simpy

Model complex discrete-event simulations in Python using process generators to define workflows and manage resource contention. It handles environment management, event scheduling, and queuing logic for manufacturing, healthcare, network systems, and logistics operations.

Discrete-event simulation models how entities interact with shared resources over time using event-driven scheduling. You need it when analyzing systems where items queue for limited resources, such as service operations, network systems, or manufacturing lines.

Simulate priority queuing and resource contention in Python using PriorityResource and PreemptiveResource types. These resource variants manage realistic contention by prioritizing or preempting processes waiting for shared facilities.

Run real-time simulations with hardware-in-the-loop using the Real-time Environment feature. It synchronizes the simulation clock with wall-clock time, enabling interactive demos and hardware integration testing within your Python process model.

Monitor utilization and queue lengths during a Python simulation using built-in monitoring and data collection hooks. These utilities observe performance metrics and track resource usage throughout the event scheduling lifecycle.

Python discrete-event simulation supports Store, PriorityStore, and Container resource types alongside standard Resource variants. These model bulk resource storage and item sorting, expanding contention modeling for logistics and manufacturing scenarios.