[mck-]

I actually agree with you there. Since OR is so multi-disciplinary, it is paramount to have a broad knowledge of all the fields it touches. Although I specialised in OR in my masters, my bachelors was actually CS&Economics/Business, which has been of great value to me.

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.

[dvse]

Just for fun - "stochastic optimization" as in here: www2.isye.gatech.edu/~ashapiro/download.php?Down=book

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"!

[msellout]

Thanks for the link.