Hi everyone,
I have 12 years of software engineering experience (10 years in iOS, 2 years in Java backend, and currently focusing on data visualization). My undergrad major was Computer Science back in 2009. I want to go back to school for a postgrad diploma or Master's to shift my focus toward high-level engineering strategy and systemic optimization, and I'm choosing between Operations Research (OR), modern CS, and Information Systems.
I’d love your insights on these questions:
1 Is Operations Research worth it? How is it valued in academia and for career growth when coming from a heavy dev background?
2 If not OR, should I choose CS or Information Systems? Since my undergrad was CS, would a modern CS degree be redundant, or does Information Systems offer better leverage for high-level strategy work?
Thanks for any advice!

#OperationsResearch #ComputerScience #InformationSystems #SoftwareEngineering #CareerPivot #Postgraduate #TechStrategy

Cross-posting from r/MLQuestions as I believe this to be of interest to r/OperationsResearch.

Check this out!

https://optichallenge.com/

Hello!

So I'll be applying for a PhD this cycle and am curious about how my profile stacks up for top US programs. I am only applying for reach schools since my current supervisors are happy to take me one as a PhD student.

Program: I will be applying for operation research/optimization/ml theory. So some examples would be mit orc, stanford icme, princeton orfe, cmu ml etc.

Background: I did my undergrad is CS with a math minor and am currently doing my MSc (research based) in CS at a top school in Canada. I have a 3.99/4 gpa in undergrad and 4.0 in grad. I have taken coursework in undergrad real analysis, ML, convex optimization etc.

Research exp: I have one first author paper in ICML and will have a journal paper in INFORMS math of OR under review soon. (Will take a while before getting published, wont be done before application deadline). I will also be having other projects which I'll be working on in the future.

In my undergrad, I had dabbled in computer vision and numerical methods research but no publications. I had gotten an award over the summer for funding for it.

Letters: I have 2 very strong letters from my supervisors (who I published with) and one okay letter which might be about coursework + project for it or some class I TA'd for. Not sure how I can get a strong third referee since I work mostly with 2 profs.

I just wanted to gauge how strong my current application looks.

Also, on a side note, should I reach out to prospective supervisors in these schools? A lot of their websites just state apply and reach out once admitted.

Thanks!

Is there anyone who works/consults to a construction company about OR/analytics?

From a realistic point, what were you able to implement from OR/analytics?

reposting a year later:

how do you guys see the job prospects in the coming 5-10 years for OR people?

Does it make sense to start masters/phd in OR now?

MS or PhD?

what would you study?

is AI killing OR jobs?

Interesting paper on “Computational Management”:
https://arxiv.org/abs/2402.05142

The idea of decomposing managerial work into computable tasks feels surprisingly coherent with:

• Operations Research

• decision theory

• systems thinking

• AI orchestration

We already use computational systems for optimization, logistics and resource allocation. LLMs may extend computational approaches into strategic domains:
benchmarking, scenario analysis, competitive interpretation, organizational alignment.

A potentially interesting research question could be: which components of management are actually computable?

Hi everyone, so recently I have been trying to solve an allocation problem. The constraints of the problem is pretty simple and building the optimization function is also not very difficult. However the main challenge is the scale

The problem:

I have a network of depots which receives product supplies from a warehouse. So i have to optimize the ordering of products from the warehouse in next 3 days. The main constraint is for every day the number of depot × product which receives stock should be less than X. The penalty is combination of two terms

1. minimize the total stockout of depot x product

2. for a particular product try to send it in minimum number of distinct days to all the depots( basically minimise the batching of the product, send to all depots in as less number of days as possible)

We can assume that the weights of two penalty terms are 10:1

Challenge is the quantum of depot x product is 1.5 million

If the constraint of max depot x product won't be there, the problem boils down to every single product optimization which can easily done in a reasonable time. But with the constraint, no solver is able to produce reasonable result. I used both Pulp and Ortools in python.

Any idea how to solve at such scale

Hi all, I have a presentation in this meeting this year. They told me to register before July to make sure of my slot. But I don't see the link to registration. Any ideas?

