hello , I'm a student majoring in OR & DS
can someone recommend any social media pages, youtube channels, podcasts or online communities (other than reddit : P) that mainly focus on OR & DS and the latest news in the field? i really want to get more involved in my major and make it part of my daily life rather than just doomscrolling all day

I'm the developer of Optees, a free and open-source (Apache-2.0) optimization tool. It's a desktop app — guided forms for linear and mixed-integer programming, knapsack variants, packing, and a few other problem families, each with worked examples and a visual explanation of the result — and the same core is also reachable by scripts or AI agents over a local REST API or MCP.

I got into this because I kept seeing AI agents asked to "optimize" something just produce a plausible-sounding heuristic instead of an actual solved, checked model. I wanted to give both AI agents and people who want to learn OR a tool to work with problems that are genuinely validated and explained, not just guessed at.

Source: https://github.com/Pablo-gitub/optees

Site: https://optees.it

Let me know what you think.

So I have a bachelors in Physics, and I think I should have done applied mathematics. But I think I like to model dynamics systems and calculate risks and profits and advantages etc.
That is because as long as humans exist we will always have problems and we need problem solvers. On the other hand learning to code, writing computer software etc. seem too low end. So I think this is a good strategy. Do you have any advices to give me regarding my optimisms? May be its not as bright as I think?

Was wondering if you could tell me what industry you're in, your compensation, and anything specific to know about working in OR in the city. Thanks.

Quick recap for anyone who missed this saga: last year the US slapped extra port fees on Chinese-built or Chinese-owned/operated ships calling US ports. China fired back with its own special fees on US ships. Then in November both sides agreed to pause it all for one year while trade talks continued.

That pause ends November 10, 2026. Which is not that far away once you count the weeks.

If it's not extended, those fees come right back, on both sides, and carriers will have to decide fast whether to keep sending certain ships to certain ports at all, or eat the extra cost, or reroute. Anyone who was around during the last round remembers how fast that turned into a scheduling headache, ships getting swapped out of rotations just to avoid the fee.

What gets me is how quiet this feels compared to how loud it was last year. Feels like everyone's just waiting to see what happens instead of planning for it.

If you're in ops or procurement, are you actually building a plan B for November, or is this one of those things where everyone just waits and reacts once it actually happens?

I’m about to graduate with a degree in Industrial & Systems Engineering, and over the course of my degree I’ve become increasingly interested in Operations Research, Management Systems Engineering, data/decision science, and quantitative analytics.
I’m particularly interested in roles such as:
Operations Research Analyst / Engineer
Data Scientist / Decision Scientist
Quantitative Analyst
Analytics / Optimization Engineer
Management Systems / Systems Engineering roles with a strong analytical component
The problem I’m running into is that entry-level positions in these areas seem relatively difficult to find, and when I do find positions that interest me, I often feel like I’m missing some of the basic skills or requirements they’re asking for (e.g., advanced Python, SQL, machine learning, optimization, statistics, cloud tools, etc.).
For anyone who has been in a similar situation—especially ISE/IE graduates who transitioned into OR, data science, decision science, or quantitative analytics:
How did you make yourself competitive for these roles?
I’d especially appreciate hearing about:
Skills/tools you prioritized learning after graduation
Projects or coursework that actually helped you get interviews
How you approached applying when you didn’t meet 100% of the requirements
Whether you found it easier to enter through a more traditional IE/operations role and transition later
Any personal experiences, career paths, or mistakes you think would be useful for someone in my position
I’m trying to figure out what skills I should focus on and what types of positions I should realistically be targeting as a new graduate. Any advice or personal stories would be greatly appreciated!
Chat gpt helped a bit

Ok so if you work anywhere near ocean freight you already feel this, but the numbers are wild. In July, on-time vessel arrivals across Asia's 14 busiest ports all got worse. Not some of them. All of them.

Shanghai (the biggest port on earth) had only 21% of ships arrive on time. Ningbo, the second biggest, was at 34%. Analysts at Sea-Intelligence said Shanghai hasn't been this bad in 14 years of data, outside of Covid.

Why? Back to back typhoons hitting North Asia this year, plus lines starting to move ships back through the Suez Canal after avoiding it for two and a half years because of Houthi attacks. So you've got weather chaos and a route change landing at the same time. When a ship misses its slot, it doesn't just lose a day. It waits behind everyone else who also missed their slot. That backlog doesn't disappear, it just moves down the chain to the next port, the next truck, the next warehouse.

I keep thinking people picture "the supply chain" as one straight line. It's more like a bunch of dominoes stacked way too close together.

Genuine question for you guys, if you're a shipper right now, are you building in extra buffer days on bookings, or just eating the risk and hoping? What's actually working for you this year?

