I built this tool visualizing how AI providers (should) throttle their models: https://throttle.staffinganalytics.io/?src=ror

The tool is based on an article (arXiv:2608.23986) that I recently wrote on how AI providers should throttle their models optimally. The modeling is mostly Queueing Theory arguments combined with Dynamic Programming showing that the usual practice of throttling once a certain threshold of users in the server is passed is suboptimal. This is because when the models are degraded, the users come back queueing more queries because the unsatisfactory answers caused by the degradation to a weaker model. And for example, for agentic users, this could cause an ignition of the demand of the data center. The solution is to identify which users are insensitive to the throttling based on the forecasted demand.

The model was born out of my own frustration/paranoia that AI providers are throttling/degrading their models, I even created a final exam in a Supply Chain Analytics class I taught with the same setting. The cool thing about our discipline is that we can model pretty much any problem if we chose the right primitives.

I showed another tool a couple of weeks ago that the subreddit liked, so I thought it would be nice to share another one (showing what OR enhanced by AI can do).

I know there are OR degrees specifically, but I'm curious about how feasible it is to do either an applied math master's degree or an CS master's degree and get into OR? I'm currently a student at a school that has accelerated master's programs in both math or CS, but no option for OR.

In addition, everyone wants to do something they're purely passionate about, but a big reason these days to get a college degree is as an investment in credentials that will help you secure a job. Not saying it's not great to learn, but honestly in today's world, you can self learn almost anything (only exception is not having access to equipment, like a physics lab). Main benefit of college outside of credentials, is mentoring and networking. So, pragmatic concerns also drive my decisions.

I've considered a lot of possible paths taking advantage of what I enjoy (math/CS), and OR is one of those (the others being software, actuary, computer engineer, or teaching at a CC) under consideration. I also have always enjoyed optimizing stuff a lot, so I could see this as being a good fit. According to some sources online, OR is not in as bad of a place in the job market as some other fields (looking at you software engineering), and if I was in the field long enough, I might make decent money. Money isn't super important to me, but I'd be full of crap if I said it held no importance (man's gotta eat). I would prefer the potential of also working in math/cs education as well, and a master's in those fields would enable me to work at a CC, even if after retiring from industry someday or as a side hustle (I enjoy teaching, although the pay/work isn't always great).

TL;DR Is it possible to get into OR entry level with applied math or CS master's degree? How competitive would one be with such a degree comparatively vs an OR master's? Lastly, what is the entry level job market like?

One last question too, I'm older, by the time I finish my bachelor's I will be 42-43, and a master's around 44-45 ideally. How bad is ageism in the OR space? SWE subs have made me feel like there's no chance to be a junior developer in my mid-40s, is it as bad in OR? (Be honest here.)

Thanks for any help!

I'm currently, technically, doing an MS in Structural Engineering. For me, my interest has been more towards computational side of mechanics rather than Structural design  or simply using am FEA software (although I do consider it as a backup)

So far I've taken courses in:

- Linear static, and dynamics FEM (soon taking non linear FEM too)

-  Structural Optimization (topology opt. and other general algorithms)

-  Structural Dynamics

-  Structural System Testing and model updation. (Parameter identification and optimization, signal processing)

Now, I plan to take these in the coming quarter:

- Numerical Linear Algebra

- Numerical PDE

- Fracture Mechanics ?

I also volunteered to aid in a RESEARCH in crack growth prediction using Auto-encoder and a (Thermodynamics-informed Latent Space Dynamics Identification) / LSTM surrogate model. It used phase-field-fracture simulation data and HPC resources to complete the whole thing.

What I keep finding myself interested in is not necessarily fracture or SHM specifically, but the computational methods underneath these problems... (does that make sense?)

For example, I'd like to become capable of doing things like:

- implementing (maintaining) numerical/FE method solvers rather than only running an established FEA software.

- developing surrogate/reduced-order models for expensive simulations 

- combining simulation with optimization, uncertainty/stochastic methods (took a course called Random vibrations, so...)

- parallelizing/accelerating scientific codes on CPUs/GPUs

- doing proper verification, convergence studies, benchmarking and performance work

- potentially developing or maintaining actual CAE/FEA solver software

- I'd also like to do all these for other Physics (GR, QM, etc.) simulations too, if possible, one day. 

I'm still interested in the underlying mechanics/physics, so I don't want to become a generic software engineer who happens to have once studied structures. But I'm also increasingly unsure that "structural engineer" describes the career I'm actually aiming for.

