31

Here are a few differences I have noticed. (I am mainly an academic, but also work part-time for a consulting company that specializes in OR and AI models for supply chain and other industries.) Project Speed. The two environments operate on totally different timelines. For the most part, industry OR projects have specific deliverables with tight deadlines—...


19

My experience, as a former phd in an academic context, post doc in a semi-industrial/semi-academic context and now working for an APS editor, is the following: When working on academic problems one will typically work on well defined problems. Even though those problems will be (very) difficult they will typically not involve a lot of side constraints. On ...


18

Here are a few sources. In my opinion, all are to be taken with a grain of salt. Scimago Journal and Country Rank The Scimago Journal and Country Rank (the link goes to the Management Science and Operations Research category) ranks journals according to SJR's proprietary index. The first 7 are: To me, the rankings seem reasonable, if not totally reliable. ...


18

One difference I did not see mentioned in earlier responses: moving targets. In academe, we tend to start out with a well-delineated (if perhaps overly simple) problem, and we stick with that problem. In the real world, the problem you think you are given and the problem you ultimately (hopefully) solve may not be the same. As models evolve and test cases ...


14

Personally, I think you shouldn't be merely motivated by quantitative journal rankings as they often rely on measures that could be manipulated. In particular, the impact factor of a journal would increase if the editor required authors to cite a large number of recent publications of that journal. Although the organizations responsible for calculating these ...


13

The implementation gap may not be a function of the "quality of the model" (or the algorithm used to solve it, which is a separate dimension). I had an experience with a logistics problem where the model was a simple shortest-path network model and the solution method was Dijkstra's shortest path algorithm. Both are time tested and thoroughly accepted. The ...


12

I'm going to assume here that we are talking about papers utilizing OR concepts (model, solution procedure, some sort of consequences of interest to the readers) as opposed to "pure OR papers" (theorem-proof, metaheuristic races, ...). Many of the journals in supply chain management and related disciplines (operations management, logistics and even sourcing)...


11

Just to add one more point to what @LarrySnyder610 said above about specific problem-driven projects of enterprises: Since many of industry OR problems are driven by specific use cases, some of the simplifying assumptions in academic projects are not acceptable. I don't mean the solutions to those simplified (academic) models are not applicable, but they ...


11

Disclaimer: the following is doomed not to be comprehensive. (I'm still cautiously optimistic about it being at least somewhat correct.) Some OR research is devoted to theory: proving that some problem is NP-weird, finding worst case bounds on either computation time or the gap between achieved solution and optimal solution for some algorithm, and so on. ...


10

You asked two questions at the end and I only try to answer the second "why is it [implementation gap] not always included anyway": If I understand your question correctly, I'd say there can be many reasons why such an "implementation gap" that you refer to doesn't exist or reported. For example: The academic studies are not necessarily implemented. Why? ...


10

Another source providing this information is google scholar. Here is the ranking for mathematical optimization journals. Also as @Chang pointed out here is the ranking for operations research journals. As per one's subject of interest; after a little search, one can get the ranking for other areas as well e.g. game theory and decision sciences could be ...


10

Sharing the same reservations on journal rankings I can add some more which are particular relevant for hiring decisions in Germany and Europe. The German association of business administration professors maintains the following journal rankings for different categories: OR: https://vhbonline.org/vhb4you/jourqual/vhb-jourqual-3/teilrating-or/ OM: https://...


9

TL;DR version For vehicle routing problems (which is a subset of OR), academic works focus on getting the best possible solution cost for overly simplified problems, industry focuses on getting workable solutions to much more complex problems. Long version I can't comment on wider OR but I can comment on vehicle routing problems. I've worked as a CS ...


9

As far as I can tell, industry demand for people who check the "OR" box (whether they are labeled as OR, industrial engineering or management science, or "analytics" with a clear ability to go beyond basic data-molesting) remains strong. As long as there are jobs, there will be majors, and as long as there are majors, there will be departments. (Occasional "...


8

You can use BARON and Knitro for free from the NEOS Server. Also, you have the following options: If you are in the first year of graduation, you can apply for a one-year free graduate student license of AMPL which includes both solvers. If you are an individual with a CMU or UIUC email address or related to sponsors of the CAPD you can have a free academic ...