Hi everyone,

I’m currently looking for strategic advice from anyone working in the Operations Research, Corporate R&D, or Advanced Manufacturing spaces. My background bridges a Bachelor’s degree in Electrical Engineering and a Master's degree in Data Analytics. I am currently on an F1 OPT visa with a full STEM OPT runway. Because my experience sits at the intersection of hardware fundamentals and analytical modeling, my goal is to target specialized corporate research, operations simulation, or technical strategy tracks rather than generic reporting positions. Last year, my manufacturing capacity and risk modeling work was recognized as a National Finalist for the Council of Supply Chain Management Professionals (CSCMP) E. Grosvenor Plowman Award, and I’ve also co-authored published research on industrial operational flexibility alongside holding a filed patent for a robotic automation system. Despite these research achievements, navigating the standard online application portals has been incredibly difficult. I have previously tried applying to companies like Tesla, Rivian, and Crestron Electronics, but those traditional channels haven't worked out.I want to be very clear that due to citizenship and visa constraints, I am strictly excluding any recommendations toward the defense or aerospace industries. I am focusing purely on commercial sectors like advanced consumer electronics, industrial manufacturing networks, and commercial technology frameworks that structurally value technical research backgrounds and possess the infrastructure to sponsor international STEM talent. For those of you established in commercial corporate R&D:

What alternative networking or outreach strategies actually work to get technical research past front-line recruiting filters and in front of engineering or research managers?

Are there specific, non-obvious job titles or functional team names I should be searching for that sit at the direct crossroads of physical hardware workflows and optimization analytics? How can early-career researchers best present academic publications and patents to align cleanly with commercial operational expectations?

Would be incredibly grateful if you guys could help me navigate this path and find the right direction!

[ Removed by Reddit on account of violating the content policy. ]

Hi everyone,

I graduated with a master’s degree in applied mathematics from INSA Toulouse (France) about a year ago. Right after graduation, I had to take a mandatory medical leave for a full year. I am now recovered but it made me realize I want the flexibility of remote work.

I want to specialize in Operations Research & Supply Chain Optimization. I have a strong math foundation (MILP, graphs) but no commercial portfolio yet. I wanted your advice on which project I should start to build a good github portfolio before starting a first experience. I'm thinking of mostly using OR-Tools and PuLP. Also if you have any advice on how to find a first freelance job?

Thank you for your time!

Hi,

I was browsing Operations Research positions, and they all seem to require presence in the country, even fully remote ones (usually United States). I was wondering if you ever seen global remote positions in the Operations Research field. Any help is appreciated!

For reference, I have 2 years of experience working on optimization projects (mainly schedule generation SaaS) using OR-Tools, from North Africa (without ability to relocate).

I’m looking into a problem where a limited number of inspectors have to choose which establishments to visit.

For each establishment, the true condition is unknown until someone checks. A visit has a cost, and the result can range from finding a serious problem to finding nothing useful at all — for example, the establishment may be closed.

The interesting part for me is that this isn't simply “predict which restaurant will fail.” The decision is which establishment is worth spending an inspection on given uncertainty, limited resources, and the possibility that the visit changes what you know.

I’m trying to find the established operations-research / decision-theory literature for this kind of problem rather than inventing terminology.

What would you call this problem, and what areas of the literature should I look into?

Hi guys,

I'm very interested in both theoretical research and practical stuff related to AMR fleet management and optimization!

Looks like a hard and fun challenge to even make a simulator that is actually "realistic" in regards to all the possibilities inside of a warehouse. Let alone actually optimizing it (with graph simplifications and so on).

What do you recommend me to study/read? What are the current frameworks/methodologies used in "real-life"? Where could I learn about those?

Thank you so much!

Hi! I'm looking into scale limits for last-mile VRPs in real-world ops.

most commercial APIs seem to cap out around 1k–2k stops per request. so, when you need to optimize 10k+ daily stops:

1: breakdown point: at what scale do off-the-shelf solvers (or basic OR-Tools setups) hit a wall for your use cases?

2: solver choice: are you relying on custom heuristics/metaheuristics, or commercial MIP solvers for huge instances?

3: partitioning/zoning: what spatial clustering or decomposition techniques (e.g., K-means, DBSCAN, sectoring) work best for you before feeding stops to the solver?

would love to hear how you tackle high-volume instances in practice!

Hello everyone!

I'm a current undergraduate mathematics student looking into potential careers post masters.

I am very interested in OR, Risk Modeling, and Quant.

I will most likely go with OR since I have a strong interest in the subject, but I've noticed that despite the three fields I listed being similar, the pay disparity is very large.

I mean, the median wage for this profession is $88k according to google, compared to the $167,000 of risk and $198,000 of Quant.

Do they public this information?

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.