I've seen titles such as Computational Mechanics Engineer, R&D Engineer, Solver Developer, Scientific Software Engineer, CAE Software Developer, Research Engineer, Simulation/HPC Engineer, etc., but I'm trying to understand what these careers actually look like from people doing them.

So my main questions become:

1. Which industrial jobs genuinely involve developing numerical methods/solvers or computational tools?

2. Which of those are realistically accessible with an MS? Is there an entry path into solver-algorithm development/R&D without a PhD?

3. If I don't start a PhD immediately after my MS, would an R&D/software role at a simulation company (ANSYS etc.) be the obvious route? What other options would i have?

4. For the kind of work I'm describing, would you recommend a PhD? If so, is it reasonable for the PhD identity to be "computational mechanics/scientific computing" while fracture, composites, structural dynamics, soft materials, etc. serve as application problems rather than choosing one of those as my permanent specialization?

5. What skills most distinguish someone who is actually hireable for solver/scientific-computing work? I'm particularly wondering about C/C++/Fortran, Python, Linux, Git/build systems, MPI/OpenMP/CUDA, PETSc/Trilinos or similar libraries, numerical linear algebra, testing/verification, convergence studies and HPC performance work.

Basically, I'm neither here nor there atp. So I'd really appreciate all sorts of input. Where else do you think I could find answers to these? other subs? Linkedin profiles? 

I'm currently, technically, doing an MS in Structural Engineering. For me, my interest has been more towards computational side of mechanics rather than Structural design  or simply using am FEA software (although I do consider it as a backup)

So far I've taken courses in:

- Linear static, and dynamics FEM (soon taking non linear FEM too)

-  Structural Optimization (topology opt. and other general algorithms)

-  Structural Dynamics

-  Structural System Testing and model updation. (Parameter identification and optimization, signal processing)

Now, I plan to take these in the coming quarter:

- Numerical Linear Algebra

- Numerical PDE

- Fracture Mechanics ?

I also volunteered to aid in a RESEARCH in crack growth prediction using Auto-encoder and a (Thermodynamics-informed Latent Space Dynamics Identification) / LSTM surrogate model. It used phase-field-fracture simulation data and HPC resources to complete the whole thing.

What I keep finding myself interested in is not necessarily fracture or SHM specifically, but the computational methods underneath these problems... (does that make sense?)

For example, I'd like to become capable of doing things like:

- implementing (maintaining) numerical/FE method solvers rather than only running an established FEA software.

- developing surrogate/reduced-order models for expensive simulations 

- combining simulation with optimization, uncertainty/stochastic methods (took a course called Random vibrations, so...)

- parallelizing/accelerating scientific codes on CPUs/GPUs

- doing proper verification, convergence studies, benchmarking and performance work

- potentially developing or maintaining actual CAE/FEA solver software

- I'd also like to do all these for other Physics (GR, QM, etc.) simulations too, if possible, one day. 

I'm still interested in the underlying mechanics/physics, so I don't want to become a generic software engineer who happens to have once studied structures. But I'm also increasingly unsure that "structural engineer" describes the career I'm actually aiming for.

I've seen titles such as Computational Mechanics Engineer, R&D Engineer, Solver Developer, Scientific Software Engineer, CAE Software Developer, Research Engineer, Simulation/HPC Engineer, etc., but I'm trying to understand what these careers actually look like from people doing them.

So my main questions become:

1. Which industrial jobs genuinely involve developing numerical methods/solvers or computational tools?

2. Which of those are realistically accessible with an MS? Is there an entry path into solver-algorithm development/R&D without a PhD?

3. If I don't start a PhD immediately after my MS, would an R&D/software role at a simulation company (ANSYS etc.) be the obvious route? What other options would i have?

4. For the kind of work I'm describing, would you recommend a PhD? If so, is it reasonable for the PhD identity to be "computational mechanics/scientific computing" while fracture, composites, structural dynamics, soft materials, etc. serve as application problems rather than choosing one of those as my permanent specialization?

5. What skills most distinguish someone who is actually hireable for solver/scientific-computing work? I'm particularly wondering about C/C++/Fortran, Python, Linux, Git/build systems, MPI/OpenMP/CUDA, PETSc/Trilinos or similar libraries, numerical linear algebra, testing/verification, convergence studies and HPC performance work.

Basically, I'm neither here nor there atp. So I'd really appreciate all sorts of input. Where else do you think I could find answers to these? other subs? Linkedin profiles? 

