simpy

combined

Process-based discrete-event simulation framework in Python. Use this skill when building simulations of systems with processes, queues, resources, and time-based events such as manufacturing systems, service operations, network traffic, logistics, or any system where entities interact with shared resources over time. Builds, inspects, tests, and analyzes bounded process-based discrete-event simulations with SimPy. Use for event scheduling, resource queues, interrupts, monitoring, independent replications, warm-up, and reproducible output analysis.

When to use

**Modeling discrete-event systems** - Systems where events occur at irregular intervals
**Resource contention** - Entities compete for limited resources (servers, machines, staff)
**Queue analysis** - Studying waiting lines, service times, and throughput
**Process optimization** - Analyzing manufacturing, logistics, or service processes
**Network simulation** - Packet routing, bandwidth allocation, latency analysis
**Capacity planning** - Determining optimal resource levels for desired performance
**System validation** - Testing system behavior before implementation
Continuous simulations with fixed time steps (consider SciPy ODE solvers)

When not to use

Continuous simulations with fixed time steps
Independent processes without resource sharing
Pure mathematical optimization

Skill page

Conflict

The K-Dense-AI copy is a distinct, more rigorous methodology skill (version 1.6) that adds installation pinning, bounded execution guards, RNG separation, and validation steps not present in the others. The other three copies (zLanqing, foryourhealth111, FreedomIntelligence) are near-identical reproductions of a simpler API reference, with foryourhealth111 adding a routing boundary. Because the K-Dense copy contains material steps and constraints the others lack, and the others contain a routing boundary the K-Dense copy lacks, crowning one would hide a real alternative.