OR itself contains a large number of applications that combine many of the above, e.g. network revenue management, but someone who has taken grad courses from the OR department alone would genuinely struggle to do anything significantly new or interesting.
People from computer science departments have also been gradually moving into these areas, witness growth in machine learning, algorithmic game theory etc.
In terms of CS moving into machine learning and artificial intelligence, the focus tends to be on applications in the consumer sphere - e.g. analysing big data to understand and recommend to consumers ala Amazon/Netflix, or image recognition to self-driving cars.
But these are mere sub-domains of OR. In business, what I think has the highest value and remains yet unexploited is the optimization branch/sub-topic of OR. The likes of production optimization, supply chain optmization, inventory optimization, facility location optimization, and my favorite, vehicle routing optimization.
is in fact identical to "empirical risk minimization" in Vapnik's statistical learning theory. As soon as you are not 100% sure about any of the numbers in your routing model, you find yourself in the same setting as that studied in "machine learning"!
Depending on the specifics of the problem, I would assert that it is possible for a more traditional OR trained person (who is rapidly disappearing IMO) to beat the CS person. Software Engineering tells you to modularize your code, use meaningful variable names etc. Typically Matlab codes written for OR problems try to replicate the same variable names used in deriving out the analytic equations (e.g. v, \lambda, \eta etc). Also, there is a strong emphasis on trying to make the code as compact as possible at the cost of modularity as a way of ensuring that mistakes are minimized. I have learned from painful experience (17 hours of debugging :) that the latter way of thinking is not wrong just different and even makes sense in specific domain related problems.
Given an unsolved OR problem, a top 10% ORite will likely beat a top 1% hacker (or even CS person) in solving that problem.
EDIT: Stop asking for top X if you really are trying to inspire more OR into more software. Most stuff in this world are done by mere mortals and not 1% of hackers. The true hackers of the world can probably already grok both malloc and TSP with timewindow constraints.
I would have guessed that most OR research labs had a fair number of CS grad students.
Exactly. A fair number. It should be the majority.
OR as a field has always had the problem of ambiguity and definition. Some call it Management Science, some call it Industrial Engineering. But whatever it entails, it has always suffered from visibility, especially in the hacker sphere, since it's not consumer related.
I don't think it is impossible to educate/increase awareness of OR in the market. Surely, the market is still behind as it is currently being educated on the value of simple analytics and data science, but eventually, I think the opportunity looms large for OR. It just has that irrefutable added value. But again, we need hackers to truly bring forth that value.