simpy

Discrete-event simulation models how entities move through time while competing for shared resources. You need it to study performance metrics like waiting, utilization, and throughput in queueing systems without manually running every scenario.

You build a process-based simulation by defining Python generator processes that yield timeouts and resource requests. Coordinate execution using event scheduling to model entities competing for limited capacity resources over simulated time.

Yes, you can model resource contention in logistics workflows using PriorityResource and PreemptiveResource constructs. These allow you to simulate competing entities requesting shared capacity based on priority levels and preemption rules.

Python supports event scheduling for network traffic modeling through generator-based processes. You can simulate packets competing for bandwidth by yielding timeout events and requesting shared resources to analyze throughput and utilization.

The best way to collect waiting and utilization metrics is by monitoring event coordination during execution. Synchronize workflows with timeouts and events to capture queue lengths, wait times, and resource utilization as the simulation progresses.

Using generator processes for time-driven event simulation requires manual coordination of yields for timeouts and resource requests. Complex interactions between competing entities can become difficult to debug if event synchronization logic is not carefully managed.