I am a dabbler in OR, and have done the basic Optimization and Operations research courses in my bachelors in engineering. I was wondering if somebody could help evaluate the road ahead in terms of the paths available. As I understand, these are the focus areas:

  1. Mixed Integer Linear Optimization
  2. Mixed Integer Convex Optimization
  3. Mixed Integer NonLinear Optimization
  4. Combinatorial Optimization.

Source : ETH Zurich OR program

Please correct me if this is wrong. Also, please evaluate the above on the basis of how hot the research in these areas is, and the job opportunities available for these.


asked 01 Oct '12, 23:01

shadowblade360's gravatar image

accept rate: 0%

By listing the ETH list, you give a very specialized listing (apparently they are all discrete optimizers of some sort). There are lots of things that don't begin "mixed integer": convex programming, stochastic programming, simulation, semidefinite programming, heuristic methods, various application areas, etc. etc. The paths available depend on the research interests of the faculty at the places you are considering.

As for job opportunities, I assume you are talking about doctoral degrees (masters students in OR are generalists, learning something about lots of areas). Practically any area can lead to a great job if you do an interesting dissertation.


answered 02 Oct '12, 10:22

Michael%20Trick's gravatar image

Michael Trick ♦♦
accept rate: 20%

Thanks for the reply.

By job opportunities, I meant which areas are "hot" now, ie are more likely to fetch one a job, or are being researched on heavily?

(02 Oct '12, 11:03) shadowblade360

One thing about Mike's reply is that it too is restrictive, considering only subfields of optimization. Traditionally, optimization is one of two broad areas of mathematics that are considered operations research. The other is stochastic processes, which encompasses problems such as characterizing the behavior of queues. This area connects with discrete-event simulation, and it connects to optimization through stochastic programming, but there is plenty of depth to the field on its own merits as well. (I don't have much to say in detail, because I'm a optimizer as well.)

OR broadly defined takes the insights from the mathematical fields and applies them to real-world situations to help decision makers improve the performance of systems. There is substantial overlap with currently hot fields like analytics and data science, as well as more traditional operations and service management, economics, computer science, and various engineering disciplines.


answered 02 Oct '12, 14:29

Matthew%20Saltzman's gravatar image

Matthew Salt... ♦
accept rate: 17%

Missing from earlier replies: forecasting; machine learning; data mining; decision theory.

Also, all those (and the previous list of categories) are model/algorithm types. There may be some schools teaching "soft OR", problem structuring techniques (influence diagrams, concept maps ...) and general care and feeding of clients.


answered 03 Oct '12, 19:13

Paul%20Rubin's gravatar image

Paul Rubin ♦♦
accept rate: 19%

You could also study OR from the application perspective (transportation, healthcare, telecommunication, etc.). Then, you would be a specialist of this sector by applying the different techniques of OR (optimization, simulation, etc.).


answered 04 Oct '12, 22:58

mrtncou's gravatar image

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Asked: 01 Oct '12, 23:01

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Last updated: 04 Oct '12, 22:58

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