DisruptSC¶

Version 1.1.7

DisruptSC is a spatial agent-based model for simulating supply chain disruptions. It models economic agents (firms, households, countries) connected through transport networks and supply chains to analyze the impact of disruptions on economic systems.

Key Features¶

🌍 Spatial Modeling: Agents are located on transport networks with realistic geographic constraints
🏭 Multi-Agent System: Firms, households, and countries with distinct behaviors and interactions
🚛 Transport Networks: Multiple transport modes (roads, maritime, railways, airways, pipelines)
💼 Economic Foundations: Based on Multi-Regional Input-Output (MRIO) tables
⚡ Disruption Analysis: Model transport disruptions and capital destruction events
📊 Rich Outputs: Detailed economic and spatial results for policy analysis

Quick Start¶

New to DisruptSC?

Start with our Installation Guide and then try the Quick Start Tutorial.

# Clone repo
git clone 

# Install dependencies
conda env create -f dsc-environment.yml
conda activate dsc

# Set up data (choose one option)
cd disrupt-sc
mkdir input  # Option 1: create your set of input data
git submodule add <data-repo-url> data     # Option 2: Git disrupt-sc-data submodule (invitation-only)

# Validate inputs
python validate_inputs.py Testkistan

# Run a simulation
python disruptsc/main.py Testkistan

Use Cases¶

🏛️ Policy Analysis: Assess economic impacts of infrastructure disruptions for policy planning
🌪️ Disaster Response: Model supply chain vulnerabilities during natural disasters
🚧 Infrastructure Planning: Evaluate critical transport links and redundancy needs

Model Workflow¶

graph TD
    A[Setup Transport Network] --> B[Create Agents]
    B --> C[Build Supply Chain Network]
    C --> D[Optimize Logistic Routes]
    D --> E[Initialize Economic Variables]
    E --> F[Run Baseline Simulation]
    F --> G[Apply Disruptions]
    G --> H[Analyze Results]

Architecture Overview¶

DisruptSC uses a modular architecture with clear separation of concerns:

Agents: Economic actors with spatial locations and behaviors
Networks: Transport infrastructure and supply chain relationships
Disruptions: Events that affect agent capabilities or network availability
Simulation: Time-stepped execution with data collection

Getting Help¶

📖 Documentation: Comprehensive guides and API reference in this documentation
🐛 Issues: Report bugs and request features on GitHub Issues
💬 Discussions: Contact the lead author directly

Citation¶

If you use DisruptSC in your research, please cite:

📚 APA Style¶

Colon, C., Hallegatte, S., & Rozenberg, J. (2021). Criticality analysis of a country’s transport network via an agent-based supply chain model. Nature Sustainability, 4(3), 209-215.

🔖 BibTeX¶

@article{colon2021disruptsc,
  author  = {Celian Colon and Stephane Hallegatte and Julie Rozenberg},
  title   = {Criticality analysis of a country’s transport network via an agent-based supply chain model},
  journal = {Nature Sustainability},
  volume  = {4},
  pages   = {209--215},
  year    = {2021},
  doi     = {10.1038/s41893-020-00649-4},
  url     = {https://www.nature.com/articles/s41893-020-00649-4}
}

@software{disruptsc2025,
  title={DisruptSC: Spatial Agent-Based Model for Supply Chain Disruption Analysis},
  author={Celian Colon},
  year={2025},
  url={https://github.com/ccolon/disrupt-sc}
}

License¶

DisruptSC is released under the MIT License.