Managing payment queues
A clothes shop becomes very busy at peak hours when people often queue to pay. Currently, there is only one cashier, and you have been asked to do a cost-benefit analysis to determine how many cashiers would be needed to reduce waiting times as much as possible to increase profitability.
You have decided to build a discrete-event model. You know that:
- On average, a new customer joins the queue every 15 seconds during peak hours;
- Customers usually bring several items with them, typically between 1 and 20; and
- It takes an average of 3 seconds to scan an item in the cashier, and the payment generally takes another 20 seconds.
The argument counter stores the SimPy resource, and the argument customer_num tracks the number of customers.
Let's run the model and calculate how long it takes to serve 30 customers with a different number of cashiers.
This exercise is part of the course
Discrete Event Simulation in Python
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
def customer(env, customer_num, counter):
num_items = random.randint(1.0, 20)
print(f"Time: {env.now:7.4f} sec | Customer {customer_num:02d} > Joining the queue with {num_items:02d} items!")
# Open the resource counter request
with counter.____() as request:
yield request
print(f"Time: {env.now:7.4f} sec | Customer {customer_num:02d} > Got to cashier!")
time_counter = TIME_PAY + random.randint(1.0, 20) * TIME_SCAN_PER_ITEM
# Yield the processing time
yield env.____(time_counter)
print(f"Time: {env.now:7.4f} sec | Customer {customer_num:02d} > Finished ")
env = simpy.Environment()
# Create resource
counter = simpy.Resource(env, capacity=____)
env.process(source(env, NEW_CUSTOMERS, INTERVAL_CUSTOMERS, counter))
env.run()