This page contains supplementary research material (test instances, detailed solutions, open source code) related to optimization problems and machine learning in general. This material is connected to the papers hereby listed.

Benchmark Instances & Detailed Results

Job Sequencing and Tool Switching Problem. The benchmark instances used in "Mecler, J., Subramanian, A., & Vidal, T. (2019). A simple and effective hybrid genetic search for the job sequencing and tool switching problem." Can be found HERE.

Support Vector Ordinal Regression. The datasets used in "Vidal, T., Gribel, D., & Jaillet, P. (2019). Separable convex optimization with nested lower and upper constraints. INFORMS Journal on Optimization, 1(1), 71–90." Can be found HERE.

Hashiwokakero Puzzles. The benchmark instances used in "Coelho, L., Laporte, G., Lindbeck, A. & Vidal, T. (2018). Solving the Hashiwokakero Puzzle through Branch-and-Cut." Can be found HERE.

Fixed Route Lateral Transhipment Problem. The benchmark instances used in "Romauch, M., Vidal, T., & Hartl, R.F. (2018). On a Fixed-Route Lateral Transhipment Problem with Piecewise Linear Profits." are documented HERE. The complete set of instances is provided HERE in HTML and HERE as a ZIP. The detailed results and provided HERE .

Soft Rectangle Packing Problems. The benchmark instances used in "Bui, Q.T., Vidal, T., & Hà, M.H. (2018). On three soft rectangle packing problems with guillotine constraints." Can be found HERE along with a description of the format.

2D-Phase Unwrapping - Minimum Spanning Forest with Balance Constraints. The benchmark instances used in "Herszterg, I., Poggi, M., & Vidal, T. (2018). 2D-phase unwrapping via balanced spanning forests. INFORMS Journal on Computing" Can be found HERE along with a description of the format.

Weighted Dominating Set Problem. The benchmark instances used in the paper "Albuquerque, M., & Vidal, T. (2018). A Simple and Efficient Matheuristic for the Minimum-Weight Dominating Set Problem" as well as all detailed solutions are available HERE.

Minimum Sum-of-Squares Clustering. The datasets based on mixtures of spherical Gaussian distributions, used in the paper "Gribel, D., & Vidal, T. (2018). HG-means, A scalable hybrid metaheuristic for minimum sum-of-squares clustering" are available HERE (warning, this is a large file of 1.7GB) and the original labels (ground truth) are HERE. The instructions about the file format are available HERE. Detailed results for the UCI datasets are available HERE, as well as a copy of the datasets used in this research HERE.

Unequal Area Facility Layout Problem. The benchmark instances used in "Paes, F. G., Pessoa, A. A., & Vidal, T. (2017). A Hybrid Genetic Algorithm with Decomposition Phases for the Unequal Area Facility Layout Problem. European Journal of Operational Research", 256(3), 742-756. Can be found HERE along with a description of the format.