A Mathematical Model for Reviewer Assignment Problem: Balancing Maximum Coverage, Fairness, and Expertise Matching

Document Type : Research Paper

Authors

1 Associate Prof., Faculty of Industrial Management & Technology, College of Management, University of Tehran, Tehran, Iran.

2 MSc., Faculty of Industrial Management & Technology, College of Management, University of Tehran, Tehran, Iran.

Abstract

Objective: This study tackles the reviewer assignment problem by proposing a model that optimizes reviewer-proposal matching based on thematic coverage, fairness, and expertise, while considering workload balance and team size constraints. The model incorporates practical constraints such as limits on the number of proposals each reviewer can handle and team composition requirements. This approach is especially relevant to institutions like academic conferences, journals, and funding organizations, aiming to enhance the integrity and efficiency of the review process.
Methods: This study is classified as descriptive research with a practical orientation and relies on data collection through applied methods. The approach is grounded in mathematical modeling. Initially, the selected articles are grouped into clusters. Reviewers are then assigned to these clusters using a multi-objective binary integer programming model that incorporates all relevant criteria and constraints. To implement this model, 150 articles were selected through purposive sampling. The model was optimized using Python, employing both the branch-and-bound algorithm and a genetic metaheuristic algorithm to maximize the degree of reviewer-proposal matching within the proposed framework. 
Results: The proposed model demonstrates strong practical relevance by closely reflecting real-world reviewer assignment challenges. By simultaneously optimizing thematic coverage, evaluation fairness, and reviewer expertise, the model captures the complexity of actual allocation scenarios. To validate its effectiveness, the model was solved using both the branch-and-bound algorithm and a genetic algorithm. The branch-and-bound method yielded an objective value of 177.349 in approximately one hour, while the genetic algorithm reached 120.35 in just seven minutes. Although branch-and-bound guarantees optimality, its longer runtime makes it less practical for larger datasets. Given the similarity of results, the genetic approach is a reliable and scalable alternative.
Conclusion: This study introduces a new allocation strategy and mathematical model for reviewer assignment, addressing often-overlooked factors such as reviewer expertise, grouping, and conflicts of interest. By integrating these elements, the proposed model better reflects real-world conditions. Future work is encouraged to expand on these findings with new frameworks and methods.

