Due to the large number of submissions that more and more conferences experience, finding an automatized way to well distribute the submitted papers among reviewers has become necessary. We model the peer-reviewing matching problem as a {\it bilevel programming (BP)} formulation. Our model consists of a lower-level problem describing the reviewers' perspective and an upper-level problem describing the editors'. Every reviewer is interested in minimizing their overall effort, while the editors are interested in finding an allocation that maximizes the quality of the reviews and follows the reviewers' preferences the most. To the best of our knowledge, the proposed model is the first one that formulates the peer-reviewing matching problem by considering two objective functions, one to describe the reviewers' viewpoint and the other to describe the editors' viewpoint. We demonstrate that both the upper-level and lower-level problems are feasible and that our BP model admits a solution under mild assumptions. After studying the properties of the solutions, we propose a heuristic to solve our model and compare its performance with the relevant state-of-the-art methods. Extensive numerical results show that our approach can find fairer solutions with competitive quality and less effort from the reviewers.

翻译：由于越来越多的会议收到大量投稿，寻找一种自动化方式来合理分配论文给审稿人已成为必要。我们将同行评审匹配问题建模为一种{\it 双层规划（BP）}形式。该模型包含一个描述审稿人视角的下层问题和一个描述编辑视角的上层问题。每位审稿人致力于最小化自身总工作量，而编辑则致力于寻找一种既能最大化评审质量又能最贴合审稿人偏好的分配方案。据我们所知，所提出的模型是首个通过考虑两个目标函数来形式化描述同行评审匹配问题的模型，其中一个目标函数描述审稿人视角，另一个描述编辑视角。我们证明了上层问题和下层问题均具有可行性，并且该双层规划模型在温和假设下存在解。在研究了解的性质之后，我们提出了一种启发式算法来求解模型，并将其性能与相关最新方法进行了比较。大量数值结果表明，我们的方法能够找到更公平的解，且具有竞争性的质量和更低的审稿人工作量。