Scheduling Data Sets for Small/Medium and Large Instances, Including Real Industrial Problem

Description This repository contains all data sets used in our manuscript on batch scheduling, where we jointly consider carbon-footprint (green) objectives and tardiness (on-time) objectives. Two main categories of data sets are provided: Small/Medium Instances (8,3,2),(10,3,2),(15,3,2),(20,4,3),(25,4,5) Each triple (i,f,k) indicates i jobs, f families, and k machines. These instances were used to compare Mixed Integer Programming (MIP), Genetic Algorithm (GA), and Ant Colony Optimization (ACO) methods on moderately sized scheduling problems. Large/Real and Scenario Instances (100,5,5), (100,5,10) (real industrial setting), (100,10,10), (200,5,5), (200,5,10), and (200,10,10). These larger data sets include the actual industrial scenario (100,5,10) plus several extended “what-if” cases that scale the numbers of jobs, machines, or families. Data Format Each instance folder contains four CSV files: jobs.csv: Job-level data (e.g., job_id, family, weight, due_date.) families.csv: Family-level data (e.g., processing_time per family). setup.csv: A matrix or list specifying setup times from one family to another. machine_capacity.csv: Machine IDs and their capacity limits. Use and Reference These data sets enable testing and comparison of exact (MIP) or heuristic (GA, ACO) scheduling methods under various problem sizes. If you make use of these data in your research, please cite both this Zenodo repository and our paper for proper attribution. Paper Reference:Eroğlu, D. Y. (2025). Green and On-Time Scheduling in Real-World Textile Production Using MIP, GA, and ACO Approaches. International Journal of Production Research. (In Revision)