MCM 2026 Problem B - Moon Colony Logistics Strategy

This repository contains our complete solution for the 2026 Mathematical Contest in Modeling (MCM) Problem B. Our work focuses on designing an optimal, resilient, and sustainable logistics system for establishing a long-term moon colony.

🚀 Project Overview

We propose the R2-WSM (Resilient & Reusable Water-Silicon-Moon) System, a hybrid logistics strategy that transitions from traditional rocket transport to a Space Elevator-based infrastructure. Our solution addresses four key challenges:

1. Transport Strategy (Task 1): Optimization of a hybrid Rocket-Elevator transport mix over a 43-year horizon (2050-2093).
2. Resilience Analysis (Task 2): Comparative robustness testing against extreme disruptions (e.g., Kessler Syndrome, Solar Storms).
3. Implementation Roadmap (Task 3): A detailed, high-density Gantt chart and critical path analysis for system deployment.
4. Sustainability Assessment (Task 4): Environmental impact analysis (GWP vs. SRI) and policy sensitivity modeling.

📂 Project Structure

MCM_MoonColony/
├── src/                    # Python source code for models and visualizations
│   ├── task1_*.py          # Task 1: Transport optimization & 3D Pareto charts
│   ├── task2_*.py          # Task 2: Resilience analysis & comprehensive comparisons
│   ├── task3_*.py          # Task 3: Timeline roadmaps & sensitivity analysis
│   ├── task4_*.py          # Task 4: Environmental tradeoffs & phase space plots
│   └── ...
├── images/                 # Publication-quality figures (High DPI PNG/PDF)
│   ├── task1_timeline_roadmap_v2.png    # 43-Year Strategy Roadmap
│   ├── task2_comprehensive_comparison_v4.png # R2-WSM vs. Baselines
│   ├── task3_implementation_timeline_v3.png  # Detailed Deployment Gantt
│   ├── task1_sensitivity_dual_axis_v3.png    # Dual-Axis Sensitivity (Time/Cost)
│   └── ...
├── docs/                   # Technical documentation and reports
│   ├── FINAL_VISUALIZATION_REPORT.md
│   └── ...
├── output/                 # Intermediate data outputs and simulation logs
└── README.md               # Project documentation

🛠️ Key Models & Scripts

Task 1: Transport Optimization

• src/task1_dynamic_model.py: Core dynamic simulation engine for resource allocation.
• src/task1_3d_pareto_v2.py: 3D Visualization of Cost-Duration-NPV trade-offs.
• src/task1_timeline_roadmap_v2.py: Generation of the master strategy roadmap.

Task 2: Resilience & Comparison

• src/task2_run_full.py: Runs the full resilience Monte Carlo simulation.
• src/task2_comprehensive_comparison_v4.py: Generates the "Decision Matrix" dashboard.
• src/task2_risk_concept_diagram_v2.py: Visualizes risk hedging strategies.

Task 3: Implementation Planning

• src/task3_implementation_timeline_v3.py: Creates the high-density Gantt chart (Master Schedule).
• src/task3_sensitivity_standalone.py: Sensitivity analysis for critical path delays.

Task 4: Sustainability

• src/task4_environmental_assessment.py: Calculates GWP (Global Warming Potential) and SRI (Space Responsibility Index).
• src/task4_advanced_sensitivity.py: Generates Environmental Phase Space plots.

📦 Requirements

pip install numpy scipy matplotlib pandas seaborn networkx

📊 Visualizations

The images/ folder contains high-resolution charts ready for academic publication. Key highlights include:

• Figure 5.7: R2-WSM Implementation Master Schedule (task3_implementation_timeline_v3.png)
• Figure 6.1: Technology Sensitivity Analysis (task1_sensitivity_dual_axis_v3.png)
• Figure 2.x: Comprehensive Strategy Comparison (task2_comprehensive_comparison_v4.png)

👥 Team

MCM 2026 Team - Problem B Solution developed for the Mathematical Contest in Modeling.

📄 License

This project is open-source for academic research and educational purposes.