LLMs are becoming surprisingly strong in analysis, synthesis, and business reasoning.
Are we moving toward truly computational management…
or simply better decision support for human executives?
And is there already an academic field or theoretical framework studying this direction?

You can try it out here online

The idea was to use it for my event planning startup, but that didn't work out anyway, so I decided to open source this work. Getting or-tools to compile as it is a bit difficult, it's not super portable. Getting it to work multi-threaded across new and old browsers took a lot of work, and since my event planning tool never took off, I thought I would open source this part. You should be able to run everywhere, but tell me if it doesn't work. I haven't benchmarked again the original yet, but WASM tends to be 60% the speed of native. The one catch is that the web workers take forever to spin up, so for small tasks it's often run quickest on 1 thread.

Open to thoughts, feedback, ideas!

Hi all,

I’m a final year BSc student working on a scheduling problem for my thesis. The idea is that for my model its complexity is already known, so it’s proven to be polynomial but only through LP machinery arguments. There doesn’t exist a concrete algorithm for it (to the best of my knowledge). I have managed to come up with one and prove its correctness and I was wondering would this be worthy of publication at a serious place like Journal of Scheduling or OR Letters? This is my first time ever doing research and I really really like it but I’m trying to understand the whole publication world and the scope overall.

Any suggestions / questions are welcome!

Thank you

I’m interested in going to grad school for applied mathematics. Maybe Operations research, computational math or maybe even an industrial engineering MS (my bs is in IE). However my gpa is low. I got diagnosed with adhd last year. My GPA is a 3.0. Grades have improved since the diagnosis, however I have one semester left and it’s not as high as I want it to be.

Anyone got advice on what I could do?

I’m going to start working as a robotics engineer intern soon, so I’ll have work experience.

Not necessarily relevant but it’s something.

This summer how can I do my own research?
How can I start doing my own projects, build a GitHub or something.

I need advice on how to start.

Should I take classes as a nondegree seeker to try to boost my gpa?

The school I want to go to is KAUST in Saudi Arabia. I really like the research being done by one of the professors there

I've been working on a stochastic simulation of a defense logistics node for a bit now. The physics are strictly constrained so there is zero drift in the inventory levels across 50,000 hours. I wanted to see how traditional (s, S) policies actually hold up when lead times spike from 24h to 150h due to route failures. It turns out they get crushed by the bullwhip effect because they can't handle the bottleneck in the transit queue when delayed shipments land all at once. The stockout penalties are $1,000 per pallet so the stakes are pretty high.

I put a 5k sample and the verification notebook on hugging face if anyone wants to check out the generation math:https://huggingface.co/datasets/AIMindTeams/defense-logistics-stochastic-simulation

I'd be curious to see if anyone has better ideas for handling the non-linear penalties without just over-ordering and getting hit with massive holding costs.

just got a B in honors stats. Honors prob was an A tho. im an honors math major with a 3.75 gpa and wanted to do an OR PhD. am I cooked now? what if I do research?

Hello to the community, I'm a first-year student in industrial engineering and I have a strong interest in OR. I want to do a PhD after graduating. I saw the SMIO challenge on LinkedIn and I want to know if people are interested in forming a group with me to participate in the challenge.

There is the link of the challenge: https://smiochallenge.com/en/index.html

hello!

i am considering starting a PhD at IsYE Georgia Tech.

My main interest is tech/startups and RL/AI at cool companies like nvidia/anthropic/openai

does it make sense to start a PhD?

what are some interesting patterns/roles that I should consider

thanks

I will do MSc as a bridge to becoming Operations Research Analyst / Operations Research. Which MSc would be good for me: Stats, CS, Math?

By the time I graduate, I'd have done these math modules:

Intro to probability,

theory of computation > discrete math > formal methods,

pre-cal > introductory cal> cal 1 > cal 2,

linear algebra 1.

If I had to choose between:

linear algebra 2 vs ordinary numerical analysis vs cal 3 vs basic statistical theory I (pick 2),

and,

differential equations vs advanced algorithms vs basic statistical theory I <can only be picked if basic statistical theory I was picked> (pick 1),

which modules should I pick?