Simulating a system of batching jobs with interrupting set-up/switch-on times using Simpy - interrupt

Question

I am new to Simpy and have a problem with combining batching jobs and interrupting set-up time. So could you please help me?
I would like to create a system with servers that need time to set up before being ready to serve.
The system starts to set up whenever enough M (2, 3,...) customers are in the queue. If the number of customers in the system reaches the maximum number of K(50), the coming customer will balk.
When a batch( group of M customers) leaves the system, we check if there are M customers(a batch) who are waiting to be served. If so, we keep the server remaining ON, otherwise, we turn off the server immediately.
I found some code for quite the same problem in a Simpy google group about Covid test simulation that uses Stores Resources and the answer for interrupting set-up time with Container Resources by Michael R. Gibbs
https://groups.google.com/g/python-simpy/c/iFYaDlL4fq0
Interrupt an earlier timeout event in Simpy
I tried to combine 2 codes but It didn't work.
Example, when M = 2, K = 50
Customer 1 arrives and waits
Customer 2 arrives, enough 2 customers then request a server.
Server 1 is SETUP in t1 secs.
Customer 3 arrives and waits
Customer 4 enough 2 customers then request a server.
Server 2 is SETUP in t1 secs.
Server 1 is ON.
Customers 1 and 2 occupied server 1
Customer 1 and 2 completes the service and leaves the system.
Customers 3 and 4 occupied server 1 (because when server 1 finishes
Server 2 is still in the setup process)
Server 2 (still in SETUP mode) is turned off...
... Customer 100 arrives and sees the system has 50 customers, then customer 100 balk

Answer 1

Broke customer arrivals into two parts. A first queue that where the customer waits until there is enough customers to make a batch. When I have enough customers to make a batch, I do so, popping the batched customers from the batching queue, and putting the batch in a processing queue. I count the customers in both queues to see if a arriving customer aborts entry.
When a batch is put in the processing queue, I also start up a server. This means that the number of batches in the processing queue will equal the number of servers starting up. This also means that when a server finishes starting up, there will be a batch to process. Since there will never be a wait for a batch, I use a simple list for my queue.
When a batch starts up, it grabs a batch, and removes itself from the list of starting servers. After the server finishes processing a batch, it checks if there is a batch in the processing queue. If so, grab the batch and keep processing, but also kill the server that is starting up to process the batch. If no batches in the processing queue, shut down.
Here is the code. You should see in the log the queues max out and customers abort, but also see servers start to shut down towards the end
"""
Simulation of servers processing batches
Customers enter a queue where they wait for
enough customers to make a batch
If the there are too many customers in the queues
the arriving customer will abort
When a batch is made, it is put into a second
processing queue where the batch waits to be processed.
When a batch is put into the processing queue, it
starts a server. The server has a start up delay
then loops by seizing a batch, process the batch, release
the batch, checking if another batch is in the
processing queue. If there is another batch, stop a server
that is starting up and process the batch, else end loop
and shutdown server
Programmer: Michael R. Gibbs
"""
import simpy
import random
max_q_size = 50
batch_size = 2
server_start_time = 55
processing_time = lambda : random.triangular(5,20,10)
arrival_gap = lambda : random.triangular(1,1,1)
# there is no wating so normal lists are good enough
batching_q = list()
processing_q = list()
server_q = list() # servers that are still starting up
class Server():
"""
Server that process batches
Has two states: starting up, and batch processing
"""
def __init__(self, id, env, processing_q, server_q):
self.id = id
self.env = env
self.processing_q = processing_q
self.server_q = server_q
self.start_process = self.env.process(self.start_up())
def start_up(self):
"""
starts up the server, then start processing batches
start up can be interrupted, stoping the server
"""
# start up
try:
print(f'{self.env.now} server {self.id} starting up')
yield self.env.timeout(server_start_time)
print(f'{self.env.now} server {self.id} started')
self.env.process(self.process())
except simpy.Interrupt:
print(f'{env.now} server {self.id} has been interupted')
def process(self):
"""
process batches
keeps going as long as there are batches in queue
If starts second batch, also interupts starting up server
"""
while True:
print(f'{self.env.now} server {self.id} starting batch process')
b = processing_q.pop(0)
yield self.env.timeout(processing_time())
print(f'{self.env.now} server {self.id} finish batch process')
if len(self.processing_q) > 0:
# more processes to do,
# steal batch from starting up server
s = self.server_q.pop() # lifo
s.stop()
else:
print(f'{env.now} server {self.id} no more batches, shutting down')
break
def stop(self):
"""
Interrupts server start up, stoping server
"""
try:
self.start_process.interrupt()
except:
pass
def gen_arrivals(env, batching_q, processing_q, server_q):
"""
Generate arring customers
If queues are too big customer will abort
If have enough customers, create a batch and start a server
"""
id = 1
while True:
yield env.timeout(arrival_gap())
q_size = len(batching_q) + (batch_size * len(processing_q))
if q_size >= max_q_size:
print(f'{env.now} customer arrived and aborted, q len: {q_size}')
else:
print(f'{env.now} customer has arrived, q len: {q_size}')
customer = object()
batching_q.append(customer)
# check if a batch can be creatd
while len(batching_q) >= batch_size:
batch = list()
while len(batch) < batch_size:
batch.append(batching_q.pop(0))
# put batch in processing q
processing_q.append(batch)
# start server
server = Server(id, env, processing_q, server_q)
id += 1
server_q.append(server)
# boot up sim
env = simpy.Environment()
env.process(gen_arrivals(env, batching_q, processing_q, server_q))
env.run(100)

