This repository presents two hybrid Genetic Algorithms to solve a combination of production scheduling and bin packing problems in the printing industry. To learn about the problem and the algorithms, refer to the following paper

M.Mostajabdaveh, S.Salman, N.Tahmasbi, Two dimensional guillotine cutting stock and scheduling problem in printing industry, Computers & Operations Research, 2022

The raw dataset from an actual printing company is available at Mendely Data .

From this dataset, we generated 335 problem instances which can be found in Data/Input directory.

The input files are binary files generated by pickle python package storing an object of Input class (please see the Input class in Input.py file)

You can generate more problem instances using Raw Data/Big_Data.xlsx in Mendely Data and the Data/Data_Gen.py .

You can use either RGA or UGA to solve the problem. Generally, RGA is faster, and UGA provides a better solution. Refer to the paper for a detailed explanation and comparison.

To use the algorithm, you can simply run the following command

python3 runner.py <RGA,UGA> <inputFile> --rep <int> --draw

• First input is the type of algorithm which should be either RGA or UGA
• Second input is the input file name located at Data/Input
• The optional input --rep <int> is the number of times we run the algorithm on the input data (default value is 1). In the case of rep>=2, the output result is the average of the best solution costs and runtime.
• The optional flag --draw, if given, the algorithm will print detailed information about the best solution and draw the layout of the bin.

The algorithm parameters are tuned using a subset of generated instances. However, if you wish to change their values, refer to the Parameters class (line 15 runner.py file).

Contributions make the open source community an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion to improve this, please fork the repo and create a pull request. You can also open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

It is distributed under the GPL-3.0 License. See LICENSE for more information.

Mahdi Mostajabdaveh - [email protected]

Project Link: github.com/mahdims/Bin_packing_scheduling