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This repository, created for an Operations Research module, contains scripts employing the Branch-and-Bound algorithm for linear optimization. Utilizing SciPy and Xpress libraries, the scripts solve and visually illustrate the optimization process, aiding in the understanding of the algorithm's application in Operations Research.

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Branch-and-Bound Algorithm

Description

This repository contains two Python scripts implementing the Branch-and-Bound algorithm to solve linear optimization problems. The two scripts utilize different libraries to solve the optimization problems:

  1. branch-and-bound-scipy.py: This script uses the scipy.optimize.linprog function from the SciPy library.
  2. branch-and-bound-xpress.py: This script uses the xpress library.

Both scripts also include functionalities for visualizing the solution process using the networkx and matplotlib libraries.

Prerequisites

  • Python 3.x
  • SciPy
  • Matplotlib
  • NetworkX
  • Xpress (only for branch-and-bound-xpress.py)

Installing Prerequisites

You can install the necessary packages using pip:

pip install scipy matplotlib networkx xpress

Execution of Scripts

You can execute each script from the command line:

python3 branch-and-bound-scipy.py

or

python3 branch-and-bound-xpress.py

Visualization

The scripts also generate a visualization of the Branch-and-Bound process, showing the search tree and the solutions found.

About

This repository, created for an Operations Research module, contains scripts employing the Branch-and-Bound algorithm for linear optimization. Utilizing SciPy and Xpress libraries, the scripts solve and visually illustrate the optimization process, aiding in the understanding of the algorithm's application in Operations Research.

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