8

In my opinion, OR is definitely broadening in scope, but let me try to be a bit more concrete (N.B.: all of this is my opinion, and I hope others will add their views as well): OR is a relatively "old" field, with the classical problems (scheduling, traveling salesman problem etc) studied by many thousands of people. In addition, OR is a very applied field, ...


8

Theoretical Biology and Medical Modelling Journal Description Theoretical Biology and Medical Modelling is an open access peer-reviewed journal adopting a broad definition of "biology" and focusing on theoretical ideas and models associated with developments in biology and medicine. Mathematicians, biologists and clinicians of various specialisms, ...


7

I only have first-hand experience of the government side, which limits my ability to compare, but since none of the other answers discuss that side of things it may be useful to have an answer from that perspective. Caveat: "government" is a large and complex beast with many different faces. My experience comes from working in a statistical agency; somebody ...


7

First question: Yes, your algebraic formulation is correct. Second question: I would lean toward using the algebraic formulation, for two reasons. First, it is not solver-specific. Second, a reader not familiar with indicator constraints will find interpreting the algebraic formulation a bit easier, while a reader familiar with indicators is not likely to ...


7

The problem is, even if you use the same number of threads, a 'rigorous' comparison of different implementations in different programming languages - for example, in terms of running time or of the quality of the solution found in the same time limit - can be quite challenging or tricky. Citing Chapter 18 of Ahuja, Magnanti & Orlin1: "The existing ...


7

You mention data pre-/post-processing. If what you propose to do is in the context of data science (for instance, a matheuristic for outlier identification to be embedded in some statistical data-torturing exercise), I don't see any problem at all. If what you propose to do is not specifically related to data science (for instance, a heuristic for solving ...


6

Personal opinion: I think it is fair to use as many threads as makes sense in context. For instance, when I use CPLEX I believe it defaults to four threads (one per core on my PC). If I'm solving a problem that is a particular memory hog, I may throttle back to three or even two threads to avoid memory problems. I have no compunction about comparing any of ...


6

You can also download our Octeract Engine - it's free for students and academic staff and solves a superset of the problem types that BARON and KNITRO do (global optimality for non convex problems + trigonometric & nonsmooth & discontinuous problems).


6

For having spent 10 years in Academia/OR and 20 years in the industry side of the force, I noticed that one difference is often the incremental aspect of the problem you have to solve in the industry. Imagine you want to schedule bus drivers for a city bus transit system. Most of the academic work is based on a global optimization question, with an objective ...


6

Journals focused on electricity/power systems often publish OR papers. Examples include IEEE Transactions on Smart Grid and IEEE Transactions on Sustainable Energy.


6

I would say Structural and Multidisciplinary Optimization with the following description taken from Springer. Journal Description The journal’s scope ranges from mathematical foundations of the field to algorithm and software development, and from benchmark examples to case studies of practical applications in structural, aerospace, mechanical, civil, ...


6

I'm going to interpret "in OR" as appearing in OR journals and/or written by people who identify as OR/MS/IE researchers. I'm a bit familiar with the intersection of optimization and statistical estimation. Machine learning, OLS and LAV regression, lasso regression etc. all rely on solving optimization problems to fit models. In addition, ...


4

There is a very new OR journal with a very broad scope: SN Operations Research Forum In addition to the journals listed in the other answers, there are some military-themed journals: Military Operations Research Journal of Defense Modeling and Simulation: Applications, Methodology, Technology Journal of Defense Analytics and Logistics These may ...


4

Something that has really become interesting to me is the creation of software in both of these contexts. Specifically as an end user of a commercial solver. In academia often code is written to solve a specific problem or explore potential solutions. This perhaps affords the ability to do more fundamental work. Like the exploration of how solvers work or ...


4

It's unlikely that you will find a generalised study on this, as the predictive power of an optimisation model depends on the accuracy of the data we use to build the model, and on the skill of the modeler themselves. This is also compounded by the fact that the predictive power of a model is also dependent on the solver we use, in particular on whether the ...


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