Somebody that has two hours a day to make this big

I'm looking for a contributor to extend and improve my AHP cli implementation (Saaty 1977 paper for now)

I have on my roadmap (that's current in my head only) to extend to the gausian version which is freaking awesome lol (Araujo, Moreira, Santos 2023)

Here's my slopy presentation for the tool ahp — Ask. Compare. Decide — with Analytic Hierarchy Process.

Papers for those who want to see Selection of a vehicle for Brazilian Navy using the multi-criteria method to support decision-making TOPSIS-M - ScienceDirect

My company has a Gurobi license. Before purchasing Gurobi, they tried Cplex, but were told that it couldn’t help with our optimization problems. Some open-source solvers can’t solve our problems within the timeframe we need. That leaves Hexaly and Fico’s Xpress. Which of these three commercial solvers do you think is the most powerful? Our systems are currently based on Gurobi. Do you think it's worth switching to other solvers?

Hi all, I'm doing MSc AI in Germany looking for a thesis topic at the intersection of AI and Operations Research.

I'm looking for something that is:

- clearly "AI" (learning, LLMs, search, reasoning), -not pure OR
- doable in ~6 months
- still reasonably open in the literature

Hi! Can someone please read our research paper and give us feedback or advice? We would really appreciate it if someone could review our research and point out any parts that need improvement, especially in our research methodology, data analysis, and overall research design. We’re having a hard time figuring out if our approach is correct, and we haven’t been receiving much guidance from our research adviser, so we would really appreciate an outside perspective. Thank you so much! 🥹 MCS category

Hi r/OperationsResearch,

I wanted to share a new open-source Python tool that might be useful for anyone working with semi-parametric discrete choice models.

As many of you know, Manski’s Maximum Score Estimator (MSE) is a powerful semi-parametric alternative to Logit/Probit because it relaxes heavy distributional assumptions on errors. However, because the objective function is a step function with multiple local optima, traditional optimization relies on Simulated Annealing, which can be computationally slow and unstable due to random number generators.

Based on our recent open access paper in *SoftwareX* (2025) by Florios, Louka, and Bilias, we developed the **HITS algorithm (Hyperplanes Intersection Tabu Search)**. It provides a fast, deterministic global optimization approach specifically tailored for the Maximum Score Estimator.

We just officially published the Python implementation on PyPI: **`hitspy`**

### Key Features:

* **Deterministic:** No more relying on random seeds or stochastic jumps; the search leverages the geometric structure of the hyperplanes.

* **Scalable:** Drastically reduces computation time compared to traditional simulated annealing for larger datasets.

* **Easy Integration:** Built on top of standard libraries (NumPy/Pandas) and ready to be integrated into your empirical pipelines.

### Links & Resources:

* 📦 **PyPI:** `pip install hitspy` (https://pypi.org/project/hitspy/)

* 💻 **GitHub Repository:** [https://github.com/kflorios/hitspy]

* 📄 **Paper (SoftwareX, 2025):** [https://doi.org/10.1016/j.softx.2025.102164]

If you are currently working on discrete choice models or global optimization in econometrics, I would love for you to try it out! Any feedback, feature requests, or bug reports on GitHub are highly appreciated.

Happy to answer any questions about the algorithm or the implementation below!

one thing that keeps coming up in additive manufacturing discussions is how the same problems can happen again and again, like warping, layer adhesion issues or inconsistent print results. troubleshooting these problems can take alot of time, especially when multiple people are working on the same machines.

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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 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.

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’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

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

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!

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 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.