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.
- 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.
- 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.
- Pandas: For data handling.
- PuLP: For optimization.
- Excel Sheets: Contain rates, orders, and capacities.
- Python Scripts: Implement the optimization models.
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Install Dependencies:
pip install pandas pulp
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Prepare Data: Place your Excel sheets in the project directory. Ensure they are named correctly as referenced in the scripts.
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Run the Scripts: Execute the relevant Python scripts to perform the optimization. The results will be outputted as specified in the scripts.
This project is licensed under the MIT License. See the LICENSE file for details.