PyPRS: A Python Software Package for Parallel Ranking and Selection Procedures

We introduce Python Parallel Ranking and Selection (PyPRS), a Python software package specifically developed to solve large-scale ranking and selection problems in parallel computing environments. The underlying parallel computing framework is Ray. PyPRS incorporates four well-known parallel procedures: the good selection procedure, the parallel adaptive survivor selection procedure, the knockout-tournament procedure, and the fixed-budget knockout-tournament procedure. The key features of PyPRS include (i) a modular structure that facilitates easy extension, enhancement, and customization of procedures; (ii) a plug-and-play functionality that enables easy applications of both built-in and custom procedures to various problems; and (iii) an intuitive graphical user interface that improves user accessibility and ease of operation. History: Accepted by Ted Ralphs, Area Editor for Software Tools. Funding: G. Jiang was supported by the National Natural Science Foundation of China Grants 72293562 and 72171060. Y. Zhong was supported by the National Natural Science Foundation of China Grants 72571049 and 72101047, the Major Program of National Social Science Foundation of China Grant 25&ZD196, and the Humanities and Social Science Fund of Ministry of Education of China Grant 24XJA630003. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2024.1045 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2024.1045 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .