Author: Andrew Gyakobo
Important
It is preferable that you run this code exclusively with Python 3.10 (and onward)
This project showcases 4 examples of queuing: Single Queue, Round Robin, Shortest Queue, Random Queue, that would try to process as many persons as possible.
Hearkening from the intro statement, this project basically tries to simulate a 5 vending stations which would in their turn accept customers. How and in what formation each queue would be organised will be analized in the methodology.
Before you run this program please run the following command to ensure the version of python you currently have:
$ python3 -VIt is important to understand that you ought need to run this program with Python3.10 (or above). If you wish to update to Python version 3.12 please follow the following instructions:
$ sudo add-apt-repository ppa:deadsnakes/ppa
$ sudo apt-get update
$ sudo apt-get install python3.12- To start off, we basically group all the passengers in one
Single Queue/Lineand process the one after the other. Nothing special here just an ordinary long queue.
def single_queue_func(self):
self.q[0].append(self.time)- The second, option would be to use a
Round Robin Queue.
def round_robin_func(self):
station = self.round_robin % self.number_of_stations
self.q[station].append(self.time)
self.round_robin += 1A round-robin queue is a type of scheduling algorithm used in computing where each process or task is assigned a fixed time slot in a cyclic order. This ensures that all tasks are treated equally and prevents any single task from monopolizing the CPU. It is particularly useful in time-sharing systems where multiple users or tasks require equal CPU time.
- Fixed Time Quantum: Each task is given a specific time slice or quantum to execute.
- Cyclic Order: Tasks are arranged in a circular queue and each task gets its turn in a cyclic manner.
- Preemption: If a task does not complete within its allocated time quantum, it is preempted and placed at the end of the queue.
- Another option is the
Shortest Queue. Long story short, each passenger attends the shortest queue he can find at the moment.
def shortest_queue_func(self):
def queue_length(var):
return len(self.q[var])
station = min(range(self.number_of_stations), key=queue_length)
self.q[station].append(self.time)- Lastly, we've got the
Random Queue. Basically each passenger joins a random queue.
def random_queue_func(self):
station = randint(0, self.number_of_stations - 1)
self.q[station].append(self.time)- Single Queue Results
At most 511 passengers were processed.
Option: Single Queue
Total Number of Passengers: 470
Station 1:
Simulation duration: 1000
Maximum queue length: 385
Average waiting time: 394.376 && Maximum waiting time: 830.000
Occupancy rate: 95.810%
Station 2:
Simulation duration: 1000
Maximum queue length: 385
Average waiting time: 0.000 && Maximum waiting time: 0.000
Occupancy rate: 0.000%
Station 3:
Simulation duration: 1000
Maximum queue length: 385
Average waiting time: 0.000 && Maximum waiting time: 0.000
Occupancy rate: 0.000%
Station 4:
Simulation duration: 1000
Maximum queue length: 385
Average waiting time: 0.000 && Maximum waiting time: 0.000
Occupancy rate: 0.000%
Station 5:
Simulation duration: 1000
Maximum queue length: 385
Average waiting time: 0.000 && Maximum waiting time: 0.000
Occupancy rate: 0.000%- Round Robin Results
At most 498 passengers were processed.
Option: Round Robin
Total Number of Passengers: 498
Station 1:
Simulation duration: 1000
Maximum queue length: 25
Average waiting time: 23.404 && Maximum waiting time: 80.000
Occupancy rate: 85.308%
Station 2:
Simulation duration: 1000
Maximum queue length: 25
Average waiting time: 45.409 && Maximum waiting time: 128.000
Occupancy rate: 96.173%
Station 3:
Simulation duration: 1000
Maximum queue length: 25
Average waiting time: 113.400 && Maximum waiting time: 257.000
Occupancy rate: 95.760%
Station 4:
Simulation duration: 1000
Maximum queue length: 25
Average waiting time: 23.845 && Maximum waiting time: 92.000
Occupancy rate: 83.921%
Station 5:
Simulation duration: 1000
Maximum queue length: 25
Average waiting time: 45.413 && Maximum waiting time: 210.000
Occupancy rate: 92.111%- Shortest Queue Results
At most 544 passengers were processed.
Option: Shortest Queue
Total Number of Passengers: 499
Station 1:
Simulation duration: 1000
Maximum queue length: 2
Average waiting time: 34.312 && Maximum waiting time: 78.000
Occupancy rate: 94.890%
Station 2:
Simulation duration: 1000
Maximum queue length: 2
Average waiting time: 30.727 && Maximum waiting time: 100.000
Occupancy rate: 94.332%
Station 3:
Simulation duration: 1000
Maximum queue length: 2
Average waiting time: 28.590 && Maximum waiting time: 100.000
Occupancy rate: 94.224%
Station 4:
Simulation duration: 1000
Maximum queue length: 2
Average waiting time: 27.674 && Maximum waiting time: 105.000
Occupancy rate: 90.925%
Station 5:
Simulation duration: 1000
Maximum queue length: 2
Average waiting time: 22.056 && Maximum waiting time: 70.000
Occupancy rate: 91.045%- Random Queue Results
At most 540 passengers were processed.
Option: Random Queue
Total Number of Passengers: 511
Station 1:
Simulation duration: 1000
Maximum queue length: 23
Average waiting time: 51.553 && Maximum waiting time: 144.000
Occupancy rate: 89.781%
Station 2:
Simulation duration: 1000
Maximum queue length: 23
Average waiting time: 60.466 && Maximum waiting time: 158.000
Occupancy rate: 90.702%
Station 3:
Simulation duration: 1000
Maximum queue length: 23
Average waiting time: 26.030 && Maximum waiting time: 92.000
Occupancy rate: 84.166%
Station 4:
Simulation duration: 1000
Maximum queue length: 23
Average waiting time: 179.733 && Maximum waiting time: 309.000
Occupancy rate: 97.104%
Station 5:
Simulation duration: 1000
Maximum queue length: 23
Average waiting time: 17.659 && Maximum waiting time: 66.000
Occupancy rate: 81.317%MIT