heuristictree

Heuristic Methods for Minimizing Cut Bars and Using Leftovers from the One-dimensional Cutting Process - TREE Heuristic.

Getting Started

Dependencies

You need Python 3.8 or later to use heuristictree. You can find it at python.org.

Installation

pip install heuristictree

Features

In this heuristic, the losses of the cutting process are concentrated on the smallest number of bars possible, using a tree structure, in order to become losses (unusable) into leftovers (usable).

Example file:

1188
229	2
208	1
400	1
327	3
373	3
182	3
285	2
88	1
154	1
83	3

First line represents the size of the bar to be cut.
The other lines represent the size of each item to be cut and the cutting demand, respectively.

Example

heuristictree run <your_file.txt>

Output

The output.txt file contains the cutting patterns obtained from executing the HeuristicTree.

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

Citation

If you use this software in your work, please cite our paper.

Bressan, G.M.; Pimenta-Zanon, M.H.; Sakuray, F. A Tree-Based Heuristic for the One-Dimensional Cutting Stock Problem Optimization Using Leftovers. Materials 2023, 16, 7133. https://doi.org/10.3390/ma16227133

@article{Bressan2023,
  title = {A Tree-Based Heuristic for the One-Dimensional Cutting Stock Problem Optimization Using Leftovers},
  volume = {16},
  ISSN = {1996-1944},
  url = {http://dx.doi.org/10.3390/ma16227133},
  DOI = {10.3390/ma16227133},
  number = {22},
  journal = {Materials},
  publisher = {MDPI AG},
  author = {Bressan,  Glaucia Maria and Pimenta-Zanon,  Matheus Henrique and Sakuray,  Fabio},
  year = {2023},
  month = nov,
  pages = {7133}
}

License

MIT