This dataset accompanies the research paper titled "A Machine Learning Approach to Rank Pricing Problems in Branch-and-Price." It features a machine learning-based ranker designed to enhance the column generation process by guiding the search for new columns. The application of this ranker is evaluated within the context of operating room scheduling.The dataset is splitted into two directories: "train" and "evaluate."The "train" directory contains the data used for training the machine learning models as detailed in the publication. This directory includes a dataset description and a CSV file capturing the recorded features. Additionally, it houses an 'instance' directory subdivided into four subdirectories (r1_s2_d10, r1_s2_d5, r1_s4_d10, r1_s4_d5), each containing 30 instances. These instances are characterized by the number of patients (10, 20, 30), the number of days (5, 10), and the number of surgeons (2, 4), culminating in a total of 120 instances across all subdirectories.The "evaluate" directory is structured to validate the methodologies developed in the research. It includes an 'input' folder with two subfolders (r1_s4_d5, r1_s4_d10), each containing 9 instances. These instances are characterized by the number of patients (30, 40, 50), the number of days (5, 10), and the number of surgeons (4), culminating in a total of 18 instances across all subdirectories. These instances are utilized to assess and validate the strategies delineated in the paper. The 'output' folder within the directory documents the results for each strategy (best and random branch-and-price, ML strategy, and ILP strategy) with 1, 3, 5, 10, 15, 20 patterns added. Results for each input instance are aggregated here, including CSV files of results, PDFs of branching trees, and output log files.All instances are stored as JSON files and represent synthetically generated data that simulate real-life hospital environments. The methodology employed to generate the data is elaborated in the corresponding paper.

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