simpy

Build a discrete-event simulation by creating generator-based processes that yield events, coordinating shared resources via request and release patterns, and executing the environment with env.run to compute contention outcomes over time.

Model priority queues in a Python simulation using PriorityResource, and handle preemption with PreemptiveResource to manage task interruption and resource reallocation when higher-priority processes enter the queue.

Simulate a manufacturing line by generating parts as processes with arrival rates, requesting limited server capacity via Resource, and measuring waiting times and throughput under contention using environment execution.

Simulate bulk inventory and object flow through a pipeline using Container for material levels and Store, FilterStore, or PriorityStore to manage item movement and bulk resource tracking within the discrete-event environment.

Discrete-event simulation works for network traffic timing and capacity planning by scheduling packet events with env.timeout, modeling shared resource constraints, and computing throughput behavior over simulated time.

Capture wait times and utilization metrics during process modeling by instrumenting resource request and release events, logging processing durations, and tracking event timing throughout the environment execution cycle.