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I am seeking some career advice regarding my current position as an Operations Research Analyst. I have been working in this role for three years, but I lack a formal academic background in Operations Research and Numerical Optimization. I got this job by demonstrating other related skills such as numerical computation, evolutionary optimization, computer programming, statistics and basic ML/Deep Learning.

Although I am confident in my basic knowledge of Operations Research and can implement optimization problems in AML's (Pyomo mainly) using solvers, I often feel that my lack of theoretical understanding limits my ability to go deeper into the concepts. I struggle when discussing topics like algorithmic implementation details of solvers, branch and bound, optimality, KKT conditions, or gap in MIP's, big-M method, among others. I hope that provides an idea regarding where I struggle.

As I want to remain in the field of OR, I am willing to put in efforts to improve my knowledge. However, I struggle to find relevant resources that can help me fill the gaps in my OR knowledge. While there are plenty of resources available for topics in ML/Data Science, I cannot find many resources in OR that can help me improve.

Therefore, I would like to ask for your advice on how I can develop my OR knowledge. Should I consider reading a book, taking formal training, or is there any other resource that you can recommend?

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  • $\begingroup$ What kind of mathematics background do you have? That can help determine how accessible (understandable) various books and other resources are to you. For example,. linear algebra, numerical linear algebra, probability, math analysis (limits, convergence, continuity, diferentiability, delta epsilon proofs, etc which .go deeper than the symbol manipulation in most calculus courses). $\endgroup$ Apr 15, 2023 at 14:30
  • $\begingroup$ @MarkL.Stone I have a sufficient understanding of Probability, Statistics, Linear Algebra, Calculus, classical Machine Learning algorithms and concepts. I generally don't find trouble in understanding equations, and if I do, with some help I can. Hope this will help you in pointing me to some helpful resources. $\endgroup$ Apr 18, 2023 at 3:50
  • $\begingroup$ "Convex Optimization" Boyd and Vandenberghe is a good book, and freely downloadable at the first authors' website. web.stanford.edu/~boyd/cvxbook . Only addresses continuous variable problems, though, but very good at what it does cover. $\endgroup$ Apr 18, 2023 at 3:59
  • $\begingroup$ Are these video lectures (youtube.com/playlist?list=PL3940DD956CDF0622) a good substitute for the book? $\endgroup$ Apr 18, 2023 at 8:28
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    $\begingroup$ The video lectures would be a good supplement to the book. But if you just watch the videos, and don't put it in the intellectual shoe leather to read and think through the details and try to solve some exercises, you will learn a lot less. The video are sort of like going to class (except you can't ask and get answers to questions). Reading the book and working the problems is like homework, which is where you really learn the stuff. $\endgroup$ Apr 18, 2023 at 12:11

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There are several good courses in Coursera: https://www.coursera.org/courses?query=operations%20research. MIT OCW is another very good option: https://ocw.mit.edu/search/?d=Mathematics&d=Sloan%20School%20of%20Management&q=%22operations%20research%22.

_____ Edited _____ (2023-04-15)

Following Prubin's suggestion, you can check EDX too (https://www.edx.org/search?q=operatins+research), very good courses.

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If you have the funds & time you can try the online MS in OR from Florida Tech. There are other online Masters in OR like Georgia Tech or Industrial Engineering courses from a number of other universities but the one at Florida Tech should be the most cost effective.
Or if not full Masters, check if you can do individual courses on optimization.
Also check Udemy as well.

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