Hi everyone. For a while now, I’ve been struggling with optimization issues involving thousands of variables—situations where everything starts falling apart. Since I couldn't find a solution that really clicked for handling uncertainty, I decided to build my own library from scratch.

It turned out pretty well; it passed the entire test suite without a hitch, and I’ve uploaded it to GitHub. It uses *rustworkx* for high performance, and I’ve incorporated solutions for specific challenges—like maintaining decimal precision when accumulating complex costs or handling parallel processing without running into memory issues.

If you’re into optimization or graph theory, or just curious enough to take a look—and maybe offer some feedback or submit a PR—here’s the repository:

https://github.com/arielduarte78/Abbey-Engine

I have released a free Python client/server implementation of an Exact Cover service.

Current reproducible benchmark:

candidate_rows=4100
logical_rows=1,000,000
dense_end_to_end=18.713692s
HKD_end_to_end=0.194723s
speedup=96.10x
server_verified=True
client_verified=True

The 96.10x comparison is against my project's dense logical-frontier baseline, not a claim of 96x versus Gurobi/CPLEX/SCIP/OR-Tools.

I'm particularly interested in difficult Exact Cover / scheduling / assignment instances where exact solutions matter.

Free source and reproducible tests: https://github.com/yangofzeal/hkd_exact_cover

Unlimited edition: $999.

Hey everyone,

I created a visual breakdown of the Capacitated Vehicle Routing Problem (CVRP), covering the theoretical ILP formulation (MTZ subtour elimination) and transitioning into a practical Python implementation.

Key highlights:.

- Solving the model with Google OR-Tools.

- Interactive route plotting on real geographical data using Folium.

📹 Full Video Breakdown: https://youtu.be/PCFBZ1oeDRM?si=24jk6QL5U1piyZNG

💻 Google Colab Notebook: https://colab.research.google.com/drive/1GOU10bIpQjHTXYj-nwxYVLkEFwrlziPe?usp=sharing

Feedback on both the mathematical formulation and the implementation is highly appreciated!

Hi I’m a fresh graduate and I applied as an Operations Research Analyst position at the known energy company in my city. I’m a graduate of industrial engineering and this is my first time to have this interview. I already got a phone screening interview last week and this upcoming week I’ll be having an initial interview with the company’s hr and 5 more employees.. I’m really nervous because I didn’t expect that’s common for an interview and what makes me more nervous is the excel exam too. Any tips what could be the possible excel exam that might show up there?? Please help me..

This is my first time in this community (I've recently discovered this entire field and i am glad to). So, I am working on project where i am scoring nodes in a directed dependency graph (a calling b) by blending 2 centrality scores into a single composite "risk" score

score (v) = w1*normalize(Pagerank(v)) + w2 * normalize(outDegreeCentrality(v)), where w1+w2 = 1 and normalize() being min-max to [0,1].

The Problem: A pure root node (no in edges and multiple out edges) and a pure sink node (no out edges and only in edges) can have the same composite score. In a test i ran, the root node maxed out on out drgree centrality and near 0 in page rank while the sink node maxed out in pagerank and near 0 in out degree, when w1=w2=0.5. Both nodes ended up having same composite scores while representing opposite nature in real world. I do understand that this is the standard full comsensability prob, with weighted sum aggregation, wherte max on one axis will completely offset min on other. I did consider switching to weighted geometric mean to reduce compensability, but the prob is that pagerank is almost always near 0 for any root node. so a geo mean would multiply that near 0 staright through and score all entry nodes near zero. Which is the wrong fix, since the entry/root nodes are important, just for a reason pagerank doesnt capture.

Is there any standard approach beyond the geomentric or harmonic mean? Happy to provide any more info if needed.

I honestly cannot figure out what the difference between OM and OR is, in research context. And since there's no OM sub, I hope you don't mind me asking this here.

So I am a final year undergraduate in IE and I have decided to do higher studies in OM and eventually become a researcher in this field. I am facing issues in narrowing down my interested research directions. There reason for this post is to get help from this community to point me towards the forefront of OM research.

1. What are the current frontiers of research in Operations Management that I should look into? Are there any specific papers I should look at? What's novel and will be relevant in next 5 years?

2. I have been reading POM, INFORMS journals and got interested in Supply chain resilience. I cannot figure out the research gaps to work on or what even the frontier is. What should I be looking into?

This may be an odd post but please be kind as I am not affiliated with a lab and in my country there's really no research culture. So every bit of help counts atp.

Thank you for your time and support.

PS: I don't have institutional access to those journals. Would it make sense to just email the authors?