Keywords


Aksoy, M., Yanik, S., & Amasyali, M. F. (2023). Reviewer Assignment Problem: A Systematic Review of the Literature. Journal of Artificial Intelligence Research, 76, 761-827. https://doi.org/10.1613/jair.1.14318
Bouanane, K., Medakene, A. N., Benbelghit, A., & Belhaouari, S. B. (2024). FairColor: An efficient algorithm for the balanced and fair reviewer assignment problem. Information Processing & Management, 61(6), 103865. https://doi.org/10.1016/j.ipm.2024.103865
Carpenter, J. M., Corvillón, A., & Shah, N. B. (2025). Enhancing Peer Review in Astronomy: A Machine Learning and Optimization Approach to Reviewer Assignments for ALMA. Publications of the Astronomical Society of the Pacific, 137(3), 034501. https://doi.org/10.1088/1538-3873/adb5c1
Charlin, L., Zemel, R. S., & Boutilier, C. (2011). A Framework for Optimizing Paper Matching. In UAI (Vol. 11, pp. 86-95). https://doi.org/10.48550/arXiv.1202.3706
Cook, W. D., Golany, B., Kress, M., Penn, M., & Raviv, T. (2005). Optimal allocation of proposals to reviewer’sto facilitate effective ranking. Management Science, 51(4), 655-661. https://doi.org/10.1287/mnsc.1040.0290
Daraei, F. (2022). Combining Genetic Algorithms And Motor Nest Optimization To Solve The Supplier's Selection Problem, Journal of Intelligent Marketing Management, 3(3), 41-81.
Daş, G. S., & Göçken, T. (2014). A fuzzy approach for the reviewer assignment problem. Computers & industrial engineering, 72, 50-57. https://doi.org/10.1016/j.cie.2014.02.014
Delavar, A. (2008). Research method in psychology and educational sciences. Tehran.
Fan, Z. P., Chen, Y., Ma, J., & Zhu, Y. (2009). Decision support for proposal grouping: A hybrid approach using knowledge rule and genetic algorithm. Expert systems with applications, 36(2), 1004-1013. https://doi.org/10.1016/j.eswa.2007.11.011
Hettich, S., & Pazzani, M. J. (2006). Mining for proposal reviewer’s: lessons learned at the national science foundation. In Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 862-871). https://doi.org/10.1145/1150402.1150521
Hoang, D. T., Nguyen, N. T., Collins, B., & Hwang, D. (2021). Decision support system for solving reviewer assignment problem. Cybernetics and Systems, 52(5), 379-397. https://doi.org/10.1080/01969722.2020.1871227
Immanuel, S. D., & Chakraborty, U. K. (2019). Genetic algorithm: An approach on optimization. In 2019 international conference on communication and electronics systems (ICCES) (pp. 701-708).                                                  https://doi: 10.1109/ICCES45898.2019.9002372.
Kamps, J., Marx, M., Mokken, R. J., & De Rijke, M. (2004). Using WordNet to measure semantic orientations of adjectives. In Lrec (Vol. 4, pp. 1115-1118).
Kolasa, T., & Krol, D. (2011). A survey of algorithms for paper-reviewer assignment problem. IETE Technical Review, 28(2), 123-134. https://doi.org/10.4103/0256-4602.78092
Kalmukov, Y. (2013). Describing papers and reviewer’s' competences by taxonomy of keywords. arXiv preprint arXiv:1309.6527. https://doi.org/10.48550/arXiv.1309.6527
Karimzadehgan, M., Zhai, C., & Belford, G. (2008). Multi-aspect expertise matching for review assignment. In Proceedings of the 17th ACM conference on Information and knowledge management (pp. 1113-1122). https://doi.org/10.1145/1458082.1458230
Karimzadehgan, M., & Zhai, C. (2012). Integer linear programming for constrained multi-aspect committee review assignment. Information processing & management, 48(4), 725-740. https://doi.org/10.1016/j.ipm.2011.09.004
Land, A. H., & Doig, A. G. (2009). An automatic method for solving discrete programming problems. In 50 Years of Integer Programming 1958-2008: (pp. 105-132). https://doi.org/10.1007/978-3-540-68279-0_5
 Leyton-Brown, K., Nandwani, Y., Zarkoob, H., Cameron, C., Newman, N., & Raghu, D. (2024). Matching papers and reviewer’sat large conferences. Artificial Intelligence, 331, 104119. https://doi.org/10.1016/j.artint.2024.104119
Li, K., Cao, Z., & Qu, D. (2017). Fair reviewer assignment considering academic social network. In Web and Big Data: First International Joint Conference, APWeb-WAIM 2017, Beijing, China, July 7–9, 2017, Proceedings, Part I 1 (pp. 362-376). https://doi.org/10.1007/978-3-319-63579-8_28
Li, X., & Watanabe, T. (2013). Automatic Paper-to-reviewer Assignment, Based on the Matching Degree of the Reviewers. Procedia Computer Science, 22, 633-642. https://doi.org/10.1016/j.procs.2013.09.144
Long, C., Wong, R. C. W., Peng, Y., & Ye, L. (2013). On good and fair paper-reviewer assignment. In 2013 IEEE 13th international conference on data mining (pp. 1145-1150).
Luo, X. G., Li, H. J., Zhang, Z. L., & Jiang, W. (2024). Multi-objective optimization for assigning reviewer’sto proposals based on social networks. Journal of Management Science and Engineering. 9(3), 419-439. https://doi.org/10.1016/j.jmse.2024.05.001
Mimno, D., & McCallum, A. (2007). Expertise modeling for matching papers with reviewer’s. In Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 500-509). https://doi.org/10.1145/1281192.1281247
Monteiro, R. D. (1997). School of Industrial and Systems Engineering Georgia Institute of Technology, Atlanta, GA 30332.
Neshati, M., Beigy, H., & Hiemstra, D. (2014). Expert group formation using facility location analysis. Information processing & management, 50(2), 361-383. https://doi.org/10.1016/j.ipm.2013.10.001
Ribeiro, A. C., Sizo, A., & Reis, L. P. (2023). Investigating the reviewer assignment problem: A systematic literature review. Journal of Information Science, 0(0). https://doi.org/10.1177/01655515231176668
Rordorf, D., Käser, J., Crego, A., & Laurenzi, E. (2023). A Hybrid Intelligent Approach Combining Machine Learning and a Knowledge Graph to Support Academic Journal Publishers Addressing the Reviewer Assignment Problem (RAP). In AAAI Spring Symposium: MAKE. https://doi.org/10.26041/fhnw-11149
Schirrer, A., Doerner, K. F., & Hartl, R. F. (2007). Reviewer assignment for scientific articles using memetic algorithms. Metaheuristics: Progress in Complex Systems Optimization, 113-134. https://doi.org/10.1007/978-0-387-71921-4_6
Simon, H. A., & Newell, A. (1958). Heuristic problem solving: The next advance in operations research. Operations research, 6(1), 1-10. https://doi.org/10.1287/opre.6.1.1
Stelmakh, I., Wieting, J., Neubig, G., & Shah, N. B. (2023). A gold standard dataset for the reviewer assignment problem. arXiv preprint arXiv:2303.16750. https://doi.org/10.48550/arXiv.2303.16750
Xu, Y., Ma, J., Sun, Y., Hao, G., Xu, W., & Zhao, D. (2010). A decision support approach for assigning reviewer’sto proposals. Expert Systems with Applications, 37(10), 6948-6956. https://doi.org/10.1016/j.eswa.2010.03.027
Wang, F., Zhou, S., & Shi, N. (2013). Group-to-group reviewer assignment problem. Computers & operations research, 40(5), 1351-1362. https://doi.org/10.1016/j.cor.2012.08.005
Wi, H., Oh, S., Mun, J., & Jung, M. (2012). A team formation model based on knowledge and collaboration. IEEE Engineering Management Review, 40(1), 44-57. https://doi.org/10.1016/j.eswa.2008.12.031
Zhao, X., & Zhang, Y. (2022). Reviewer assignment algorithms for peer review automation: A survey. Information Processing & Management, 59(5), 103028. https://doi.org/10.1016/j.ipm.2022.103028