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Process-based discrete-event simulation framework in Python.
Passed 23 of 23 test cases with the skill, and 21 of 23 without it.
and 1 more in the full report
2 fixed21 passed either way0 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 2 test cases the skill fixed. Same model, same prompt, with and without the skill.
We are running stochastic simulations of a bank teller system with random customer arrival and service times using Python's `random` module. We need to ensure that every run of the simulation produces identical numeric wait-time results for testing. What essential best practice line should be executed before starting the simulation?
| Checked | Without skill | With skill |
|---|---|---|
| The script calls random.seed() before running the simulation. | Missed | Passed |
The instructions your model reads when the skill loads.
---------|----------| | Resource | Limited capacity (servers, machines) | | PriorityResource | Priority-based queuing | | PreemptiveResource | High-priority can interrupt low-priority | | Container | Bulk materials (fuel, water) | | Store | Python object storage (FIFO) | | FilterStore | Selective item retrieval | | PriorityStore | Priority-ordered items |
import simpy
env = simpy.Environment()
# Basic resource (e.g., servers)
resource = simpy.Resource(env, capacity=2)
# Priority resource
priority_resource = simpy.PriorityResource(env, capacity=1)
# Container (e.g., fuel tank)
fuel_tank = simpy.Container(env, capacity=100, init=50)
# Store (e.g., warehouse)
warehouse = simpy.Store(env, capacity=10)
import simpy
import random
def customer(env, name, server):
arrival = env.now
with server.request() as req:
test cases passed, vs 21 / 23 without
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