## Saturday, July 2, 2011

### Little's Lore

Guerrilla alumnus Paul P. has a penchant for sending me interesting things, and recently he sent me a piece on Little's law. Remarkably, it wasn't just another proof of L = λW, but a brief retrospective written by none other than John Little himself. I quote it here because it not only provides some unusual insight into how these things get done, but it is written in a charming and self-effacing style.

How did a sensible young PhD like me get involved in a crazy field like this? From 1957-1962, I taught operations research at the Case Institute of Technology in Cleveland (now Case Western Reserve University). I was asked to teach a course on queuing. OK. Initially I used my own notes, but when Morse (1958) came out, I used his book extensively. Queuing was taken by most of the OR graduate students and, indeed, one of these, Ron Wolff, went on to become a first class queuing theorist (Wolff 1989). One year we were at the point when we had done the basic Poisson-exponential queue and moved through multi-server queues, and some other general cases. I remarked, as many before and after me probably have (and Morse does), that the often reappearing formula L = λW seemed very general. In addition I gave the heuristic proof. After class I was talking to a number of students and one of them (Sid Hess) asked, "How hard would it be to prove it in general?" On the spur of the moment, I obligingly said, "I guess it shouldn't be too hard." Famous last words. Sid replied, "Then you should do it!"

The remark stuck in my mind and I started to think about the question from time to time. Clearly there was something fundamental going on, since, when you draw the picture you do not really seem to need any detailed assumptions about interarrival times, service times, number of servers, order of service, and all the other ingredients that go into the panoply of queuing models. You only seemed to need a process that goes up and down in unit amounts and some guarantee of steady state and conservation of items. In addition, because I could see there were end effects in the picture, there needed to be a way to get rid of them in the limit. It seemed to me I was in the general arena of stationary stochastic processes. I am not a mathematician by training, and so I bought copies of measure theoretic stochastic process books like Doob (1953), which mentioned stationary processes and ergodic theorems.

My family's habit at the time was to go to Nantucket in the summer where my wife's family had a small summer house. We would load our children in a station wagon, drive to Woods Hole, take the ferry, and spend a couple months away from the world. Since the beach was the baby-sitter, I was able to split off solid blocks of time to work on research as a good assistant professor should. (I wish I could do that today!) I always brought a pile of books and projects with me. L = λW was one of them. I soon ran into problems that required more than looking up theorems in my new books, but I worked out approaches to the road blocks and eventually wrote everything up, giving it my best shot. I sent the paper off to Operations Research. It was accepted on the first round.

[Source: D. Chhajed and T. J. Lowe (eds.) Building Intuition: Insights From Basic Operations Management Models and Principles. © Springer, 2008]

In my usual notation, I write Q = λR where Q is the queue length (including requests in service) and R is the residence time: R = W + S, the sum of the time spent waiting for service and the service time. In the above, W is also the waiting time and therefore L is the waiting-line length or the queue length that doesn't include requests in service.
Same as the visual proof in Chapter 2 of Perl::PDQ.