There’s a branch of math that studies how people wait in line: queueing theory. It’s not just about people standing in line, but about any system with clients and servers.
An introduction to queueing theory, about what you’d learn in one or two lectures, is very valuable for understanding how the world around you works. But after a brief introduction, you quickly hit diminishing return on your effort.
The simplest models in queueing theory have nice closed-form solutions. They’re designed to be easy to understand, not to be highly realistic in application. They make simplifying assumptions, and minor variations no longer have nice solutions. But they make very useful toy models.
One of the insights from basic queueing theory is that variability in arrival and service times matters a great deal. This is unlike revenue, for example, where you can estimate total revenue simply by multiplying the estimated number of customers by the estimated average revenue per customer.
A cashier who can handle a stream of customers on average is in big trouble. If customers arrived at regular intervals and all required the same amount of time to serve, then if things are OK on average, they’re OK. But if there’s not much slack, and there’s the slightest bit of variability in arrival or service times, long lines will form.
I work through a specific example here. The example also shows that when a system is near capacity, adding a new server can make a huge improvement. In that example, going from one server to two doesn’t just cut the waiting time in half, but reduces it by a couple orders of magnitude.
As I mentioned at the beginning, queueing theory doesn’t just apply to shoppers and cashiers. It also applies to diverse situations such as cars entering a highway and page requests hitting a web server. See more on the latter here.