The following is an implementation of the machine shop example from the Simpy documention using Ciw. Parts of this implementation came from this post.
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import ciw
import pandas as pd
class CiwMachineShop:
"""
A class to simulate a machine shop using Ciw.
This class models a machine shop with a set of machines that can break
and be repaired. It simulates the production of parts over a specified
simulation time.
Parameters:
- mfft (float): Mean time between machine failures (default 300.0).
- repair_time (float): Time required to repair a machine (default 30.0).
- job_duration (float): Default duration of a single job (default 30.0).
- num_machines (int): Number of machines in the shop (default 10).
Attributes:
- mfft (float): Mean time between machine failures.
- break_mean (float): Mean time between machine failures in rate form.
- repair_time (float): Time required to repair a machine.
- network (ciw.network.Network): Ciw network configuration for simulation.
Methods:
- run(sim_time=4*7*24*60):
Simulate the machine shop and return parts produced.
Args:
- sim_time (int): Total simulation time in minutes (default 4 weeks).
Returns:
- pandas.Series: A Series containing the number of parts produced for each job.
- sample_parts_made(uptime, part_mean=10.0, part_sigma=2.0):
Simulate parts production over a given uptime using a normal distribution.
Args:
- uptime (float): The duration for which parts production is simulated.
- part_mean (float): Mean time to produce a part (default 10.0).
- part_sigma (float): Standard deviation of time to produce a part (default 2.0).
Returns:
- int: The number of parts produced during the specified uptime.
"""
def __init__(self, mfft=300.0, repair_time=30.0, job_duration=30.0, num_machines=10):
"""
Initialize the CiwMachineShop.
Args:
mfft (float): Mean time between machine failures (default 300.0).
repair_time (float): Time required to repair a machine (default 30.0).
job_duration (float): Default duration of a single job (default 30.0).
num_machines (int): Number of machines in the shop (default 10).
"""
self.mfft = mfft
self.break_mean = 1 / mfft
self.repair_time = repair_time
self.network = ciw.create_network(
arrival_distributions=[ciw.dists.Sequential([0, float("inf")]), None],
service_distributions=[
ciw.dists.Exponential(rate=self.break_mean),
ciw.dists.Deterministic(value=repair_time),
],
routing=[[0, 1], [1, 0]],
batching_distributions=[ciw.dists.Deterministic(num_machines), None],
number_of_servers=[float("inf"), 1],
)
def run(self, sim_time=4*7*24*60):
"""
Run the machine shop simulation and return the parts produced.
Args:
sim_time (int): Total simulation time in minutes (default 4 weeks).
Returns:
pandas.Series: A Series containing the number of parts produced for each job.
"""
sim = ciw.Simulation(self.network)
sim.simulate_until_max_time(sim_time)
recs = sim.get_all_records()
df = pd.DataFrame(recs)
parts = (
df[df["node"] == 1]
.groupby(by="id_number")["service_time"]
.sum()
.apply(self.sample_parts_made)
)
return parts
def sample_parts_made(self, uptime, part_mean=10.0, part_sigma=2.0):
"""
Simulate the production of parts over a given uptime.
Args:
uptime (float): The duration for which parts production is simulated.
part_mean (float): Mean time to produce a part (default 10.0).
part_sigma (float): Standard deviation of time to produce a part (default 2.0).
Returns:
int: The number of parts produced during the specified uptime.
"""
count = 0
parts_time = 0
while True:
parts_time += ciw.dists.Normal(part_mean, part_sigma).sample()
if parts_time >= uptime:
return count
else:
count += 1
When we use the run method on an instance of the CiwMachineShop we obtain similar counts as the SimPy machine shop.
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>>> CiwMachineShop().run()
id_number
1 3304
2 3208
3 3317
4 3276
5 3277
6 3376
7 3267
8 3254
9 3356
10 3262
Name: service_time, dtype: int64