Programming Assignment 1

Programming Assignment 1 The purpose of this homework is to run a simulation of different queuing policies, and observe their impact on average waiting time. Imagine that we have five interchangeable service stations (airline check-in counters; store check-out counters; DMV service windows; immigration posts in an international airport; etc). Arriving passengers (or clients or citizens or visitors) are lined up in FIFO queues, and you have to decide how to organize these queues and associated queuing policies: Option 1: all arriving passengers are placed in a single queue, and service stations take passengers from the front of that queue. Option 2: each service station has its own queue, and arriving passengers are dispatched to a queue according to one of many policies: 2.A: round robin (1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, ...). 2B: arriving passenger is placed in a shortest queue. 2.C: arriving passenger is placed in a random queue. Inputs to the simulation: ● The duration of the simulation measured in minutes (D: make it arbitrarily long, do not worry about it being or not being realistic). ● The average arrival rate measured in minutes (A: arrivals are random, but on average there is one new passenger every A minutes), ● The average service rate measured in minutes (S: service rates are random, but on average they need about S minutes of service). For the sake of this study, make sure to crowd the system, by choosing S >> 5*A, without causing an overflow of your queues. Also choose D to be long enough to get rid of any transitory effects. Outputs of the Simulation for each queuing policy: ● The duration of the simulation (which may be longer than the input parameter, as when check-in closes, there may be passengers in the waiting queues and service stations). ● The maximum length of the queue for each queue. ● The average and maximum waiting time for each queue. ● The rate of occupancy of each service station (percentage of time each station was busy). ● If you want: show the real-time evolution of the queues during the run-time simulation. Code( Python): import random import matplotlib.pyplot as plt

class Pro_Ass: Queue_init = [] Station_init = [] def __init__ ( self ) -> None : self .Station_init = [ 0 for i in range ( 5 )] def add_Service_job ( self , job_time): pass def get_service_job ( self , num_Station) -> int : pass def Ser_Time ( self ): for i in range ( len ( self .Station_init)): if self .Station_init[i]: self .Station_init[i] -= 1 # Pull from the queue when station is empty if self .Station_init[i] == 0 and not self .is_empty(): job = self .get_service_job(i) if job: self .Station_init[i] = job def is_empty ( self ) -> bool : for q in range ( len ( self .Queue_init)): if len ( self .Queue_init[q]): # Find if something in Queue_init return False return True #SingleQueue class SingleQueue(Pro_Ass): def __init__ ( self ): super (). __init__ () self .Queue_init = [[]] def add_Service_job ( self , job_time): self .Queue_init[ 0 ].insert( 0 , job_time) def get_service_job ( self , num_Station) -> int : return self .Queue_init[ 0 ].pop() #RoundRobinQueue class RoundRobinQueue(Pro_Ass): RR_num = 0 def __init__ ( self ): super (). __init__ () self .Queue_init = [[] for i in range ( 5 )] def add_Service_job ( self , job_time): self .Queue_init[ self .RR_num].insert( 0 , job_time) self .RR_num = ( self .RR_num + 1 ) % 5 def get_service_job ( self , num_Station) -> int : if len ( self .Queue_init[num_Station]): return self .Queue_init[num_Station].pop()

Totalitems_in_system = 0 for x in range ( len (stat[ 'size' ])): items = 0 for y in range ( len (stat[ 'size' ][x])): items += stat[ 'size' ][x][y] Totalitems_in_system += items stat[ 'Average_time' ] = Totalitems_in_system / stat[ 'Time_count' ] / A return stat , test if __name__ == "__main__" : # initialize parameters A = 4 S = 8 * A D = 10000 stats = { 'single_Queue' : {} , 'Round_robin' : {} , 'Short_Queue' : {} } single_Queue = SingleQueue() stat , _ = simulation(A , S , D , single_Queue) stats[ 'single_Queue' ] = stat for q in range ( len (stats[ 'single_Queue' ][ 'size' ])): ts = plt.plot(stats[ 'single_Queue' ][ 'size' ][q] , label = 'Queue %d' % q) print ( "Single Queue" , stat[ 'Average_time' ]) Round_robin = RoundRobinQueue() stat , _ = simulation(A , S , D , Round_robin) stats[ 'Round_robin' ] = stat for q in range ( len (stats[ 'Round_robin' ][ 'size' ])): ts = plt.plot(stats[ 'Round_robin' ][ 'size' ][q] , label = 'Queue %d' % q) print ( "RoundRobin Queue" , stat[ 'Average_time' ]) Short_Queue = ShortestQueue() stat , _ = simulation(A , S , D , Short_Queue) stats[ 'Short_Queue' ] = stat for q in range ( len (stats[ 'Short_Queue' ][ 'size' ])): ts = plt.plot(stats[ 'Short_Queue' ][ 'size' ][q] , label = 'Queue %d' % q) print ( "Shortest Queue" , stat[ 'Average_time' ]) Random_Queue = RandomQueue() stat , _ = simulation(A , S , D , Random_Queue) stats[ 'Random_Queue' ] = stat for q in range ( len (stats[ 'Random_Queue' ][ 'size' ])): ts = plt.plot(stats[ 'Random_Queue' ][ 'size' ][q] , label = 'Queue %d' % q) print ( "Random Queue" , stat[ 'Average_time' ])