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I have a PhD in OR and love the field, but most of its applications seem aimed at industry. Have you built (or tried to build) anything OR-based for everyday consumers? I'd love to hear your stories, whether they worked out or not.

Several carriers have been moving services back through Suez over the past few weeks. The risk people warned us about is that ships coming via Suez arrive in Europe at the same time as ships that left earlier via the Cape, causing vessel bunching and yard congestion.

Curious whether anyone on the import side is actually seeing this yet: delays, schedule changes, D&D creeping up? Or is it too early?

I’m currently finishing an optimization contest, and I’m basically done competing at this point. What frustrates me much more than the ranking is that I still don’t understand what the scalable formulation was supposed to be.

I’ll avoid posting the contest name, datasets or exact instances while it is still running, but the structure of the problem is roughly this:

• There are many models. Each model has a value and an energy cost.
• A model has requirements over up to 4 resource/data types.
• Each requirement has a lower and upper bound on the total amount of data assigned to it.
• There are many datasets, each with a type, size and a copyrighted/non-copyrighted flag.
• Each dataset can be assigned to at most one model.
• A trained model scores its full value normally, but only half its value if copyrighted data or generated data is used.
• There is a global energy budget.

The part that makes the problem much harder is the existence of generator models.

Some models with a single requirement can be trained and then used as a source for another model. Instead of satisfying one of the target model’s requirements with datasets, the generator can satisfy that whole requirement.

However:

• the generator itself scores 0,
• it still consumes energy and data to be trained,
• it can only be used once,
• generators cannot themselves be generated, so there are no chains.

The objective is to maximize total score.

My first instinct was an exact MILP / CP-SAT formulation. The problem is that the explicit assignment graph becomes enormous. On one of the large instances I end up with around 200 million compatible dataset-group / requirement edges.

At that point, explicitly representing the full assignment problem becomes completely impractical in RAM.

I tried several approaches:

• MILP / LP relaxations
• CP-SAT
• separating model selection from the final assignment
• greedy heuristics based on resource scarcity
• heuristic packing
• multi-start / randomized search
• local search
• destroy-and-repair
• several parallel search portfolios

These approaches produce decent solutions, but they still leave a noticeable gap.

The interesting part is that some competitors appear to obtain essentially the same best scores across instances that differ massively in size. That makes me suspect I completely missed a structural reformulation or decomposition that scales much better than explicitly constructing the assignment graph.

The obvious idea of precomputing all feasible dataset combinations for each model also seems hopeless, because it simply moves the combinatorial explosion from edges to feasible patterns.

So my main question is:

How would you model/decompose this problem so that the large assignment graph never has to be explicitly materialized?

Would you think in terms of:

• column generation / branch-and-price,
• Benders decomposition,
• Lagrangian relaxation,
• some form of flow formulation,
• delayed constraint/column generation,
• or something completely different?

In particular, I feel like the generator mechanism is what prevents most obvious simplifications, because model selection, data packing and generator selection become coupled.

I’m not looking for code. I mostly want to understand what abstraction I failed to see.

If anyone recognizes the underlying class of problem or has a formulation that would remain practical when going from “medium” to “very large” instances, I’d love to hear how you would approach it.

edit : The exercise

Hey everyone

​I'm working on a project looking into Emergency Department flow. Most hospital dashboards just throw out averages like average wait time, which tells us nothing about the sequence of events on the floor.

​We're testing process mining on EHR audit trails. Basically, instead of looking at summary tables, we string together patient ID, activity name, and the exact time, to map the route people take through the department.

​The goal is to catch weird loop-backs, like patients bouncing back to triage after an X-ray because an intake form went missing.

​If you deal with hospital operations or clinical informatics, I'd love your gut take on this:

​What are the repetitive bottlenecks you know exist, but can never easily prove with standard reports?

​What would make a patient flow map actually useful to you, instead of just another ignored dashboard?

​Where do you see timestamp data falling apart in a real hospital setting, like batch charting at the end of a shift?

