Celebrating the operations research mindset
At a national society meeting, the president of INFORMS stated:
“We need to develop new methodology and to adapt old; we need to generalize our basic theoretical techniques and to broaden their range of application.”
Who said this and when?
The speaker was Phil Morse, the first president of the Operations Research Society of America (ORSA), and the occasion was the ORSA Annual Meeting in May 1953. Morse’s basic prescription – keep building up our theory; keep expanding our applications – works just as well today as a guide for the future of operations research.
Others have proffered up their own views of the future of operations research since Morse first looked into the crystal ball. Some of these are devastating in their pessimism. In 1979, Russell Ackoff wrote, “The life of O.R. has been a short one – it was born late in the 1930s – by the mid-1960s most O.R. courses were given by academics who never practiced it, depriving O.R. of its unique incompetence.” Ackoff argued that we “... should want to help create a world in which the capabilities of O.R. are considerably extended but in which the need for O.R. is diminished.” This does not sound like a recipe for growing a discipline. With a healthy 12,000+ membership, INFORMS has happily not followed Ackoff’s advice.
Others have provided more optimistic views. Ten years post‐Ackoff, the irrepressible Alexander Rinnooy Kan wrote that, “The future of O.R. is bright – if there is anything worrying about the state of O.R., it is that our discipline seems to spend such an inordinate amount of time and effort worrying about itself.”
Who can’t relate to that? What should our name be: Operations research? Management science? Decision sciences? Analytics? Calcuholics?
This annoying issue has been around for a long time.
In a 1952 article in the Journal of Applied Physics, Phil Morse wrote: “Personally, I would prefer to forget about definitions and get on with the work. After all, who cares what it’s called, as long as it’s useful and is used?” 1952!!
Our methodological focus might seem odd to outsiders who do not appreciate the history of our field. Why are our main mathematical tools rooted in optimization and stochastic processes? Why not number theory and topology? The answer is that our discipline is rooted in the scientific study of operations, those tasks and processes that represent how organizations “get things done.” Such study is meant to improve decisions, which explains why optimization is so important, while randomness and uncertainty abound in operational processes, making applied probability expertise essential.
Advances in theory have intrinsic value, like art or music, beyond that offered from use in future applications. And while methods and tools evolve over time, our basic approach of using models to better understand systems and improve their performance has stood the test of time. Going back to Morse for a moment, he was really excited about using analogue devices for teaching O.R. One exciting educational application went like this:
“A radioactive source and two Geiger counters provide two purely random sequences of pulses, which may be varied in mean rate merely by changing the distance of the counter from the source. For example, one counter can represent arrivals in a queue, and the other can represent the service operation that removes the individual from the waiting line; an electronic counter can then indicate the instantaneous length of the queue.”
Talk about glowing customers!
Operations research, unlike economics (or physics for that matter), does not possess a “world view” – we have no underlying holistic theory for how the world works. The natural unit of interest in O.R. is “the problem.” It shows in how we label things – the diet problem, traveling salesman problem, stochastic queue median problem, etc. – and it shows in how we decompose more complicated situations into something we can study, model, understand and perhaps improve.
But operations research has a mindset. Operations researchers are the masters of structuring messy situations into problems amenable to analysis. Operational science includes seeing or characterizing phenomena of all sorts as operations. Modeling science (or perhaps modeling art) calls upon our creativity to create new models for such operations. These are key O.R. skills, and they capture what many INFORMS members really do.
Sometimes, immersion in a particular problem domain leads operations researchers to become subject matter experts. Thus, Jon Caulkins has figured out the optimal price for cocaine, Larry Wein and Jerry Brown have secured the homeland, Margaret Brandeau optimally allocates public health resources, Dimitris Bertsimas will ensure that optimization is robust, Arnie Barnett can tell you when your plane will crash, Garrett van Ryzin will get you a seat on that plane at a lower price, Linda Green and Carri Chan will divert an ambulance to get you to the hospital, and Ralph Keeney can explain why it’s all your fault.
O.R. is not an add‐on to such expertise; rather O.R. was crucial in establishing this expertise in the first place.
So, with mindset and domain expertise in place, all of us can rally to our core purpose of advancing our science and practice. All of us can contribute to helping decision‐makers use our technologies, and enable organizations to institutionalize our approaches in their own decision processes. And, all of us can, at least in some small way, use our expertise to help make the world a better place.
Phil Morse had it right 60 years ago. We need to develop new methodology and to adapt old; we need to generalize our basic theoretical techniques and broaden their range of application.
Some final thoughts from your departing member‐in‐chief: We can have a lot of fun doing these things while celebrating how our field has helped us lead more meaningful lives. Operations research is a terrific, wonderful area of endeavor of which you should all be proud.
Keep doing stuff!