ESICUP Cutting & Packing Datasets

This repository contains a collection of datasets for academic research in Cutting and Packing (C&P) problems, maintained by the ESICUP community. The datasets are intended to facilitate benchmarking and comparison of different algorithms and approaches.

Note

This resource was previously hosted at https://www.euro-online.org/websites/esicup/data-sets/, but has been migrated to GitHub in an effort to improve maintainability and long-term availability.

Structure

The datasets are organized into subdirectories based on the dimensionality and type of the cutting and packing problems they address. Each subdirectory contains a README file that provides information about the datasets, including their source, characteristics, and any relevant references.

The main subdirectories are:

• 1d: Datasets for one-dimensional cutting and packing problems.
• 2d_rectangular: Datasets for two-dimensional rectangular cutting and packing problems.
• 2d_irregular: Datasets for two-dimensional irregular cutting and packing problems.
• 3d_rectangular: Datasets for three-dimensional rectangular cutting and packing problems.
• 3d_irregular: Datasets for three-dimensional irregular cutting and packing problems.
• misc: Miscellaneous datasets that do not fit into the above categories.

Contribute

As this is a community-driven resource, contributions are welcome! To contribute, please fork this repository, make your changes, and submit a pull request for review.

We encourage contributions in the following areas:

New Dataset

Let's go through an example of contributing a new dataset called my_new_dataset which contains a new 1D C&P dataset.

1. Edit the 1d/README.md file to include a new entry for my_new_dataset in alphabetical order, following the format of existing entries. For example:
   • my_new_dataset
     • Author(s). (Year). Title of the publication. Journal/Conference, Volume(Issue), Page numbers. DOI: https://doi.org/xxxx
     • A very brief description of the dataset and its characteristics.
2. Create a new folder 1d/my_new_dataset and add your source files there.
3. Make sure the 1d/my_new_dataset directory contains a README.md file describing the contents, characteristics and the file format in detail.
4. Commit your changes and submit a pull request for review.

Ensure to always provide proper reference with DOI for the dataset (can be either a preprint or published version). We also welcome converted datasets, but do require the original source files to also exist in the repository.

Tip

See this pull request as an example of a new dataset contribution to this repository.

Corrections and Improvements

This repository started off as a direct clone of the ESICUP datasets webpage. However, some entries contained incomplete/messy descriptions and/or files.

If you detect any errors, inconsistencies or have suggestions for improvements, please feel free to submit an issue or (preferably) open a pull request with the necessary changes.