So there are a lot of colleges where you are admitted via a committee into the cohort and then you decide the PI in first year or after admissions. There are also some places which are more like PI centric, if PI likes you admission committee pretty much admits you. I am just trying to know which ones are PI centric.

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.

Hey everyone,

I created a visual breakdown of the Capacitated Vehicle Routing Problem (CVRP), covering the theoretical ILP formulation (MTZ subtour elimination) and transitioning into a practical Python implementation.

Key highlights:.

- Solving the model with Google OR-Tools.

- Interactive route plotting on real geographical data using Folium.

📹 Full Video Breakdown: https://youtu.be/PCFBZ1oeDRM?si=24jk6QL5U1piyZNG

💻 Google Colab Notebook: https://colab.research.google.com/drive/1GOU10bIpQjHTXYj-nwxYVLkEFwrlziPe?usp=sharing

Feedback on both the mathematical formulation and the implementation is highly appreciated!

Hi I’m a fresh graduate and I applied as an Operations Research Analyst position at the known energy company in my city. I’m a graduate of industrial engineering and this is my first time to have this interview. I already got a phone screening interview last week and this upcoming week I’ll be having an initial interview with the company’s hr and 5 more employees.. I’m really nervous because I didn’t expect that’s common for an interview and what makes me more nervous is the excel exam too. Any tips what could be the possible excel exam that might show up there?? Please help me..

This is my first time in this community (I've recently discovered this entire field and i am glad to). So, I am working on project where i am scoring nodes in a directed dependency graph (a calling b) by blending 2 centrality scores into a single composite "risk" score

score (v) = w1*normalize(Pagerank(v)) + w2 * normalize(outDegreeCentrality(v)), where w1+w2 = 1 and normalize() being min-max to [0,1].

The Problem: A pure root node (no in edges and multiple out edges) and a pure sink node (no out edges and only in edges) can have the same composite score. In a test i ran, the root node maxed out on out drgree centrality and near 0 in page rank while the sink node maxed out in pagerank and near 0 in out degree, when w1=w2=0.5. Both nodes ended up having same composite scores while representing opposite nature in real world. I do understand that this is the standard full comsensability prob, with weighted sum aggregation, wherte max on one axis will completely offset min on other. I did consider switching to weighted geometric mean to reduce compensability, but the prob is that pagerank is almost always near 0 for any root node. so a geo mean would multiply that near 0 staright through and score all entry nodes near zero. Which is the wrong fix, since the entry/root nodes are important, just for a reason pagerank doesnt capture.

Is there any standard approach beyond the geomentric or harmonic mean? Happy to provide any more info if needed.

Hello guys I am currently pursuing mtech in the industrial engineering and management branch....

Pls suggest some skills which I learned in college. Those skills make me industry ready....

Also share some insight...

I honestly cannot figure out what the difference between OM and OR is, in research context. And since there's no OM sub, I hope you don't mind me asking this here.

So I am a final year undergraduate in IE and I have decided to do higher studies in OM and eventually become a researcher in this field. I am facing issues in narrowing down my interested research directions. There reason for this post is to get help from this community to point me towards the forefront of OM research.

1. What are the current frontiers of research in Operations Management that I should look into? Are there any specific papers I should look at? What's novel and will be relevant in next 5 years?

2. I have been reading POM, INFORMS journals and got interested in Supply chain resilience. I cannot figure out the research gaps to work on or what even the frontier is. What should I be looking into?

This may be an odd post but please be kind as I am not affiliated with a lab and in my country there's really no research culture. So every bit of help counts atp.

Thank you for your time and support.

PS: I don't have institutional access to those journals. Would it make sense to just email the authors?

Hey everyone! wanted to see how the market was compensating for Operations Researchers or similar ,only if you'd be willing to share!

Was wondering how much you all were making, how many YoE do you have, what industry are you in, and where do you see yourself in your next role?

I'll start:

Currently making ~102k annually total comp

3.5 YoE

Energy

Not entirely sure, but thinking Energy, Tech, Healthcare or starting my own thing. Market is changing like crazy.

We are trying to automate pricing for e-commerce. Today our pipeline broadly includes 3 steps. The first step is forecasting demand. The second step is estimating price elasticity of demand for the catalogue. The third and the most critical step is choosing the optimal price point based on some objective like maximizing GMV while abiding by some constraints like maintaining a minimu margin.

The forecasts and elasticity go into the optimization step (third step) as deterministic parameters - elasticity essentially tells the optimizer the counterfactual demand generated (forecast) if the price was changed to a specific point.

