When I was applying for jobs, that is, faculty positions at universities, the search committees asked a lot of questions; about my research plans, teaching concepts, collaboration opportunities, about third party funding and my willingness to take on administrative duties. Besides this information gathering, I was also asked “more challenging” questions. One of them was
What were the greatest achievements in our field in the past ten years?
Our field, that is operations research, and in my particular case, discrete optimization and integer programming. Depending on the audience, my answer would have been more mathematical (“smoothed analysis of the simplex method“) or more business/applications (“revenue management”). The precise answer is not so important, but you should have one. Yet, good schools demand more of their leading scholars: they look for vision. So, some of them unavoidably asked
What will be the next big thing?
That is a tough one! It can be answered generally or personally. Depending on our angle, we would rephrase the question in different ways. Being a scientist, my interpretation is: Which major research question would you like to see settled next? Most research topics in my head deal with finding and exploiting structures in mathematical programs. I truely believe that we are not making best use of what modelers explicitly or implicitly encode in their integer programs. I expect from this to be able to solve much larger and more complicated models. But does this make it a big thing?
What are the grand challenges of operations research? My best way of answering this is to speak about opportunities, and the big loss if we miss them. If you look at emerging technologies (see Gartner’s 2014 hype cycle) there are several buzz words everyone is talking about, like analytics (yes!), big data, internet of things, fourth industrial revolution, etc. Non-specialists can connect to these notions, laymen have at least a fuzzy understanding of what these are all about, and: all consider them important. This is a mix that makes many companies jump on the bandwaggon, just to be part of it, they don’t want to miss anything.
But what is the substance behind these notions? What is the scientific foundation? Is this science?
I believe this is our field! Data is useless per se, even when big (maybe big data alone is even more useless than small data). We need methods to let data guide our best decisions—optimization methods. My fridge orders fresh milk, but according to which algorithms? I cannot speak of the #IoT without speaking about networks and network algorithms. Of what use is a digitalized production if I don’t know how to (best) operate the machines? Operations research and mathematical optimization offer models and methods to make significant and scientifically sound contributions here. Mathematics and algorithms are much needed to capture the buzz words’ true complexity and to answer questions like “how concretely should my internet of things function?” The answers won’t be always simple and this is the big risk: if we do not provide what we think are the right answers—operations research replies to the buzz words—someone else will. And believe me, someone else will have simpler answers, non-scientific answers, because these are much easier to digest and accept.
We should all work hard to establish operations research as the science behind big data, internet of things, industry 4.0, etc. We are the optimizers. We can make the best of these concepts. And we should.