Created by: Davide Carecci
Initial commit: 02.09.2025
BIOGoAlS.TE_LP was developed following the Robust Optimization PhD course at Politecnico di Milano, taught by Professor Erick Delage.
The tool implements a robust linear programming (LP) optimization framework for diet or feed composition problems inspired by supply-chain formulations.
- See
/Project/RobustOptimization_Project_Proposal.pdffor a conceptual introduction. - See
/Project/RobustOptimization_Project_Report.pdffor rigorous mathematical formulations and interpretation of the results for the example case study. - See
/BIOGOALS.TE-LP_USER_MANUAL.pdffor a step-by-step guide through the files and the main Jupyter notebook/Project/RO_project.ipynb. - See
MATH80624_LectureNotes.pdffor detailed mathematical background.
Note: The /80624 folder is deprecated.
-
Jupyter Notebook:
Main implementation of the BIOGoAlS.TE_LP tool, performing robust linear programming optimization for diet formulation (supply-chain-like problem).
Mosek license recommended.
If Mosek is not available, install GUROBI or another open-source solver, and modify the notebook (/Project/RO_project.ipynb) accordingly.
The default solver isscipy.optimize. -
Supplementary Python functions:
Supporting scripts required for the Jupyter Notebook to execute correctly. -
Excel input file:
Contains input data required by the Jupyter Notebook. -
Excel output file:
Stores optimization results generated by the Jupyter Notebook. -
PDF documents:
Provide an introduction and documentation of the example implementation and its corresponding results.
- Markdown links (
./folder/file) are used for proper GitHub rendering. - The repository assumes a Python 3.10+ environment with
numpy,pandas, and a linear programming solver (Mosek, GUROBI, or open-source alternative e.g.scipy).
© 2025 Davide Carecci — All rights reserved.