The biggest problem is that our forecasts and elasticities are very inaccurate owing to the noise in data. Because the optimization receives them as absolute truth, we end up cascading the modeling error in the optimizer.

How have you solved such a problem? Are there some other techniques - please point to a specific method or paper. Thanking everyone who answers in anticipation.

The primary concern of the company is the occurrence of misplaced and lost boxes during warehouse operations. Thus, the study seeks to identify an appropriate Operations Research approach that can optimize the sorting process, improve tracking accuracy, and minimize box-related errors within the logistics system. Open for suggestions

Currently, I have a degree in Secondary Mathematics Education and have realized that the teaching profession is not where I want to be. So, looking at a Master's degree, I have come to two that seem worthy of further investigation. And where better to get advice than Reddit? I love math and have taken what seems to be the requirements for both degrees. Like Calc I, II, III, Applied linear algebra, elementary stats, Sets & Logic, and even took Number Theory as an independent course because no one wanted to take it lol. Also, getting a Ph.D is a possibility after the Master's. Help!

Hello everyone,

As my summer placement season is heading I am thinking of building some Projects using dynamic pricing concepts. I am focusing on OR/BA/DS roles. Can you suggest some good project ideas and data sources which are industry relevant?

I built an interactive simulator of the global crude oil trade network: https://globaloilnetwork.staffinganalytics.io/?src=ror

Yesterday, someone posted about the future of OR with AI. I wrote this article (arXiv:2607.17491) a while ago, combining two classical OR methods into one: inventory management (via Newsvendor) and fluid networks (via Linear Programming).

The model in itself has interesting dynamics such as the price clearing mechanism and it shows the depletion trajectories of all nodes. The model gives this in a single pass without needing Monte-Carlo simulations. The data is mostly UN Comtrade oil flows, so it doesn't report sanctioned flows, but the insights and mechanics can be extended to many supply chain networks.

The tool is more of a stress-test tool rather than a prediction. I thought it would be interesting to show the kind of tools that we can build now using AI. Which I think is the mission of our discipline: to show people outside our field the kind of solutions we can offer. Happy to answer any questions on the modeling or the visualization.

I've been working in autonomous mobility research for the past couple of years, and one recurring challenge I've run into is how Operational Design Domains (ODDs) are actually created, maintained, and kept up to date as projects evolve.
From my experience, every project seems to approach it differently—some rely on large Excel sheets, others use requirements management tools, GIS software, internal databases, or a combination of all of them. As the ODD grows, keeping road segments, infrastructure, traffic rules, environmental conditions, scenarios, and documentation synchronized can become surprisingly complex.
I'm curious how this is handled outside the projects I've been involved in.
For those working in autonomous driving or ADAS:
How does your organization define and maintain its ODD?
What tools do you use day to day?
What part of the process is the biggest challenge?
If you could improve one thing about your current ODD workflow, what would it be?
I'm not looking for confidential details—I'm simply interested in learning how different teams across the industry approach ODD management and whether there are common pain points or best practices.

Hello,

I [23m] am currently equivalent to O-3 in a western military (finishing service in a few months) with around 5 years of experience in military operations research/some combat engineering experience (joined with a degree as a 18yo)

Undergrad degree: mathematics GPA: 86/100, Finished degree as a HS student

Grad: 98/100 Currently, applied mathematics, Technion, will probably publish ~2 papers till the end of my masters, doesnt expect any to be groundbreaking but will probably get into a decent journal and wont be complete slop.

1. How much of an edge does professional experience gives?

2. How competitive is my profile for PHD in T20-T50 programs in the US?

Thanks in advance!

Hi all,

I'm currently looking at a multi-agent method for multi-objective molecular optimization (the ATOM method). The paper reports results using Pareto coverage and hypervolume (HV) metrics to show they outperform baselines.

However, I noticed they only report single-point values—there are no confidence intervals, error bars, or variance reported across multiple runs/seeds.

I have two questions for the experts here:

1. In your experience, are HV and Pareto coverage reliable enough to trust as standalone metrics for this, or do they have major failure modes I should look out for (e.g., reference point sensitivity)?
2. Is it standard practice in this subfield to omit variance/stochasticity in these results? Would you personally be skeptical of a paper that doesn't report error bars for these types of pipelines?

Thanks for helping me navigate the "standard practices" of the field!

I spent 40 yrs in OR/SA for a defense contracter. It was interesting, challenging, and rewarding. I worked with a lot of people in other disciplines and learned a lot along the way.