SimPy Discrete-Event Simulation

Workflow

1. Define system boundary, entities, resources, queues, events, time units, warm-up, and output metrics.
2. Specify stochastic assumptions and distributions separately from process logic.
3. Implement small composable processes, resource requests, and monitors.
4. Run multiple replications with controlled random seeds and collect summary statistics.
5. Validate event traces against hand-calculated toy cases before scaling.
6. Run sensitivity/scenario analysis and report uncertainty across replications.

References

• Read references/simulation-workflow.md for model structure, validation, and reporting checklists.
• Read references/legacy-full-skill.md for older SimPy patterns and examples.

Validation

• Time units and resource capacities are explicit.
• A small deterministic or hand-checkable scenario passes before full simulation.
• Reported results include replication variability, not a single run only.