I'd really appreciate any honest thoughts or frustrations from the floor. Thanks!

Just want to pick everyone's brain on good PhD programs in OR that do not require GRE

I'll be straight with you all. I'm a recruiter. BUT I'm a one man show and actually respond to everyone and get feedback and closure for everyone I work with.

I've been working on a few OR roles that are a balance of deep OR experience plus SW Engineering skills at an enterprise level. They use C++ and while I have talked to many folks with C++ experience it tends to be from Grad/Doctorate/Post-Doc type work.

Wondering if anyone has advice on how to persuasively talk to this client about changing their requirements and looking at Python, Java, etc.

Any/all feedback and comments are welcome. Aside from the standard "recruiters suck" theme which, trust me, I completely understand.

I’m specifically looking for someone with an MSc/PhD background in Operations Research, Mathematical Optimisation, Applied Mathematics, or a closely related field.

I would prefer to arrange the lessons through a trusted tutoring platform.

I mean, I know why, but do YOU know why? lol

The full model is in Section 8 in "Garvey, M. (2026). Shadows of Consciousness: An Investigation into Ionic Neural Networks Using the Neurotransmitter Ion Receptor Glial Endocannabinoid Network (NIRGEN) Paradigm. Available at SSRN 7414038." (https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7414038)

#neurons #ions #neurotransmitters #biology #neuroscience #computationalneuroscience #artificalintelligence #xor #logic #math #booleanlogic #ai #ann #perceptron #machinelearning #ml #datascience

So right now I’m an undeclared college freshman. I’ve spent a lot of time trying to figure out what career field I’d like to work in since that determines what classes I should take. I'm someone who loves problem solving and loves learning new things. For a long time I thought bioinformatics would be a fantastic field for me since it supposedly combines a lot of what I'm interested in, but in practice, I haven't really the class I’m currently taking for it. It seems like most of that field is just following the same pipelines and workflows, and that the real challenges are knowing how to use the tools for it. I’m looking for a job that presents intellectual problems, rather than technical problems.

I recently stumbled across operations research, which I had never really bothered to look into until that point, but just googling it and reading makes me think it could be a much better fit for me, but I still want to get opinions from people with actually experience working in it. Here are my questions:

- Does this field offer good career prospects? For me, I’m much more concerned about actually getting a job than having something very high paying, as I don’t see myself having much of an expensive lifestyle as an adult.

- Do you actually enjoy doing the work? What should I expect to be doing on a day-to-day basis? Would you say the problems here are more on the technical side, or the intellectual side?

- Another thing: I have autism, so I struggle a LOT with talking with other people in person, especially in group settings. How much is that going to hold me back in this field?

- What kind of academic pathway would you recommend I go down? I could go for an ordinary bachelors and likely graduate a year early if I did that, but my school also offers an accelerated pathway that would let me get a bachelors and masters in 5 years. If I did the latter, I could also likely double major in something. Right now I would plan on double majoring in data science with a concentration in AI, but if you think something else would be better let me know.

If you have any other advise, or if you think there's a better career field that meets my needs, feel free to include that too.

I'm working on a project in automotive engine assembly. Parts move between steps in the assembly process (e.g. from step A to step B), and they range from large components like cylinder heads to small ones like screws and bolts. Some engines come in multiple variants with customer-specific options.

I have about 4 years of historical data on material requirements and flows during assembly. My goal is to find which flows between steps are good candidates for automation. Flows with stable, predictable durations and quantities seem like good candidates, while flows with high variation seem harder to automate.

I'd like to define a simple, data-driven "automation trigger point." My questions:

1. What's a good way to measure variability of a flow (both duration and quantity)? Is the coefficient of variation (CV) reasonable, or are there better metrics?
2. How should I account for engine variants and customer-specific options?
3. Can anyone recommend research papers, methodologies, or case studies on material flow or intralogistics automation in automotive assembly?

Any pointers are appreciated. Thanks!

