🐄 Bowman Farm Optimisation Model (2025–2026)

🔍 Project Overview

This project presents a stochastic integer linear programming (ILP) optimisation model developed in Python (PuLP) to help a farm (the Bowman family) make optimal operational decisions. The goal is to maximize expected farm wealth over a one-year planning horizon, under uncertain weather-dependent crop yields and labour requirements.

Key decisions include:

• Livestock purchases and sales (cows & hens)
• Crop allocation (corn, wheat, rapeseed)
• Feed requirements and crop storage
• Financial investment for interest gains

The model helps allocate limited land, labour, and financial resources for profit maximization, while accounting for risk and resource constraints.

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💡 Key Findings

• The optimal strategy recommends investing heavily in hens and rapeseed cultivation, with minimal cow investment.
• 1600 hens are purchased while 400 are sold, maximizing income under uncertain weather.
• 778 acres are planted with rapeseed, leveraging its high profitability.
• A total expected wealth of £1,288,507.28 is achieved under the most likely weather scenario (Frost & Dry).
• The model prioritizes feed sufficiency and financial sustainability through strategic crop storage and investment.

🧰 Tech Stack & Tools

• Language: Python 3.x
• Library: PuLP for optimisation
• IDE: Jupyter Notebook / VS Code
• Visuals: Matplotlib, Seaborn (optional for plotting)
• GenAI: ChatGPT for model formulation, code structure, and documentation support

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📦 Repository Structure

bowman-farm-optimisation/
1. models/              # Python scripts for ILP model (PuLP)
2. analysis report/            # Parametric analysis results and graphs
3. README.md            # Project documentation (this file)

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📊 Parametric Analysis

To test model robustness, cow purchase prices and income levels were varied. Key insights:

• Even with increased cow income, hens remained the preferred livestock due to higher return on investment.
• Model results remained stable across most price ranges, showing strong decision robustness.
• GenAI helped structure this analysis using nested loops and clear visual strategies (e.g., 2D heatmaps).

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📌 Conclusion

This project showcases real-world application of:

• Operations Research
• Mathematical Optimisation
• Decision Support Systems
• Data-Driven Agricultural Planning

It reflects strong analytical thinking, programming, and the ability to integrate advanced tools (like GenAI) into practical problem-solving workflows.