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Optimal Allocation of Proposals to Reviewers to Facilitate Effective Ranking

Author

Listed:
  • Wade D. Cook

    (Schulich School of Business, York University, Toronto, Ontario M3J 1P3, Canada)

  • Boaz Golany

    (Faculty of Industrial Engineering and Management, Technion---Israel Institute of Technology, Haifa 32000, Israel)

  • Moshe Kress

    (Center for Military Analyses, POB 2250, Haifa 31021, Israel, and Operations Research Department, Naval Postgraduate School, Monterey, California 93943)

  • Michal Penn

    (Faculty of Industrial Engineering and Management, Technion---Israel Institute of Technology, Haifa 32000, Israel)

  • Tal Raviv

    (Faculty of Industrial Engineering and Management, Technion---Israel Institute of Technology, Haifa 32000, Israel)

Abstract

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.

Suggested Citation

  • Wade D. Cook & Boaz Golany & Moshe Kress & Michal Penn & Tal Raviv, 2005. "Optimal Allocation of Proposals to Reviewers to Facilitate Effective Ranking," Management Science, INFORMS, vol. 51(4), pages 655-661, April.
    • Handle: RePEc:inm:ormnsc:v:51:y:2005:i:4:p:655-661
    DOI: 10.1287/mnsc.1040.0290
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    References listed on IDEAS

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    1. Wade D. Cook & Lawrence M. Seiford, 1978. "Priority Ranking and Consensus Formation," Management Science, INFORMS, vol. 24(16), pages 1721-1732, December.
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    Cited by:

    1. Jian Jin & Baozhuang Niu & Ping Ji & Qian Geng, 2020. "An integer linear programming model of reviewer assignment with research interest considerations," Annals of Operations Research, Springer, vol. 291(1), pages 409-433, August.
    2. Katarína Cechlárová & Tamás Fleiner & Eva Potpinková, 2014. "Assigning evaluators to research grant applications: the case of Slovak Research and Development Agency," Scientometrics, Springer;Akadémiai Kiadó, vol. 99(2), pages 495-506, May.
    3. Xiaoyu Liu & Xuefeng Wang & Donghua Zhu, 2022. "Reviewer recommendation method for scientific research proposals: a case for NSFC," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(6), pages 3343-3366, June.
    4. Yeşilçimen, Ali & Yıldırım, E. Alper, 2019. "An alternative polynomial-sized formulation and an optimization based heuristic for the reviewer assignment problem," European Journal of Operational Research, Elsevier, vol. 276(2), pages 436-450.
    5. Dorit S. Hochbaum & Asaf Levin, 2006. "Methodologies and Algorithms for Group-Rankings Decision," Management Science, INFORMS, vol. 52(9), pages 1394-1408, September.
    6. Vladimír Bureš & Jiří Cabal & Pavel Čech & Karel Mls & Daniela Ponce, 2020. "The Influence of Criteria Selection Method on Consistency of Pairwise Comparison," Mathematics, MDPI, vol. 8(12), pages 1-13, December.
    7. Thomas Feliciani & Junwen Luo & Lai Ma & Pablo Lucas & Flaminio Squazzoni & Ana Marušić & Kalpana Shankar, 2019. "A scoping review of simulation models of peer review," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(1), pages 555-594, October.
    8. Ma, Li-Ching, 2010. "Visualizing preferences on spheres for group decisions based on multiplicative preference relations," European Journal of Operational Research, Elsevier, vol. 203(1), pages 176-184, May.
    9. Cheng, Li-Chen & Chen, Yen-Liang & Chiang, Yu-Chia, 2016. "Identifying conflict patterns to reach a consensus – A novel group decision approach," European Journal of Operational Research, Elsevier, vol. 254(2), pages 622-631.
    10. Linton, Jonathan D., 2016. "Improving the Peer review process: Capturing more information and enabling high-risk/high-return research," Research Policy, Elsevier, vol. 45(9), pages 1936-1938.
    11. Huang, Tony Cheng-Kui, 2013. "A novel group ranking model for revealing sequence and quantity knowledge," European Journal of Operational Research, Elsevier, vol. 231(3), pages 654-666.
    12. Fernandez Martinez, Roberto & Lostado Lorza, Ruben & Santos Delgado, Ana Alexandra & Piedra, Nelson, 2021. "Use of classification trees and rule-based models to optimize the funding assignment to research projects: A case study of UTPL," Journal of Informetrics, Elsevier, vol. 15(1).
    13. Chen, Yen-Liang & Cheng, Li-Chen, 2009. "Mining maximum consensus sequences from group ranking data," European Journal of Operational Research, Elsevier, vol. 198(1), pages 241-251, October.
    14. Song, Lianlian & Fu, Yelin & Zhou, Peng & Lai, Kin Keung, 2017. "Measuring national energy performance via Energy Trilemma Index: A Stochastic Multicriteria Acceptability Analysis," Energy Economics, Elsevier, vol. 66(C), pages 313-319.

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