simpy

Use SimPy to define processes as Python generator functions that yield events such as env.timeout(). Create an Environment, register processes with env.process(), and run with env.run(until=time) to execute the simulation.

Resource models limited-capacity servers like machines, Container holds homogeneous bulk amounts like fuel with put and get by quantity, and Store queues discrete Python objects with FIFO, priority, or filtered retrieval variants.

Processes are themselves events, so yield the process object returned by env.process() to wait for its completion. Combine multiple processes with the & operator for all-of or | for any-of semantics.

Yes, simpy.rt.RealtimeEnvironment synchronizes simulation time with wall-clock time using a factor parameter, where factor=1.0 maps one simulation unit to one second. Use strict=False to tolerate computations that exceed the time budget.

Common causes include forgetting to yield an event, reusing an already-triggered event since events fire only once, or deadlocks where no process can acquire a resource. Ensure every pause yields a fresh event and resources are released.

Avoid SimPy for continuous-time systems requiring fixed-step integration, where SciPy ODE solvers fit better, and for pure mathematical optimization, where SciPy optimize is appropriate. SimPy targets discrete events with resource contention.