cutting-stock 0.1.3

A Python implementation of the cutting stock problem solver

Cutting Stock Problem

A CVXPY implementation of the cutting-stock problem.

Installation

The project is available on PyPI. You can install it using pip:

pip install cutting-stock

Alternatively, if you are a uv user, you can install the associated CLI directly:

uvx cutting-stock

or you can add it to your environment dependencies:

uv add cutting-stock

Command Line Options

-r, --roll_length: The length of the roll. E.g. -r 12.0
-l, --lengths: The lengths of the items. Must be a list of floats. E.g. -l 3.4 3.0 2.7
-q, --quantities: The quantities of the items. Must be a list of floats. E.g. -q 34 13 5
-s, --solver: The solver to use. Must be either GLPK or ECOS. E.g. -s GLPK
-g, --ge_required: If specified, the constraint is >= instead of ==.
--verbose: If specified, the output will be more verbose.

Example

uv run cutting-stock -r 12.0 -l 3.4 3.0 2.7 -q 34 13 5

This produces the following output:

> uv run cutting-stock
Required pieces:
███ 3.4m ×34   ▓▓▓ 3.0m ×13   ▒▒▒ 2.7m ×5
Stock length: 12m

Solution: 16 stocks of 12m each, 23.9m waste

Use 10× → 3.4m |3.4m |3.4m |1.8m
          █████|█████|█████|···· (12m stock)
Use  2× → 3.4m |3.4m |2.7m|2.5m
          █████|█████|▒▒▒▒|···· (12m stock)
Use  3× → 3.0m |3.0m |3.0m |3.0m 
          ▓▓▓▓▓|▓▓▓▓▓|▓▓▓▓▓|▓▓▓▓▓ (12m stock)
Use  1× → 3.0m |2.7m|2.7m|2.7m|0.9m
          ▓▓▓▓▓|▒▒▒▒|▒▒▒▒|▒▒▒▒|···· (12m stock)

Setup Development Environment

1. This project uses uv for fast Python package management. If you don't have uv installed, you can install it with: Install uv:

   If you have curl installed, you can run:

   curl -LsSf https://astral.sh/uv/install.sh | sh
   

2. Clone the repository:

   git clone <repository-url>
   cd cutting-stock
   

3. You can run commands directly with uv without activating the environment or manually installing dependencies:

   uv run cutting-stock
   

Development

Running Tests

uv run pytest

Code Formatting and Linting

uv run ruff check src/

Dependencies

• scipy: Scientific computing library
• numpy: Numerical computing library
• cvxpy: Convex optimization library
• cvxopt: Convex optimization library

License

MIT License