LAMOSCAD Optimization Project ✈️⚙️

This repository contains optimization models and algorithms developed for the LAMOSCAD (Location Allocation Modeling to Optimize Spill Coverage and Cost in the Canadian Arctic) project.

The models are designed to solve complex facility location, resource allocation, and emergency response optimization problems using tools such as Gurobi, Python, and Geospatial Data.

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📂 Project Structure

MIP-Python-Lamoscad/
│
├── data/                            # Input datasets (e.g., oil spill, station info)
│
├── results/                         # Output results
│   ├── artifacts/                   # Saved output files or model artifacts
│   └── plots/                       # Excel and figure output files (e.g., .xlsx, .png)
│
├── scripts/                         # Experiment scripts for running models and analyses
│   ├── generate_data.py             # Preprocessing script
│   ├── obtain_pcp_data_s4.3.2.py
│   ├── perform_sensitivity_analysis_s4.3.py
│   ├── run_computational_findings_s4.2.py
│   ├── run_lamoscad_mclp_s4.2.3.py
│   └── run_milp_BnC_s4.2.6.py
│
├── src/                             # Core source code
│   ├── config/                      # Configuration and YAML loaders
│   ├── models/                      # Mathematical model definitions
│   ├── preprocessing/               # Data loading and preprocessing utils
│   ├── solvers/                     # Custom solver logic (e.g., branch and cut)
│   ├── utils/                       # General utilities
│   └── visualization/               # Drawing maps, routes, and networks
│
├── requirement.txt

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📊 Optimization Techniques Used

• Mixed Integer Linear Programming (MILP)
• Branch-and-Cut Algorithms
• Large-Scale Optimization with Gurobi
• Sensitivity Analysis
• Geospatial Optimization

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🚀 Key Features

• Gurobi-powered mathematical programming
• Modular & reusable codebase (easy to extend)
• Configurable parameters via YAML files
• Automated result saving & reporting
• Ready-to-run Jupyter notebooks & scripts

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⚙️ Technologies

• Python 🐍
• Gurobi Optimizer
• Pandas, GeoPandas, Shapely, Matplotlib
• YAML Configurations
• Jupyter Notebooks

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📥 Installation

1. Clone this repository:

git clone https://github.com/your_username/lamoscad-optimization-project.git
cd lamoscad-optimization-project

2. Install dependencies:

pip install -r requirements.txt

3. Set up Gurobi license (if required).

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📄 How to Run

• Scripts: Execute optimization models from scripts/:

python scripts/perform_sensitivity_analysis_s4.3.py

• Configurations: Adjust model parameters inside src/config/model_config.yaml.

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📚 References

This project is based on research in operations research and optimization for maritime emergency response systems.

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📢 Acknowledgements

• Gurobi Academic License
• Python Open Source Libraries
• MARS team at Dalhousie University

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📧 Contact

For questions or collaborations, please contact: [Tanmoy Das] — Operations Research Scientist
GitHub: tanmoyie

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Note: This repo is intended for academic and research purposes only.