پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 163 |
| تعداد شماره‌ها | 6,761 |
| تعداد مقالات | 72,826 |
| تعداد مشاهده مقاله | 131,642,403 |
| تعداد دریافت فایل اصل مقاله | 103,419,357 |
A Mathematical Model for Reviewer Assignment Problem: Balancing Maximum Coverage, Fairness, and Expertise Matching | ||
| مدیریت صنعتی | ||
| دوره 17، شماره 2، 1404، صفحه 149-174 اصل مقاله (1.41 M) | ||
| نوع مقاله: مقاله علمی پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/imj.2025.384968.1008200 | ||
| نویسندگان | ||
| Jalil Heidary Dahooie* 1؛ Raheleh Fathollahi2؛ Mohammad Gholami2 | ||
| 1Associate Prof., Faculty of Industrial Management & Technology, College of Management, University of Tehran, Tehran, Iran. | ||
| 2MSc., Faculty of Industrial Management & Technology, College of Management, University of Tehran, Tehran, Iran. | ||
| چکیده | ||
| 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. | ||
| کلیدواژه‌ها | ||
| reviewer assignment؛ maximum coverage؛ fairness؛ expertise matching | ||
| مراجع | ||
|
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. 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. Carpenter, J. M., Corvillón, A., & Shah, N. B. (2024). Enhancing Peer Review in Astronomy: A Machine Learning and Optimization Approach to Reviewer Assignments for ALMA. arXiv preprint arXiv:2410.10009. Charlin, L., Zemel, R. S., & Boutilier, C. (2011). A Framework for Optimizing Paper Matching. In UAI (Vol. 11, pp. 86-95). 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. 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. (in Persian) Daş, G. S., & Göçken, T. (2014). A fuzzy approach for the reviewer assignment problem. Computers & industrial engineering, 72, 50-57. Delavar, A. (2008). Research method in psychology and educational sciences. Tehran: Ravan, Virayesh, Arasbaran. (in Persian) 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. 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). 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. 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). IEEE. 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. Kalmukov, Y. (2013). Describing papers and reviewer’s' competences by taxonomy of keywords. arXiv preprint 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). Karimzadehgan, M., & Zhai, C. (2012). Integer linear programming for constrained multi-aspect committee review assignment. Information processing & management, 48(4), 725-740. Land, A.H., Doig, A.G. (1960). An automatic method of solving discrete programming problems, Econometrica, 28, 497-520. Lappas, T., Liu, K., & Terzi, E. (2009). Finding a team of experts in social networks. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 467-476). 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. 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). Springer International Publishing. Li, X., & Watanabe, T. (2013). Automatic Paper-to-reviewer Assignment, Based on the Matching Degree of the Reviewers. Procedia Computer Science, 22, 633-642. 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). IEEE. 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. 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). 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. Ribeiro, A. C., Sizo, A., & Reis, L. P. (2023). Investigating the reviewer assignment problem: A systematic literature review. Journal of Information Science, 0(0). Rordorf, D., Käser, J., Crego, A., & Laurenzi, E. (2023, March). 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. 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. Simon, H. A., & Newell, A. (1958). Heuristic problem solving: The next advance in operations research. Operations research, 6(1), 1-10. Stelmakh, I., Wieting, J., Neubig, G., & Shah, N. B. (2023). A gold standard dataset for the reviewer assignment problem. arXiv preprint 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. Wang, F., Zhou, S., & Shi, N. (2013). Group-to-group reviewer assignment problem. Computers & operations research, 40(5), 1351-1362. 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. Zhao, X., & Zhang, Y. (2022). Reviewer assignment algorithms for peer review automation: A survey. Information Processing & Management, 59(5), 103028.
| ||
|
آمار تعداد مشاهده مقاله: 26 تعداد دریافت فایل اصل مقاله: 22 |
||
سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب