Everyday Math. Calculating a Faster Checkout Line - wall street journal article (see images below) by: Eugenia Cheng 1) Although the column does not explicitly mention economics concepts relevant to the evaluation of queuing systems, what economics concepts is the column describing? 2) How does risk aversion with regard to wait times affect the preference for serpentine or separate line queuing system? 3) How does a large penalty for queuing above a certain amount of time, say t, affect the preference for a serpentine or separate line queuing system?
Everyday Math. Calculating a Faster Checkout Line - wall street journal article (see images below) by: Eugenia Cheng 1) Although the column does not explicitly mention economics concepts relevant to the evaluation of queuing systems, what economics concepts is the column describing? 2) How does risk aversion with regard to wait times affect the preference for serpentine or separate line queuing system? 3) How does a large penalty for queuing above a certain amount of time, say t, affect the preference for a serpentine or separate line queuing system?
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Everyday Math. Calculating a Faster Checkout Line - wall street journal article (see images below)
by: Eugenia Cheng
1) Although the column does not explicitly mention economics concepts relevant to the evaluation of queuing systems, what economics concepts is the column describing?
2) How does risk aversion with regard to wait times affect the preference for serpentine or separate line queuing system?
3) How does a large penalty for queuing above a certain amount of time, say t, affect the preference for a serpentine or separate line queuing system?
![I love online grocery shopping-in part because I enjoy buying the same thing every week, in part
because I don't drive, but mainly because I hate standing in line.
When I do get stuck, I often find myself analyzing the way that checkout lines work. We are used to the
standard system of one line for each cashier. But what if there is just one big lane feeding multiple
cashiers?
How we think about the virtues of these two systems depends in part on psychology, but there's quite a
bit of mathematics in there too. The math of queuing theory turns out to be satisfyingly counterintuitive.
Having one line feeding many checkouts is sometimes called the "serpentine" system: The single queue
often snakes around, as lines do at airport security checkpoints. Mathematical queuing theory says that
the serpentine system should be faster than separate lines leading to separate cash registers, but only
with a condition called "no jockeying"-the assumption that people in multiple lines won't hop over to a
different line that has become free.
But that isn't realistic, as we can all attest. If you allow jockeying in multiple lines, the serpentine system
is no faster on average. It might intuitively seem faster because you won't get stuck behind a single
person taking a long time, but that same delay is just portioned out among more people, leaving the
average wait time the same.
I enjoy doing a small simulation by hand about one problematic customer, and I've found that under both
systems, the mean wait time-the total of all wait times divided by the number of people-is the same.
In a curious twist, one recent study showed that cashiers work faster when they are serving a
dedicated line.
In practice, the average wait time is likely to be longer in the serpentine system. That is because of human
delays: reaction times, walking times, unloading times. In a curious twist, one recent study showed that
cashiers work faster when they are serving a dedicated line. Perhaps it instills a sense of pride or
connection with the customers waiting for one hardworking cashier.
Summing up a situation in a single number is useful but crude. Averages are the most familiar way to
summarize data, but they come in different types. Instead of the mean, we could consider the median-
the middle number in a list of customers' wait times-in which case multiple lines seem better than a
serpentine line. A few people get through very slowly, but more people get through quickly.
Still, averages omit information that might be key. For the serpentine lines, I care about a measure called
variance, which assesses how far the numbers in the set vary from the mean. This can be far greater](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F6f534d69-c061-4715-803c-90e5bf5a63ee%2F729297cd-3d61-4285-ade8-0a2640f96521%2Fe5m9vhi_processed.png&w=3840&q=75)
Transcribed Image Text:I love online grocery shopping-in part because I enjoy buying the same thing every week, in part
because I don't drive, but mainly because I hate standing in line.
When I do get stuck, I often find myself analyzing the way that checkout lines work. We are used to the
standard system of one line for each cashier. But what if there is just one big lane feeding multiple
cashiers?
How we think about the virtues of these two systems depends in part on psychology, but there's quite a
bit of mathematics in there too. The math of queuing theory turns out to be satisfyingly counterintuitive.
Having one line feeding many checkouts is sometimes called the "serpentine" system: The single queue
often snakes around, as lines do at airport security checkpoints. Mathematical queuing theory says that
the serpentine system should be faster than separate lines leading to separate cash registers, but only
with a condition called "no jockeying"-the assumption that people in multiple lines won't hop over to a
different line that has become free.
But that isn't realistic, as we can all attest. If you allow jockeying in multiple lines, the serpentine system
is no faster on average. It might intuitively seem faster because you won't get stuck behind a single
person taking a long time, but that same delay is just portioned out among more people, leaving the
average wait time the same.
I enjoy doing a small simulation by hand about one problematic customer, and I've found that under both
systems, the mean wait time-the total of all wait times divided by the number of people-is the same.
In a curious twist, one recent study showed that cashiers work faster when they are serving a
dedicated line.
In practice, the average wait time is likely to be longer in the serpentine system. That is because of human
delays: reaction times, walking times, unloading times. In a curious twist, one recent study showed that
cashiers work faster when they are serving a dedicated line. Perhaps it instills a sense of pride or
connection with the customers waiting for one hardworking cashier.
Summing up a situation in a single number is useful but crude. Averages are the most familiar way to
summarize data, but they come in different types. Instead of the mean, we could consider the median-
the middle number in a list of customers' wait times-in which case multiple lines seem better than a
serpentine line. A few people get through very slowly, but more people get through quickly.
Still, averages omit information that might be key. For the serpentine lines, I care about a measure called
variance, which assesses how far the numbers in the set vary from the mean. This can be far greater
![under the standard system, since an unlucky choice of line can lead to an extra-long wait. The serpentine
system distributes that risk, which means that there is less variance.
I often care more about the variance than any average because I prefer predictability to good averages
over time. That's why I prefer escalators to elevators: An elevator may be faster on average, but I dislike
not knowing how long l'l have to wait for it. Similarly, I like walking (within reason) even if it takes a bit
longer than waiting for a bus or a train.
A different principle applies for meeting someone at the airport. I need neither the mean nor the
variance; I need just the minimum time it could take them to wait for their checked bags.
When I'm walking to work, I need to know the maximum time my commute can take (if I hit a red light at
every crosswalk). If I'm in a car or a bus, my caleulation is more subtle. The maximum time the trip can
take is almost infinite, since a bad accident could mean a big delay.
But that isn't very likely, so I'm more focused on something like the 95th percentile: If the mean journey
time is 30 minutes and the 95th percentile is 45 (that is, 95% of journeys take less time), I'llallow 45
minutes to be 95% sure of arriving on time. But l'l add some time for luck, especially if I really need to be
punctual. Sometimes, the only way to be completely certain of being on time is arriving the day before.
As for our grocery lines: Life isn't math. Human factors prevent us from knowing which line is the fastest.
My best strategy is just not to be in a hurry. I once spent a few weeks trying to pick the slowest lines and
used the time to memorize Wordsworth's "Intimations of Immortality." It not only helped me to pass the
time but actually made me want to stand in line longer.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F6f534d69-c061-4715-803c-90e5bf5a63ee%2F729297cd-3d61-4285-ade8-0a2640f96521%2F6nfeapr_processed.png&w=3840&q=75)
Transcribed Image Text:under the standard system, since an unlucky choice of line can lead to an extra-long wait. The serpentine
system distributes that risk, which means that there is less variance.
I often care more about the variance than any average because I prefer predictability to good averages
over time. That's why I prefer escalators to elevators: An elevator may be faster on average, but I dislike
not knowing how long l'l have to wait for it. Similarly, I like walking (within reason) even if it takes a bit
longer than waiting for a bus or a train.
A different principle applies for meeting someone at the airport. I need neither the mean nor the
variance; I need just the minimum time it could take them to wait for their checked bags.
When I'm walking to work, I need to know the maximum time my commute can take (if I hit a red light at
every crosswalk). If I'm in a car or a bus, my caleulation is more subtle. The maximum time the trip can
take is almost infinite, since a bad accident could mean a big delay.
But that isn't very likely, so I'm more focused on something like the 95th percentile: If the mean journey
time is 30 minutes and the 95th percentile is 45 (that is, 95% of journeys take less time), I'llallow 45
minutes to be 95% sure of arriving on time. But l'l add some time for luck, especially if I really need to be
punctual. Sometimes, the only way to be completely certain of being on time is arriving the day before.
As for our grocery lines: Life isn't math. Human factors prevent us from knowing which line is the fastest.
My best strategy is just not to be in a hurry. I once spent a few weeks trying to pick the slowest lines and
used the time to memorize Wordsworth's "Intimations of Immortality." It not only helped me to pass the
time but actually made me want to stand in line longer.
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