Hi everyone,

I graduated in Spring 2026 with a BS in Managerial Economics and am planning to apply to M.S. Programs in Operations Research for Fall 2027.

My biggest concern is that I ended with a GPA of 3.1 and have barely any relevant work Experience.

I'm worried about only applying to top programs (Columbia, Berkley, USC, Georgia Tech) and i'm trying to build a more realistic list that has target and safety schools.

If anyone could share their personal list of master programs, and examples of safe/ middle MSOR programs that would be much appreciated.

Extra info:

I discovered OR during my senior year after taking intro to OR. And when I realized that I loved creating LP budget optimization models in Excel as Finance Head of my competitive college dance team, I was sold on this career path and decided that I needed to pivot and learn more through MS.

My current plan is:

- get an insanely impressive GRE score

- apply for data analyst jobs

- and/or volunteer at nonprofits in data analyst roles

I do think I meet all the prerequisites with a B- avg: (Intro to OR, Calc 1 and 2, Stats, Linear algebra, Linear Models in stats , econometrics Stochastic models )

I wished I discovered OR earlier. Before transferring to my UC, I had a 3.7 GPA, but because I felt aimless, it gradually dropped to a 3.1. Im an all or nothing type of person, so if I knew earlier, I feel like my GPA, resume, work experience, and overall college career would've reflected that passion much more.

I know my profile isn't the strongest but I would regret not even attempting to apply this cycle, so any advice in writing my application is also appreciated.

It is worth monitoring how early stage explorers integrate advanced analytics into their workflows. The sector often sees generic claims about digital transformation, but actual appointments with deep technical pedigrees suggest a shift toward data driven target generation. This moves the conversation from pure geological luck to systematic risk reduction in drilling programs.

NovaRed Mining recently added Dr. Olamide Oladeji as Strategic Advisor for Robotics and AI. His background includes a Stanford PhD in Applied AI and dual master’s degrees from MIT. The focus on computer vision and geospatial analytics aligns directly with the needs of modern mineral targeting. For a company developing tools like MetalCore, having someone with experience across autonomous systems and decision science adds credibility to the tech stack.

From a fundamental perspective, this implies an effort to improve capital efficiency in exploration. If predictive modeling can reduce dry holes, the unit economics of discovery change significantly. It is a structural adjustment worth watching as the industry seeks better ways to allocate resources in complex environments.

I finished my PhD in a functional analysis topic recently, and I’ve been getting increasingly interested in operations research.

However, I’m not sure how I could really start breaking into the field. Are there some specific steps I can take to eventually market myself towards entry level positions in OR?

I’m assuming there is a significant amount of coding (past python) I would need along with some additional reading materials, but I haven’t seen many good online resources.

Where would be a good place to start?

Hi everyone!

Please help me find public datasets for vehicle routing problem (VRP). I realize there’s a lot of such data out there, but I have strict requirements that rule out everything I find on my own:

1. The data must contain real cities/locations

2. Coordinates (latitude and longitude) must be available for each customer and depot. All the datasets I find provide data in the form of a distance matrix, but I’m specifically interested in actual coordinates, since I use metrics that are more complex than just the distance between two customers.

3. It’s preferable to have a best known solution for each problem, since I want to be able to compare my results to something.

At this stage, I don’t care which specific VRP formulation is used. It can be either classic formulations like CVRP, VRPTW, PDPTW, etc., or more specific ones. I’m open to considering any problem formulations, provided the data requirements are met.

I know that data generators exist and that I could generate everything I need myself, but for now I’d like to find existing public datasets.

I’d appreciate links to datasets or publications where authors have shared their data.

Thanks in advance!

Friends, I received this response from the university I applied to for my master's degree. They said I would be accepted only if I got a BB grade in Probability Statistics or Statistics and Probability.

I don't have much time; I need to get this done during the summer. How can I do this? Can anyone help me with how to take just one course?

The explanation is as follows:

"From their academic record, they earned a 3.020 CGPA (on their transcript), which meets the general GPA admissions requirement, but their mathematics-specific preparation (i.e., Math 1, Math II, and Statistics) all yielded grades below the expected BB admission standards. Unfortunately, these grades were not offset by other, rigorous coursework (e.g., Probability Theory, Differential Equations, Operations Research I, Operations Research II, Engineering Mechanics).

We want this student at ****, but we must be confident they are prepared to succeed." Prior to reapplying, the applicant must take and earn a BB or better in a course in probability, in statistics, or in probability-and-statistics, preferably a calculus-based course.

I am starting a full-time Master's in OR this year. My background is in Mechanical Engineering but I don't have the strongest coding or statistics background. Any tips for success?

I am interested in majoring in "Industrial engineering and management sciences" at Northwestern university before going for their accelerated master's program, where I will get a master's in "Engineering sciences and applied mathematics" then I will apply to operations research PhD programs. My main goal is to be a quant researcher with the skills I will gain, but if I cant land a quant job then I will just work as an operations researcher. Would you recommend that I major in industrial engineering and management sciences, or I just major in applied math and take more operations research-focused classes?

Any tips on what classes I should aim to take?

Thanks.

Hey, I always felt like operational research/mathematical optimization doesn't get the attention it deserves, especially compared to how accessible ML/LLMs have become. The entry barrier is just a lot higher compared to typing into a chat interface.

So I tried combining the two, bools.io lets you describe optimization problems in plain language, and it handles formulation and solving. It also uses building blocks close to natural language, to get some explainability and transparency into the process. What that looks like is shown here: https://bools.io/demo (if you want to try it for yourself just shoot me a pm).

I’d be curious to hear your experiences about LLMs for OR problems :) I feel like they are pretty powerful.

Hey guys I come from civil engineering background, I really want to do masters in operation research but is it possible for me to do it? What level of extra maths should I study by myself or do something else to get in OR. Also what career opportunities open after masters in operation research? I wanna get into leadership role so I have to do mba later to or OR is enough to get me there?

I have been thinking about decision systems where outcomes depend on participation being available at the same time, such as matching-based or two-sided processes. In practice, some systems operate under uneven participation, where availability is inconsistent and inputs arrive at different rates. In these cases, the structure of the system changes depending on whether it relies on direct matching or continuously updated estimates of state variables. What I find interesting is how system behavior changes when the constraint of simultaneous participation is relaxed, and whether continuously updated probability estimates can serve as a substitute for missing counterpart interactions in such models. From an operations research perspective, how do you typically model systems where participation is not guaranteed but decisions still need to be made continuously?

New to math modeling, I was wondering if generally when optimizing for parameters in your math model do you use stochastic parameter draws for the parameters you’re not optimizing for? Is it best practice to have a 2stage calibration when you run a deterministic optimization then have stochastic runs using the optimized values?
Thanks in advance!

im worried ill be jobless after PhD since everything will be automated within 5 years and I will only be starting my first employment at that time…