Tutorial 2: Adding Randomness and Rules

In this tutorial, you will expand the factory model by adding stochasticity (random service times) and testing dynamic capacity updates.

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1. Introducing Randomness

In real-world factories, machine processing times are never constant. We can register a statistical distribution to represent milling/molding tasks.

Update your SimulationContext configuration to: 1. Define a random seed (random_seed=42) to guarantee that all random samplings are fully reproducible. 2. Add a milling service using a normal distribution (mean of 3.0 seconds, standard deviation of 0.5 seconds).

from dynamic_des import SimulationContext, ConsoleEgress

app = (
    SimulationContext(sim_id="Factory_A", factor=1.0, random_seed=42)
    .add_resource("lathe", current_cap=1, max_cap=5)
    .add_arrival("parts", dist="exponential", rate=0.5)
    .add_service("milling", dist="normal", mean=3.0, std=0.5)
    .add_egress(ConsoleEgress())
)

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2. Setting Up Dynamic Rules

We can simulate an external control system (like an operator logging a machine online) by scheduling a capacity change using LocalIngress.

Let's configure the ingress schedule to: * Start with 1 lathe. * Increase capacity to 3 at t=10.0 seconds. * Drop capacity to 2 at t=20.0 seconds.

from dynamic_des import LocalIngress

ingress = LocalIngress(schedule=[
    (10.0, "Factory_A.resources.lathe.current_cap", 3),
    (20.0, "Factory_A.resources.lathe.current_cap", 2)
])

app.add_ingress(ingress)

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3. Decorating Tasks with Distributions

Now, update the @app.task decorator to point to the milling service distribution. The framework will automatically sample from the distribution to dictate the duration of each task:

@app.task(service_id="milling", resource_id="lathe")
def process_part(part_id: int):
    # The timeout delay is now automatically sampled from the 'milling' service config!
    return {"part_id": part_id}

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4. Run and Observe

if __name__ == "__main__":
    app.run(until=25.0)

When you execute the script, you will notice: * Stochastic timings: Each task takes a slightly different amount of time to complete. * Queuing: In the first 10 seconds, parts pile up because the arrival rate (0.5 parts/sec) exceeds the machine capacity/duration. * Capacity Increase: At t=10s, capacity increases to 3, causing the queue to clear instantly. * Seeded determinism: Because you pinned random_seed=42, re-running this script will produce the exact same timestamps and random samples every time.