Data · dataset · 2026
A Two-Stage Constraint Programming Approach for Artificial Teeth Scheduling Models and Results
Listed in TU Wien Research Data
Supplemental dataset for the paper “A Two-Stage Constraint Programming Approach for Artificial Teeth Scheduling”, accepted for publication at the 32nd International Conference on Principles and Practice of Constraint Programming .
Description
Conference information is available at CP 2026 conference website . A link to the open-access publication will be added once the paper becomes publicly available.
Dataset Contents The dataset contains the complete implementation and experimental material used for the two-stage constraint programming approach presented in the paper. The main entry point is the 2stage_solver.py script, which orchestrates the full workflow across both optimization stages. The stage1/ directory contains all resources required for the first stage of the approach, including scripts, benchmark instances, MiniZinc models ( .mzn files), and the column generation solver implementation.
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The stage2/ directory contains the corresponding scripts, benchmark instances, and MiniZinc models for the second stage of the scheduling process. Experimental outcomes discussed in the publication are summarized in the results/ directory. Detailed information regarding solver settings, software versions, hardware configuration, and parameterization is documented in the paper itself.
Configuration templates for reproducing experiments and running customized executions are provided in the configs/ directory. Software Requirements Reproducing the experiments and running the provided solvers requires the following software environment: Python version 3.11 or newer, available from Python MiniZinc version 2.9.5 or newer, available from MiniZinc . MiniZinc command-line tools must be accessible through the system PATH environment variable.
Gurobi version 9 or newer, available from Gurobi Optimizer , together with the gurobipy Python package. Usage and Licensing Detailed instructions for installing dependencies and executing the solver pipeline are provided in the README.txt file included in the dataset. All data is licensed under the CC BY 4.0 license.
All source code is distributed under the MIT License. See LICENSE.txt for the full license text.
Links
Where it is published
- TU Wien Research Data record researchdata.tuwien.ac.at/records/qvzg8-c9t90 ↗
landing page · from researchdata tuwien ac at
- DOI doi.org/10.48436/qvzg8-c9t90 ↗
DOI / persistent id · from researchdata tuwien ac at
Catalogue records · 1
- InvenioRDM API researchdata.tuwien.ac.at/api/records/qvzg8-c9t90 ↗
metadata API · from researchdata tuwien ac at
Topics
- Stated by source
- Combinatorial Optimization · Computer and information sciences · Constraint Programming
- Inferred from text
- Artificial intelligence 75% · Text 75%
Provenance · 1 source records, 11 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| TU Wien Research Data | qvzg8-c9t90 | 12 d ago | JSON v1 |
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|---|---|---|---|
| access_level | source · researchdata tuwien ac at | connector:researchdata_tuwien_ac_at@1.0.0 | |
| concepts[field].anzsrc:group:4602 | enrichment · researchdata tuwien ac at | taxonomy-embedding@1.1.0 | title+keywords+description (75%) |
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| concepts[topic].invenio_subject:researchdata_tuwien_ac_at:constraint-programming | source · researchdata tuwien ac at | connector:researchdata_tuwien_ac_at@1.0.0 | |
| description | source · researchdata tuwien ac at | connector:researchdata_tuwien_ac_at@1.0.0 | /metadata/description |
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| publication_date | source · researchdata tuwien ac at | connector:researchdata_tuwien_ac_at@1.0.0 | |
| title | source · researchdata tuwien ac at | connector:researchdata_tuwien_ac_at@1.0.0 | /metadata/title |
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