SimPy Discrete-Event Simulation
Workflow
- Define system boundary, entities, resources, queues, events, time units, warm-up, and output metrics.
- Specify stochastic assumptions and distributions separately from process logic.
- Implement small composable processes, resource requests, and monitors.
- Run multiple replications with controlled random seeds and collect summary statistics.
- Validate event traces against hand-calculated toy cases before scaling.
- Run sensitivity/scenario analysis and report uncertainty across replications.
References
- Read
references/simulation-workflow.mdfor model structure, validation, and reporting checklists. - Read
references/legacy-full-skill.mdfor 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.