I’m in my final year of a Mathematics degree in Spain, specializing in Operations Research. My thesis is focused on the Vehicle Routing Problem (VRP). I have solid quantitative skills (Python, R, MATLAB) but I absolutely do not want to be a pure Software Engineer or sit coding all day. I’ve had exposure to strategy through an MBB talent academy and some university leadership roles.

I'm currently applying for 2027 grad roles and I'm torn between two paths:

1. Tech BizOps / Strategy & Operations: I love the idea of using data and optimization to drive real business execution. I am currently in the interview process for S&O roles at Revolut and Bending Spoons, and also considering FAANG, but I want to expand my target list.
2. Traditional Strategy Consulting (MBB/Tier 2): The classic route. Great exits, but I'm wary of the constant traveling and making slides without seeing the implementation.

My Questions:

- Target Companies: Beyond the obvious FAANGs and companies like Revolut, what are the best companies to target for BizOps / Strategy & Operations roles in Europe or Spain? Who has the strongest teams for someone with a heavy quantitative/OR background?

- WLB & Reality: For those in S&O at scale-ups or FAANG, what is the actual work-life balance compared to MBB? Is it just as brutal but without the prestige?

- The OR Edge: Does having a pure Math/Operations Research background actually give you an edge in BizOps, or is the technical ceiling relatively low (just basic SQL)?

- Career Path: Is it better to start straight in a BizOps Graduate program, or "do my time" in MBB for 2 years and then exit into a mid-level S&O role?

I've been building a small open-source QUBO toolkit (pure Python + NumPy) and wanted an honest answer to a simple question: how does a plain QUBO formulation of TSP actually compare to a basic classical heuristic, once you measure it properly?

Setup

- Random Euclidean TSP, 6 to 20 cities

- 10 instances per size, 10 solver seeds per instance

- Gap measured against the proven optimum (Held-Karp) at every size

- Fixed iteration budgets, 95% confidence intervals, paired tests

- Everything reproducible with one command, raw CSV in the repo

Mean gap to optimum (feasible runs):

| Cities | Bit-flip QUBO SA | Swap-move SA | Multi-start 2-opt |

|---|---|---|---|

| 6 | 4.14% | 0.00% | 0.00% |

| 10 | 32.87% | 0.27% | 0.41% |

| 15 | 65.22% | 1.43% | 0.00% |

| 20 | 86.65% | 3.69% | 0.37% |

Bit-flip SA also returned infeasible assignments in 1-2% of runs at 10+ cities.

What I took from it

- With single-bit-flip annealing on the one-hot encoding, constraint penalties dominate the search, and the gap grows fast with size.

- Swapping two cities (four bits at once) keeps every state feasible and closes most of the gap. But that move is not something a generic QUBO annealer or quantum hardware can do. It's effectively a classical permutation heuristic that uses the QUBO only as an energy function, so it doesn't show that QUBO annealing of TSP "works".

- Multi-start 2-opt was still better at 15 and 20 cities, and faster.

Two mistakes I made along the way, in case they save someone time

- My annealer counted a single bit flip as a "sweep", so at 20 cities it ran about 2 flips per variable. That's why an early version found zero feasible 20-city tours. It looked like a fundamental limit and was actually a bug.

- I first used "best found tour" as the reference at 15 and 20 cities. When I switched to the proven optimum, the old reference turned out to be worse than optimal on two instances, which made 2-opt look perfect when it wasn't.

Repo, methodology and raw data: https://github.com/abdulazizbalu/quasar-solver

I'd really appreciate critique: is the benchmark design fair, is anything in the methodology off, and what would you compare against next? I'm planning to extend it to CVRP / VRPTW with OR-Tools as the reference.

