r/OperationsResearch

Hello everyone. I'm sure you've gotten a gazillion questions like this but I'd really like to know. Is this a good long-term job? I'm currently in my last year of high school and probably the only one in my school that hasn't chosen a career.

This is what I'm looking for and Operation Research Analyst is the closest one to what I'm looking for (according to Gemini 😅):

High Salary early on: basically making bag in my early 20s unlike doctors who peak when their hair turns grey 😅
Good Work-life balance: cuz I need time for myself
Passion: I like math and science, and in science I particularly enjoy the solving side

As unbiased and honest as possible, if you could go back to you younger self, would you recommend this? Is it a fun job? Is it super inflated or hard to find a job in? Is the entry salary really as high as the internet claims?

For context:

- I love math and I'm pretty good at it ngl, but I'm no genius. Just incase this is the type of person this jobs needs. (cuz i heard you gotta be a real genius for some of these jobs).

- Also both my parents are in the medical field so I've seen first-hand how draining their jobs are just to sustain our comfortable life. So I'd like a math/science related job that comes with the comfortable life like that of a doctor, but without the life-sucking part. High-salary and comfortable for a family, but not life draining and unsustainable. Is this it?

(I apologize in advance if my post is unrelated to this subreddit's theme)

I recently worked on some pure game theoretic and information design proofs. After I showed it to my Mathematics professor, he was amazed. He told me that the proofs are solid and recommended that I publish this as a short note to a reputed journal.

He suggested two primary options for now: Economics Letters, and Operations Research Letters. He emphasized to try at Economics Letters first as he sees it as more prestigious and only try Operations Research Letters as the 2nd option, though he thinks that my paper fits both very well.

I also plan to apply for PhD in Operations Research and Industrial Engineering programs in the future. Will a publication in Economics Letters even be seen as something good in these communities?

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.

Hi everyone, I'm completely new to Operational Research, so I apologize if these are basic or even silly questions. I've been trying to understand the field by reading online, and from AI, but I'm still struggling to figure out what it actually looks like in practice. Because of that, I'm not even sure whether this is the right field for me.

I have a few questions:

1 What does an Operational Research professional actually do on a day-to-day basis?

2 How is OR used in the real world? Which industries or companies rely on it the most?

3 Is OR mostly theoretical, or do you spend a lot of time building practical solutions and working with real data?

4 If I study OR, am I mostly limited to OR-specific roles, or can I transition into other fields later (like Data Science, Machine Learning, AI, Software Engineering, Supply Chain, Finance, Quant roles, etc.)? How difficult is that transition?

5 What programming, mathematics, and software tools do professionals use regularly?

6 What kinds of problems are considered "good OR problems" that wouldn't normally be solved by a software engineer or data analyst?

7 If you could go back to when you first started, would you still choose OR? Why or why not?

I'm trying to decide whether this is the right direction for me before committing to it.

Let’s consider the warehouse of a 3PL logistics company. There is a route optimization project aimed at minimizing the walking distance for pickers who gather orders, and I need to code it in Python. However, there are some areas within the warehouse that are impassable, such as columns, fire extinguishers, and stairs/elevators. In your opinion, in what data format should I provide these obstacles to the optimization model? A graph-based approach seems like the best option, but I’m not sure how to model the physical obstacles. Because these workers won’t be able to navigate around these physical obstacles, and I’ll need to map out a walkable path so that I can optimize the routing along that path based on the locations of the orders on the work list. Does anyone have any ideas? There are about 2,000 items on a single job list, and the workers pick up the orders using large hand carts, similar to shopping carts. For such a large-scale optimization problem, should I try heuristic methods or other approaches like MIP, MILP, etc.? I’m open to suggestions.

So I want to ask about your experience in MOR. About me: I have done BCA, not very good at maths.

If any of you currently pursuing MOR at du, Could you tell me how good is its course curriculum?
I have heard students regretting the course being math and stats heavy and not practical for jobs, although I know MOR has to be maths heavy as it comes under Quants. However, to get jobs in this market, it should also be job oriented.
What job opportunities and roles students get after MOR? and which companies visit the campus for placements? I checked their LinkedIn placement cell, they have shown 20+ companies, and I doubt it. What’s the reality?
That’s all I want to know. Thanks.

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?

Hey everyone. Sorry beforehand if this is a silly question, but I just recently started to learn about these fields. I am not in college yet. I took a gap year to figure out what I want to study before going to college. I have looked at other threads, asked AI, and googled a lot, but still can’t fully picture how these field related/different from each other. All I see is “optimizing processes”. I want to know how did you learn about them. Did you read any books? Or is it something else? Thanks in advance!

Hi all, I’m fairly new to operations research. Just wondering, how do you do cost benefit analysis when it’s not a quantifiable benefit (or cost)? For example, how to factor in the wider social benefits or morals or happiness/satisfaction?

Hi,

I once tried to open an 8 GB .lp file. My system hung, and the editor occasionally crashed. Even files around 1 GB feel slow to open.

I was wondering if this is a common problem, so I'd like to understand a few things:

1. Do you open .lp files to understand or debug your models? If so, what tools do you use?
2. How often do you encounter large .lp files? By "large," I mean files that take a long time to load, cause your system to hang, or crash your editor. If you remember the file size or model size (number of variables, constraints, and nonzeros), please share.
3. Does anyone work with mathematical models and solvers without ever needing to open or inspect .lp files?

I was asked to optimize routing in the “mezzanine” warehouse system of a major 3PL logistics company. Here, pickers who gather products take a mobile cart and go to the relevant locations to retrieve the orders listed on their work orders. We have a Gurobi license. Do you think we should proceed using MILP? There are obstacles such as columns and fire cabinets in some aisles. Generally, the mezzanine structure has five levels, and I need to account for special conditions, such as exit points to the next level. It doesn’t appear to be a classic TSP problem. We requested the x, y, and z coordinates of the locations as data. What other data do you think we should request? Aside from MILP, do you have any other suggestions?

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?

Is there anyone with a solid background in MPC or related expertise? I need a serious discussion

Hi everyone! We made maintenance optimization model for repairable deteriorating systems under imperfect preventive maintenance and free repair warranty. The main goal of the study is to determine a maintenance and replacement policy that minimizes the expected user cost rate, considering downtime, warranty expiration, and post-warranty costs. Is there someone here can help me review the model we created and gives critic? Thank you.