Dataset of Standard Re-entrant Hybrid Flow Shop Scheduling for New-energy Battery Substrate Manufacturing
Description
This benchmark dataset is curated standardized re-entrant hybrid flow shop scheduling instances reconstructed from desensitized real engineering data from a leading new-energy battery substrate manufacturer in China. Existing public re-entrant scheduling benchmarks mainly focus on semiconductor wafer production, while standardized test datasets targeting long-cycle batch manufacturing of battery substrates are rarely available, which this dataset fills. The dataset covers a complete seven-operation production line, with the 4th annealing stage defined as the bottleneck workstation. The parallel machine configuration for each operation is set as (2, 2, 2, 3, 1, 2, 1), as listed in Table A.1. Fixed batch setup time and fixed pure processing time for each non-annealing operation are provided; processing time and setup time equal zero for missing operations in reentrant routes. Four product families F1–F4 with unique annealing durations (7.6 h, 8.0 h, 8.4 h, 8.8 h) are defined in Table A.2, together with 12 standardized reentrant paths covering 0 to 3 rolling-annealing circulation layers, represented by (j,l) operation pairs. A sequence-dependent family changeover time matrix exclusive to annealing machines is supplied in Table A.3. Five multi-scale instance groups G1–G5 are constructed, corresponding to 10, 20, 30, 40 and 50 batches respectively. Each batch stands for one coil group containing three raw coils. Table A.4 records full detailed job parameters of the smallest instance G1, including job ID, product family, path, due date and sequence of non-zero processing durations. Table A.5 lists the quantity of four families and uniform random due date intervals for each group, and Table A.6 provides the allocation count of 12 paths for G2 to G5 to support reproducible expansion of large-scale instances. All tabular data are stored in editable Word format for direct simulation coding and intelligent scheduling algorithm verification. This dataset incorporates multiple practical industrial constraints including fixed batch capacity, multi-layer reentry and family-based annealing changeover, which can be used to evaluate hybrid flow shop models and metaheuristic optimization algorithms. This dataset is co-submitted alongside the related research article published in Journal of Industrial Information Integration. By releasing the standardized instances publicly, this work provides shared test resources for research on new-energy material production scheduling and industrial information integration systems.
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Institutions
- Xiamen University of TechnologyFujian, Xiamen
- Tianjin University of TechnologyTianjin, Tianjin