I am new to simulation and am going through the simpy documentation. I kind of get the gist of it but cannot really seem to grasp how to translate the production line that I want to simulate.
I am trying to simulate a production line with m number of machines and m-1 number of buffers.
The machines basically work the same:
- Processes units at speed s per state
- random distribution of states and time in said state
- Is there starvation/blockage from the buffers?
The buffers all work the same:
- Receives units from previous machine
- Loses units from next machine
- capacity(t) = capacity(t-1) + (input previous machine) – (output next machine)
- Capacity(t) <= Max capacity
Now I get that the first steps of simulation are baby steps so I need to make a simple model first. What I am looking to create then using simpy is a 2 machine and 1 buffer system with fixed processing speeds, failure rate and maintenance speeds:
- An unlimited capacity of units is delivered to machine 1.
- Machine 1 processes at speed s, fails after f minutes and is repaired after r minutes. If buffer is full then Machine 1 stops.
- Buffer gets filled by Machine 1 and emptied by Machine 2. There is a max capacity.
- Machine 2 processes at min(speed s, capacity buffer) fails after f minutes and is repaired after r minutes. If buffer is empty then Machine 2 stops.
- Unlimited buffer capacity after Machine 2.
EDIT: The answer I received from @Michael works very well, I tried playing around with failures and maintenance. The machine seems to fail and repaired, but keeps failing at multiples of the time to failure (which I need to fix). The code I am using is as follows:
# Machine 1
speed_1 = 2 # Avg. processing time of Machine 1 in minutes
# speed_1_stdev = 0.6 # St. dev. of processing time of Machine 1
MTTF_1 = 10 # Mean time to failure Machine 1
# fail_1 = 1/MTTF_1 # Parameter for exp. distribution
repair_1 = 3 # Time it takes to repair Machine 1
# Machine 2
speed_2 = 3 # Processing time of Machine 2 in minutes
# speed_2_stdev = 0.6 # St. dev. of processing time of Machine 2
MTTF_2 = 7 # Mean time to failure Machine 1
# fail_2 = 1/MTTF_2 # Parameter for exp. distribution
repair_2 = 4 # Time it takes to repair Machine 2
# Simulation time
time = 120 # Sim time in minutes
#---------------------------------------------------------------------
# Class setup for a Machine
class Machine(object):
"""
A machine produces units at a fixed processing speed,
takes units from a store before and puts units into a store after.
Machine has a *name*, a processing speed *speed*, a preceeding buffer *in_q*,
and a proceeding buffer *out_q*.
Next steps:
- Machine produces units at distributed processing speeds.
- A machine fails at fixed intervals and is repaired at a fixed time.
- Failure and repair times are distributed.
"""
def __init__(self, env, name, in_q, out_q, speed, mttf, repair):
self.env = env
self.name = name
self.in_q = in_q
self.out_q = out_q
self.speed = speed
self.mttf = mttf
self.repair = repair
self.broken = False
# Start the producing process
self.process = env.process(self.produce())
# Start the failure process
env.process(self.fail_machine())
def produce(self):
"""
Produce parts as long as the simulation runs.
"""
while True:
part = yield self.in_q.get()
try:
# If want to see time {self.env.now:.2f}
print(f'{self.name} has got a part')
yield env.timeout(self.speed)
if len(self.out_q.items) < self.out_q.capacity:
print(f'{self.name} finish a part next buffer has {len(self.out_q.items)} and capacity of {self.out_q.capacity}')
else:
print(f'{self.env.now:.2f} {self.name} output buffer full!!!')
yield self.out_q.put(part)
print(f'{self.name} pushed part to next buffer')
except simpy.Interrupt:
self.broken = True
yield self.env.timeout(self.repair)
print(f'{self.env.now:.2f} {self.name} is in fixed')
self.broken = False
def fail_machine(self):
"""
The machine is prone to break down every now and then.
