Which are the more math intensive areas in Operations research? I know OR is largely math, but could anybody rate operations-research courses/topics in the order of how intensively/deeply they require math? The comparison that is being drawn here is depth vs breadth of math used.

Another sub-question here: Is it usually so that interests and proficiency in deeper math OR courses are better suited for a career in academia, while broader,shallow and more real world math application areas are more suited to the industry?

Background: Was going through the core reqd courses for an MS in OR on the USC website, and found that only 3/6 of these went into actual fun and interesting math. The other 3 looked more like professional/vocational courses.

asked 12 Jan '13, 07:17

shadowblade360's gravatar image

shadowblade360
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edited 12 Jan '13, 13:45

This is not an answer: but check out the course list at Eotvos Lorand University in Hungary here

(12 Jan '13, 10:05) Gilead ♦

Thanks Gilead.

(12 Jan '13, 14:55) shadowblade360

I am guessing the math intensity has got more to do with the kind of courses offered at a univ, than actual OR courses.

(13 Jan '13, 00:11) shadowblade360
1

Well, I've always conceived of operations research as "very" applied math (as John D Cook puts it), which is historically how it was. However, some schools offer OR degrees that feel like semi pure-math degrees. I think @Matthew's answer hits the nail on the head. There is a core mathematics basis but different places differ on how intensely they pursue the rigorous mathematical aspects of the subject.

(13 Jan '13, 01:31) Gilead ♦

The core areas of mathematics that constitute operations research are optimization (or mathematical programming) and stochastic processes. So if you want mathematical content, look for courses like Linear Programming (the mathematical foundation is linear algebra), Nonlinear Programming (differential calculus and linear algebra), Network (linear algebra and computer science) Integer Programming, Combinatorial Optimization, and Convex Analysis on the optimization side. On the probability side, Stochastic Processes, Queueing, Simulation.

It is probably broadly but not entirely true that academics is more associated with more mathematical courses and practice careers are more associated with applied courses. But exposure to a mix of both will make you a better operations researcher in either career path than concentrating exclusively on one or the other. Historically, the mathematics of operations research were initially motivated by applied problems, though the mathematics can get quite esoteric when pursued for its own sake.

link

answered 12 Jan '13, 17:29

Matthew%20Saltzman's gravatar image

Matthew Salt... ♦
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Here is how I would rate them

  1. Queueing theory was the hardest for me. sure one can do M\M\1 gueues easily but anything beyond that is very difficult. It is almost impossible to contribute anything meaningful there. Queuing theory needs mastery of almost all areas of OR. You have to be really good in your measure theory and stochastic processes and also on top of your optimization game. You have to either have a good intuition or really a master of simulation. I passed it with George Shantikumar and it was very challenging. I have seen many professors who confessed queueing theory is hard for them too
  2. Second on the list is anything "stochastic". It can be quantitative finance or stochastic control or stochastic optimization. This thing is hard!
  3. Optimization might look difficult but it is easier to grasp. You read a couple of books (Do not forget Schrijver's book) and you can read almost any paper
  4. At the bottom of the list is logistics, supply chain management and those things. And they are pretty easy.
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answered 13 Jan '13, 02:32

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Mark ♦
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edited 13 Jan '13, 02:33

This is exactly what I was looking for. Thanks! :)

(13 Jan '13, 03:15) shadowblade360
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Asked: 12 Jan '13, 07:17

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Last updated: 13 Jan '13, 03:15

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