastern University, Shenyang, PR China;Department of Information Systems, City University of Hong Kong, Kowloon, Hong Kong, China;Department of Management Information Systems, University of Arizona, United States
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

It is a common task to construct the reviewer group with diverse background between reviewers. This task is complicated considering the multiple criteria and sizable reviewers and groups. However, it has not been clearly addressed in the current studies. This paper investigates this problem and proposes a solution approach. In our study, this problem is firstly formulated as an integrated model that covers the situations of different group number and group size. Then, considering the computational difficulties of solving this model, the grouping genetic algorithm hybridizing the local neighborhood search heuristic is proposed. In the grouping genetic algorithm, the initialization, crossover and mutation are designed according to our problem's characteristics. Extensive numerical experiments show that the proposed algorithm is computationally efficient. Moreover, the application of the proposed algorithm on a case from NSFC also indicates its effectiveness for practical problems.