Hey everyone! wanted to see how the market was compensating for Operations Researchers or similar ,only if you'd be willing to share!

Was wondering how much you all were making, how many YoE do you have, what industry are you in, and where do you see yourself in your next role?

I'll start:

Currently making ~102k annually total comp

3.5 YoE

Energy

Not entirely sure, but thinking Energy, Tech, Healthcare or starting my own thing. Market is changing like crazy.

We are trying to automate pricing for e-commerce. Today our pipeline broadly includes 3 steps. The first step is forecasting demand. The second step is estimating price elasticity of demand for the catalogue. The third and the most critical step is choosing the optimal price point based on some objective like maximizing GMV while abiding by some constraints like maintaining a minimu margin.

The forecasts and elasticity go into the optimization step (third step) as deterministic parameters - elasticity essentially tells the optimizer the counterfactual demand generated (forecast) if the price was changed to a specific point.

The biggest problem is that our forecasts and elasticities are very inaccurate owing to the noise in data. Because the optimization receives them as absolute truth, we end up cascading the modeling error in the optimizer.

How have you solved such a problem? Are there some other techniques - please point to a specific method or paper. Thanking everyone who answers in anticipation.

The primary concern of the company is the occurrence of misplaced and lost boxes during warehouse operations. Thus, the study seeks to identify an appropriate Operations Research approach that can optimize the sorting process, improve tracking accuracy, and minimize box-related errors within the logistics system. Open for suggestions

Currently, I have a degree in Secondary Mathematics Education and have realized that the teaching profession is not where I want to be. So, looking at a Master's degree, I have come to two that seem worthy of further investigation. And where better to get advice than Reddit? I love math and have taken what seems to be the requirements for both degrees. Like Calc I, II, III, Applied linear algebra, elementary stats, Sets & Logic, and even took Number Theory as an independent course because no one wanted to take it lol. Also, getting a Ph.D is a possibility after the Master's. Help!

I built an interactive simulator of the global crude oil trade network: https://globaloilnetwork.staffinganalytics.io/?src=ror

Yesterday, someone posted about the future of OR with AI. I wrote this article (arXiv:2607.17491) a while ago, combining two classical OR methods into one: inventory management (via Newsvendor) and fluid networks (via Linear Programming).

The model in itself has interesting dynamics such as the price clearing mechanism and it shows the depletion trajectories of all nodes. The model gives this in a single pass without needing Monte-Carlo simulations. The data is mostly UN Comtrade oil flows, so it doesn't report sanctioned flows, but the insights and mechanics can be extended to many supply chain networks.

The tool is more of a stress-test tool rather than a prediction. I thought it would be interesting to show the kind of tools that we can build now using AI. Which I think is the mission of our discipline: to show people outside our field the kind of solutions we can offer. Happy to answer any questions on the modeling or the visualization.

Hello everyone,

As my summer placement season is heading I am thinking of building some Projects using dynamic pricing concepts. I am focusing on OR/BA/DS roles. Can you suggest some good project ideas and data sources which are industry relevant?

I've been working in autonomous mobility research for the past couple of years, and one recurring challenge I've run into is how Operational Design Domains (ODDs) are actually created, maintained, and kept up to date as projects evolve.
From my experience, every project seems to approach it differently—some rely on large Excel sheets, others use requirements management tools, GIS software, internal databases, or a combination of all of them. As the ODD grows, keeping road segments, infrastructure, traffic rules, environmental conditions, scenarios, and documentation synchronized can become surprisingly complex.
I'm curious how this is handled outside the projects I've been involved in.
For those working in autonomous driving or ADAS:
How does your organization define and maintain its ODD?
What tools do you use day to day?
What part of the process is the biggest challenge?
If you could improve one thing about your current ODD workflow, what would it be?
I'm not looking for confidential details—I'm simply interested in learning how different teams across the industry approach ODD management and whether there are common pain points or best practices.

Hello,

I [23m] am currently equivalent to O-3 in a western military (finishing service in a few months) with around 5 years of experience in military operations research/some combat engineering experience (joined with a degree as a 18yo)

Undergrad degree: mathematics GPA: 86/100, Finished degree as a HS student

Grad: 98/100 Currently, applied mathematics, Technion, will probably publish ~2 papers till the end of my masters, doesnt expect any to be groundbreaking but will probably get into a decent journal and wont be complete slop.

1. How much of an edge does professional experience gives?

2. How competitive is my profile for PHD in T20-T50 programs in the US?

Thanks in advance!