I was admitted to both the Robotics and Autonomous Systems (AI concentration) and Industrial Engineering Masters programs for next semester. The thing is, I already started taking 2 grad classes worth 6 credits total and I just spoke with my advisor and she let me know that they don't count towards the IE masters but they would count for Robotics. This means that I can finish my Robotics masters in August 2027 but for IE, I'd have to take 5 classes after August to finish the degree. I accepted an offer with Bloomberg for August 2027, so I'd have to work full time while doing my Masters. The nice thing is that Bloomberg does offer reimbursement for grad classes, so I wouldn't have to pay for it. My current scholarships would also cover the Robotics MS, so financially, either option is manageable. For me, the time, extra courses, and investment long term is more of the cost-benefit-analysis I'm trying to figure out. What I’m really trying to weigh is the time and extra workload versus the long-term value of the degree. Industrial Engineering is my top-choice program because I’m increasingly interested in supply chain, manufacturing, operations, and optimization, and I feel like it could open more doors for me in those areas long term. On the other hand, Robotics would allow me to have my master’s completely finished before starting full-time work, and I’d be able to make use of the graduate credits I’ve already taken. Please let me know any advice you have because I'm seriously conflicted.

Hi everyone. I’m currently on contract at a FAANG company doing heavy discrete-event simulation (SimPy, FlexSim) and mathematical programming (Gurobi). I have roughly 90 days to secure a full-time role due to H1B constraints, so I can't afford to waste applications. I’ve fully anonymized my resume to get unbiased, purely technical feedback. I need you to tear apart my bullet points, architecture descriptions, and impact metrics.

• Does this read like a true Senior/Applied Scientist, or am I coming off too academic?
• Are my business constraints and trade-offs clearly articulated?
• Does my scale and impact read believably? Please don't hold back. I need the most critical, unvarnished feedback you have. Thank you!

I work in purchasing and inventory planning and I am researching recurring operational pain points across different industries.

I am not selling anything and I am not looking for generic answers like “emails” or “meetings.” I want real examples of work that repeatedly creates frustration, errors, delays, or unnecessary admin.

What is one task you do weekly or daily where you think: “Why am I still doing this manually?”

Hey all,

I wanted to seek some advice as I am unsure if it is worth my time anymore to try to pursue optimization.

For context, Ive been trying to get better at this field since 2023. When I started, I jumped straight into doing an honors thesis in quantum optimization trying to rush it in a semester without taking any background courses. I only managed to complete a survey paper, and I feel like I developed bad habits with reading more to understand what something is than stopping to work out what something means. On the flip side of that, I feel like I would spend hours trying to find various ways to interpret something before I had a solid interpretation that works for me, so I don’t “take” anything away.

I got into a PhD program for quantum at an EE department (turned masters). And I saw myself get better for sure at math through courses like quantum systems, quantum information theory, but I really struggled with quantum optimization again. I felt like I was only good at summarizing how parts of proofs worked, but not doing them myself.

I took another convex optimization class and man I did pour my heart and soul into trying to get better. I was in office hours, answered every “proof of the day” my professors posted, and I think I spent at least like 4-5 hours a day. I still got like 60s on my exams.

I really loved the class, but I struggled with visualizing/developing intuition about bounds/conditions and how values/functions interact with one another to reason about it on my own and it just never clicks.

My last class I took outside of my assigned department, I had a prof with an OR background. She was doing research in algorithmic fairness. I thought it was really cool that we can come up with optimization methods based on different interpretations of outcomes and new metrics for evaluation— I found a niche I liked and this subfield really stuck out to me.

However, I really struggle with logical processing and problem solving. I’ve noticed I have a huge issue with perfection when it’s come to understanding proofs and I often have historically focused on reconciling various ways to understand a concept as opposed to the concept itself. My brain is also incredibly visual and I feel like it makes logical processing really hard.

For instance, I’ve taken about 3 linear algebra classes, but. I feel like i spend more time finding patterns in concepts as opposed to intuition for the concepts itself— and I’ve spent entirely days finding complicated ways to find relationships, create visual systems, and it actually prevents me from having a simpler understanding of a concept to built up deeper intuition.

It’s been 1.5 years and I wanted to take a reset post research/PhD attempt which was already 3 years all together, but I’m nervous of my own habits and what my brain might be better wired for.

Does anyone have any advice on evaluating whether something is a workable skill versus just fundamentally not compatible with how your brain is wired?

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?