simpy

To run a discrete-event simulation in Python, create an Environment, define processes using generator functions, and call env.run to execute and observe system behavior. This models interacting entities and shared resources.

Discrete-event simulation models queueing systems by tracking entities moving through shared resources over time. It evaluates performance for manufacturing lines, service operations, telecommunications networks, and logistics where queues determine throughput.

Python discrete-event simulation supports priority and preemptive resource queues. You can use PriorityResource and PreemptiveResource classes alongside standard Resource, Container, and Store types to manage constrained shared resources with varying urgency levels.

Python discrete-event simulation provides real-time capabilities for network traffic modeling. The environment supports optional real-time synchronization, allowing simulated events to align with wall-clock time during execution for monitoring purposes.

The best way to model manufacturing lines is using process-based discrete-event simulation. Define generator-based processes for line activities and use Resource objects to manage shared machinery, enabling performance analysis of queues and bottlenecks.

You do not need custom solvers to analyze service operations with queues. Process-based discrete-event simulation enables modeling of entities, resources, and events to evaluate system behavior entirely through built-in generator functions and event handling.