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Fall 2016 PhD Industrial Engineering - Need Advice on Studying Up Before School Starts


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Hi all -

I will be attending an [undecided] Industrial Engineering PhD program this Fall, but I have been working in an unrelated field for the past few years. Do any current IE/OR PhDs have any advice for studying up in preparation for Grad School? I have a Linear Algebra textbook I plan to work through, but are there any other subjects I should especially focus on at this point? It's been a few years since undergrad, so anything I can do to refresh and prepare would be great.

Thanks for any advice!

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It might be helpful to mention your background, and what you already know. Much of OR is centered around the following, so it might help to brush up on these topics. More directed feedback is possible if you provide further info.

  • Optimization - start with LP, duality etc. Also study convex optimization, numerical optimization (BFGS, conjugate gradient etc.). If time permits, get some exposure to integer programming using gomory cuts and other methods.
  • Probability theory - basics of probability and statistics starting from common distributions and moment calculations. Bayes rule and its applications are also important. You should also study stochastic processess like markov process, martingales, and Gaussian process models (Bayesian framework) if time permits.
  • Data structures and algorithms - do not under estimate the utility of algorithm design, complexity analysis, and programming in general. Some advanced topics like NP-completeness, approximation, and randomized algorithms are helpful too.
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Thank-you so much, Dawnbreaker. Because I'm coming from undergrad with only a superficial introduction into most of these topics, and since then I've been in an unrelated field, I would say "what I already know" is limited. I've been particularly interested in production and logistics systems design, and I've recently gotten an offer to join a well-funded Human Factors lab (which I hadn't considered but sounds intriguing). I think I would prefer using optimization methodology over statistical quality control, if that makes sense, but I'm fairly fluid at this point.



Edited by bandinterwebs
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