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Supply Chain Optimization

This repository contains Python code for optimizing supply chain distribution through linear programming. The objective is to minimize transportation costs from various origin ports to a destination port.

Versions

Normal Version

  • Objective: Minimize costs using average values.
  • Method: Reads and merges data from Excel sheets, calculates average costs and weights, and uses linear programming with supply and demand constraints.

Second Version

  • Objective: Minimize costs with balanced cost estimates.
  • Method: Uses midpoints between minimum and maximum values for cost estimates, filters and merges plant and warehouse data, and applies additional constraints for refined results.

Libraries Used

  • Pandas: For data handling.
  • PuLP: For optimization.

Files Included

  • Excel Sheets: Contain rates, orders, and capacities.
  • Python Scripts: Implement the optimization models.

How to Use

  1. Install Dependencies:

    pip install pandas pulp

  2. Prepare Data: Place your Excel sheets in the project directory. Ensure they are named correctly as referenced in the scripts.

  3. Run the Scripts: Execute the relevant Python scripts to perform the optimization. The results will be outputted as specified in the scripts.

License

This project is licensed under the MIT License. See the LICENSE file for details.