Notes

import statistics
import numpy.random
import simpy
import random
from random import seed
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import time
from simpy.util import start_delayed

seed(8889189) #for seed of randint function
random_seed = 997811 #for seed of other random generators
interarrivalTimes = []
serviceTimes = []
waitingTimes = []
renegingTimes = []
value = []
avg = []
in_queue, in_system = [],[]
tme_in_queue, len_in_queue = [],[]
interarrival_time = 10
service_time = 8
num_server = [1,2]

print('Customer_Num -- Arrival Time -- Service Begin -- Service End -- Num Server')


class Markovian(object):
def __init__(self, env):
self.env = env

def getService(env, Customer, server):
# new Customer arrives in the system
waitingStart = env.now
arrivalStart = env.now
serviceEnd = env.now
serviceStart = env.now
queue_in = env.now
length = len(server.queue)
tme_in_queue.append(queue_in)
len_in_queue.append(length)
req = server.request()
yield req
#checks if the server is empty or not, if empty, enters from the below command
queue_out = env.now
serviceStart = env.now
length = len(server.queue)
tme_in_queue.append(queue_out)
len_in_queue.append(length)
time_in_queue = queue_out - queue_in
in_queue.append(time_in_queue)
waitingTimes.append(serviceStart - waitingStart)
#starts service, and appends waiting times
#print('%.1f Begin Service of Customer %d' % (serviceStart, Customer))

yield env.timeout(random.expovariate(1.0/service_time))
serviceEnd = env.now
serviceTimes.append(env.now - serviceStart)

yield server.release(req)
#print('%.1f End Service of Customer %d' % (env.now, Customer))
#print("Customer %d spent %7.4f time in server" %(Customer,))
for i in range(len(num_server)):
if(num_server[i] == 1):
print('%d --------------- %.2f -------- %.2f -------- %.2f -------- 1' % (Customer, arrivalStart, serviceStart, serviceEnd))
elif(num_server[i] == 2):
print('%d --------------- %.2f -------- %.2f -------- %.2f -------- 2' % (Customer, arrivalStart, serviceStart, serviceEnd))

def generator(env, server):
markovian = Markovian(env)
Customer = 0
# generate new Customers
while True:
t = random.expovariate(1.0/interarrival_time)
yield env.timeout(t)

interarrivalTimes.append(t)
Customer += 1
#print('%.1f Arrival of Customer %d' % (env.now, Customer))

env.process(Markovian.getService(env, Customer, server))

def avg_line(df_length):
## finds the time average number of customers in the waiting line
## use the next row to figure out how long the queue was
df_length['delta_time'] = df_length['time'].shift(-1) - df_length['time']
df_length = df_length[0:-1]
avg = np.average(df_length['len'], weights=df_length['delta_time'])
return avg
def main():
random.seed(6)
env = simpy.Environment()
server = simpy.Resource(env, len(num_server))
env.process(Markovian.generator(env, server))
t=1000
env.run(until = t)
df1 = pd.DataFrame(tme_in_queue, columns=['time'])
df2 = pd.DataFrame(len_in_queue, columns=['len'])
df_length = pd.concat([df1, df2], axis=1)
avg_length = Markovian.avg_line(df_length)
print('The average number of customer in queue is %.2f' % (avg_length))
if __name__ == "__main__":
main()