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.

Cet exercice fait partie du cours

Discrete Event Simulation in Python

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Exercice interactif pratique

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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()

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