Answer 2

When I add a condition to limit the number of servers, it works until a server was interrupted or shut down. Then, these servers seem to have disappeared and no longer active.
Sorry for asking you too much. Here is my code:
import simpy
import random
import numpy as np
class param:
def __init__(self, x):
#self.FILE = 'Setup_time.csv'
self.MEAN_INTERARRIVAL = x # arrival_gap
self.MEAN_SERVICE_TIME = 2 # processing_time
self.MEAN_SWITCH_TIME = 3 # server_start_time
self.NUM_OF_SERVER = 4 # maximum number of servers
self.MAX_SYS_SIZE = 10 # maximum number of customers in the system
self.BATCH_SIZE = 2
self.RANDOM_SEED = 0
# there is no wating so normal lists are good enough
class Server():
"""
Server that process batches
Has two states: starting up, and batch processing
"""
def __init__(self, id, env, processing_q, server_q, param):
self.id = id
self.env = env
self.processing_q = processing_q
self.server_q = server_q
self.start_process = self.env.process(self.start_up(param))
def start_up(self, param):
"""
starts up the server, then start processing batches
start up can be interrupted, stoping the server
"""
global num_servers
# start up
if self.id <= param.NUM_OF_SERVER: # I add the condition to limit the number of servers
try:
num_servers += 1
print(f'{self.env.now} server {self.id} starting up')
yield self.env.timeout(param.MEAN_SWITCH_TIME)
#yield env.timeout(np.random.exponential(1/param.MEAN_SWITCH_TIME))
print(f'{self.env.now} server {self.id} started')
self.env.process(self.process(param))
except simpy.Interrupt:
print(f'{env.now} server {self.id} has been interupted-------------------')
def process(self, param):
"""
process batches
keeps going as long as there are batches in queue
If starts second batch, also interupts starting up server
"""
global num_servers, num_active_server
while True:
num_active_server += 1
b = processing_q.pop(0)
print(f'{self.env.now} server {self.id} starting batch process')
yield self.env.timeout(param.MEAN_SERVICE_TIME)
#yield env.timeout(np.random.exponential(1/param.MEAN_SERVICE_TIME))
num_servers -= 1
num_active_server -= 1
print(f'{self.env.now} server {self.id} finish batch process')
if len(self.processing_q) > 0:
# more processes to do,
# steal batch from starting up server
#if self.server_q:
#s = self.server_q.pop(0) # Do these lines work for FIFO rule?
#s.stop()
s = self.server_q.pop() # lifo
s.stop()
else:
print(f'{env.now} server {self.id} no more batches, shutting down')
break
def stop(self):
"""
Interrupts server start up, stoping server
"""
try:
self.start_process.interrupt()
except:
pass
def gen_arrivals(env, batching_q, processing_q, server_q, param):
"""
Generate arring customers
If queues are too big customer will abort
If have enough customers, create a batch and start a server
"""
global num_servers, num_balk, num_cumulative_customer, num_active_server
id = 1
while True:
yield env.timeout(param.MEAN_INTERARRIVAL)
#yield env.timeout(np.random.exponential(1/param.MEAN_INTERARRIVAL))
num_cumulative_customer += 1
customer = object()
batching_q.append(customer)
q_size = len(batching_q) + (param.BATCH_SIZE * len(processing_q))
sys_size = q_size + (num_active_server * param.BATCH_SIZE)
#if q_size > max_q_size:
if sys_size > param.MAX_SYS_SIZE: # I check the limited condition for number customer in system instead of number customer in queue
num_balk += 1
batching_q.pop(-1) # I added the statement
print(f'{env.now} customer arrived and aborted, sys len: {sys_size }')
else:
#customer = object() # I moved these 2 lines above to update system size before using the if statement
#batching_q.append(customer)
print(f'{env.now} customer has arrived, q len: {q_size}, sys len: {sys_size}')
# check if a batch can be creatd
while len(batching_q) >= param.BATCH_SIZE:
batch = list()
while len(batch) < param.BATCH_SIZE:
batch.append(batching_q.pop(0))
# put batch in processing q
processing_q.append(batch)
# start server
server = Server(id, env, processing_q, server_q, param)
id += 1
server_q.append(server)
#Calculate balking probability
prob_balk = num_balk/num_cumulative_customer
#print(f'{env.now} prob_balk {prob_balk}')
list_prob_balk.append(prob_balk)
# boot up sim
trial = 0
Pb= [] #balking probability
global customer_balk_number
for x in range(1,3):
trial += 1
print('trial:', trial)
batching_q = list()
processing_q = list()
server_q = list() # servers that are still starting up
num_servers = 0 # number of server in system (both starting and serving server)
num_active_server = 0 # number of servers serving customers
num_balk = 0 # number of balking customers
num_cumulative_customer = 0 # total arriving customers
list_prob_balk = [] #list balk prob each trial
paramtest1 = param(x)
random.seed(paramtest1.RANDOM_SEED)
# create and start the model
env = simpy.Environment()
env.process(gen_arrivals(env, batching_q, processing_q, server_q, paramtest1))
env.run(30)
Pb.append(list_prob_balk[-1])
#print('List of balk prob', Pb )