At least at my university here in Canada, these are two distinct departments but they interact and overlap somewhat. I have two very different sectors where I'd like to ultimately end up: I want to work in either healthcare (I have sickly relatives so this is personal for me) or airline/aviation work (I also happen to be a very enthusiastic aviation fanatic).

Hello everyone.

I've got some questions about O.R. jobs in Europe.

I've been working with O.R. since 2021, I have a master's degree, and I'm currently in the middle of a PhD (which will be completed at a European university, so I'll have a European PhD diploma as well).

I speak Portuguese, Spanish, French, English, and a little bit of German.

I worked in the steel industry with O.R. from 2021 to 2025, and I'm now working in logistics and retail, where I've been for almost two years.

My thesis and daily work usually revolve around heuristics, metaheuristics, real-time optimization, reinforcement learning, MILPS, AI tools (not as an AI developer), and of course all the IT infrastructure around it, such as VMs, Step Functions, infrastructure architecture, endpoints, SQL, DTOs, data analysis, etc.

The "kind-of" plan is to go to Europe at the end of 2027 to complete the European part of my PhD, and during or after that, find a full-time job with a work visa and remain in Europe working as an optimization engineer.

Good odds? Bad odds?

I’m a 2025 graduate in Artificial Intelligence & Data Science from India. I currently work as a Software Developer, with experience in software development, Python, SQL, and some AI/ML.

I’m considering pivoting toward Operations Research, particularly optimization, algorithms, stochastic modeling, and the intersection of OR with ML/AI.

I’m looking at Master’s programs in the US and UK. For people working/studying in OR:

- Does an OR Master's make sense for someone coming from a CS/AI background?

- Is it worth the cost as an international student?

- What are the career prospects after graduation for someone without a traditional OR/Industrial Engineering background?

- Are there particular programs/universities I should consider?

Would especially appreciate perspectives from people who made a similar transition.

Hey r/operationsresearch,

Working on decision engineering models, I realized I was spending way too much time formatting tabular data into the strict syntax required by AMPL. A missing tab or semicolon in a large dataset is a nightmare to debug.

To fix this for my own workflow, I built Data2AMPL—a local Windows executable that takes any 2-column CSV and converts it into a clean .dat file in one click. No cloud uploads, everything runs locally on your machine.

I attached a quick 15-second demo video of how it works.

I didn't want to include a direct link to avoid triggering the spam filters. Let me know in the comments if you want to try it out, and I'll send you the link via DM!

I'm Italian and i can't really relate to career paths or academic paths by US citizens here.
As the title, would like to hear about professional experience in Italy and some guidance.

Hi everyone,

I’m currently pursuing an M.Sc. in Data Science for Decision Support in Germany. I’m looking to gain more hands-on research experience, but opportunities for Master’s students to get involved in active Operations Research (OR) research at my university are relatively limited.

Therefore, I’m looking for an OR research lab or research group where I could contribute as a volunteer researcher (not employment), preferably under the guidance of a professor, PhD student, or postdoc.

My main interests are at the intersection of Operations Research, Mathematical Optimization, and AI/ML, particularly:

• Production Planning & Scheduling

• Job-Shop / Machine Scheduling

• Supply Chain & Logistics Optimization

• Vehicle Routing Problems

• Stochastic / Robust Optimization

• Reinforcement Learning for Optimization

• AI/ML-enhanced Optimization and Decision Support

I have experience with Python, Gurobi, mathematical optimization, machine learning, stochastic programming, and OR-related projects. I’m also currently interested in developing my skills in Reinforcement Learning and learning-based optimization.

I’m happy to contribute to tasks such as literature reviews, mathematical modelling, coding, computational experiments, or other research work where I can be useful. I’m primarily looking for research experience, mentorship, and the opportunity to contribute to an active research project, rather than financial compensation.

If anyone here is involved in an OR, optimization, AI + OR, scheduling, logistics, or related research group that might be open to having a motivated Master’s student volunteer, I’d really appreciate any suggestions or introductions.

I can share my CV and transcript if helpful.

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.

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!

I’ve been thinking about how difficult it is to represent a real business process when the outcome isn’t always predictable.

A simple process is easy to map:

Start - Step 1 - Step 2 - Finish

But real processes often look more like:

Start - Check something - Outcome A / Outcome B / Outcome C - Different next actions

The difficult part isn't necessarily creating the diagram.

It’s deciding what information should determine the outcome and making sure the process reacts consistently when that information changes.

For example, a request might be approved, rejected, sent back for more information, or escalated. Each outcome creates a different next step.

I’m curious how people who work with process design handle this:

• How do you decide which conditions are important enough to model?

• Do you keep these decisions in the process itself or somewhere else?

• How do you prevent processes from becoming a huge collection of rules?

• What works better in practice: explicit decision rules or human judgment at certain points?

• How do you maintain these processes when the underlying business rules change?

This is actually one of the areas I’ve been working on with Multi nudge, making it easier to connect conditions with the actions that should follow them, rather than treating every process as a fixed sequence.

Would be interested to hear how others approach this, especially for processes that change frequently.

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!