"""
while True:
yield self.env.timeout(self.mttf)
print(f'{self.env.now:.2f} {self.name} is in failure.')
if not self.broken:
# Machine only fails if currently working.
self.process.interrupt(self.mttf)
#---------------------------------------------------------------------
# Generating the arrival of parts in the entry buffer to be used by machine 1
def gen_arrivals(env, entry_buffer):
"""
Start the process for each part by putting
the part in the starting buffer
"""
while True:
yield env.timeout(random.uniform(0,0.001))
# print(f'{env.now:.2f} part has arrived')
part = object() # Too lazy to make a real part class, also isn't necessary
yield entry_buffer.put(part)
#---------------------------------------------------------------------
# Create environment and start the setup process
env = simpy.Environment()
bufferStart = simpy.Store(env) # Buffer with unlimited capacity
buffer1 = simpy.Store(env, capacity = 8) # Buffer between machines with limited capacity
bufferEnd = simpy.Store(env) # Last buffer with unlimited capacity
# The machines __init__ starts the machine process so no env.process() is needed here
machine_1 = Machine(env, 'Machine 1', bufferStart, buffer1, speed_1, MTTF_1, repair_1)
machine_2 = Machine(env, 'Machine 2', buffer1, bufferEnd, speed_2, MTTF_2, repair_2)
env.process(gen_arrivals(env, bufferStart))
# Execute
env.run(until = time)
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Answer
Got bored of my work, so I made some small changes to fix your code. I think it even works. One final note, more then one machine can use the same buffer. so a pool of machines can process out of the same one buffer. This is what I do instead of using a simpy.resurce for a resource pool
import simpy
import random
speed_1 = 3 # Avg. processing time of Machine 1 in minutes
speed_2 = 4 # Processing time of Machine 2 in minutes
time = 120 # Sim time in minutes
# Class setup for Machine 1
# This needs to be done as there are varying processing speeds
class Machine(object):
"""
A machine produces units at a fixed processing speed,
takes units from a store before and puts units into a store after.
Machine has a *name*
Next steps:
- Machine produces units at distributed processing speeds.
- A machine fails at fixed intervals and is repaired at a fixed time.
- Failure and repair times are distributed.
"""
def __init__(self, env, name, in_q, out_q, speed):
self.env = env
self.name = name
self.in_q = in_q
self.out_q = out_q
self.speed = speed
# Start the producing process
self.process = env.process(self.produce())
def produce(self):
"""
Produce parts as long as the simulation runs.
"""
while True:
part = yield self.in_q.get()
print(f'{self.env.now:.2f} Machine {self.name} has got a part')
yield env.timeout(self.speed)
print(f'{self.env.now:.2f} Machine {self.name} finish a part next q has {len(self.out_q.items)} and capacit of {self.out_q.capacity}')
yield self.out_q.put(part)
print(f'{self.env.now:.2f} Machine {self.name} pushed part to next buffer')
def gen_arrivals(env, entry_buffer):
"""
start the process for each part by putting
the part in the starting buffer
"""
while True:
yield env.timeout(random.uniform(1,4))
print(f'{env.now:.2f} part has arrived')
part = object() # too lazy to make a real part class
yield entry_buffer.put(part)
# Create environment and start the setup process
env = simpy.Environment()
bufferStart = simpy.Store(env) # Buffer with unlimited capacity
buffer1 = simpy.Store(env, capacity = 6) # Buffer between machines with limited capacity
bufferEnd = simpy.Store(env) # Last buffer with unlimited capacity
# the machines __init__ starts the machine process so no env.process() is needed here
machine_1 = Machine(env, 'Machine 1', bufferStart, buffer1, speed_1)
machine_2 = Machine(env, 'Machine 2', buffer1, bufferEnd, speed_2)
#machine_3 = Machine(env, 'Machine 3', buffer1, bufferEnd, speed_2)
env.process(gen_arrivals(env, bufferStart))
# Execute
env.run(until = time)