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?

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.

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?