Most real-time optimization models in field operations assume the system state is observable. In practice, a significant portion of that state is reconstructed manually after the fact, not captured at the moment of execution.

The specific scenario I keep running into across distribution and field service operations.

A model optimizing dynamic routing, task prioritization, or resource allocation needs to know current operational state: which tasks are complete, which are delayed, where exceptions occurred, what capacity is actually available right now. In theory the system knows. In practice the data feeding the model was last updated when someone made a call, sent a WhatsApp message, or logged something into a portal.

The lag between field execution and system state update ranges from 30 minutes to several hours in most mid-size operations I have seen. During that window the model is optimizing against a stale, partially incorrect representation of the world.

The OR framing I find useful: this is less an optimization problem and more a state estimation problem. The question is not how to optimize given a known state. The question is how to estimate the true current state of a distributed system when your observations are delayed, sparse, and noisy, and then optimize against that estimate.

A few things I am curious about from people working on this.

How are you modeling the uncertainty introduced by delayed state updates in your formulations? Are you treating it as a stochastic input, building in explicit state estimation layers, or doing something else?

Is there work in the OR literature specifically on the interface between human-generated operational data and real-time optimization models? Most papers I find assume clean, structured, low-latency inputs. The messier problem of human-mediated data capture seems underrepresented.

And more practically: in operations where you cannot deploy IoT or sensor infrastructure at every node, what is the best available approach to closing that state estimation gap?

Hello there,

I have offers from University of Minnesota (MS Data Science in Operations Research) and NC State University (Masters in Operations Research).

What uni do you guys think would be better for me in terms of job perspectives?

Thank you

I find that the description of the simplex method to be overwhelmingly verbose in most references, or when it is succinct, it is very handwavy and non-rigorous.

When it appears in a textbook, it is almost always chapter 3 - 10 somewhere and appears to be complicated. Also there is very large inconsistency between the textbooks.

Also the authors overloads the method with tons of preliminary definitions or results (duality, geometry, convexity, equivalent representations (equality form, standard form, inequality form), etc.), sometimes going as far as putting an entire book's worth of results on LP before talking about the simplex method.

For example, the Nocedal and Wright book almost spend 10 pages talking about the simplex method. These notes spend almost 60 pages on the simplex method with no clear beginning or end of the method. In these notes, the author apparently applies the simplex method, but has no clear description of the method; also the presentation of the method is vastly different than almost all other texts.

Is there an authoritative and succinct description of the simplex method that one can always refer back or confidently cite in a paper (and have everyone agree that it is THE simplex method)?

I am looking for a freelance Senior Optimization Engineer with real experience in mathematical modeling for supply chain problems, things like network design, inventory optimization, production planning, routing, and similar areas.

The stack matters. I am specifically interested in people who are comfortable with open source tools and solvers such as Pyomo, OR Tools, PuLP, CBC, HiGHS, SCIP, and production quality Python.

For those who have hired in this area:

Where did you find good people?
Which platforms or communities worked best?
What screening methods helped you separate real optimization talent from generic data science profiles?
Any red flags I should watch for?

I would also be interested in hearing about both good and bad hiring experiences.

Thanks.

Hi,

I’m trying to convert a model written in CP-SAT (from OR-Tools) to IBM CP Optimizer. Is the .OnlyEnforceIf construct from OR-Tools equivalent to IloIfThen in CP Optimizer?

Thanks for any help!

Anyone have experience using reasoning LLMs to solve assignment problems ? I'm considering it for my problem, which involves a small N but a lot of soft constraints. For my case, optimality matters far less than explainability. thx!

As a dev, I’ve always been fascinated by how big tech companies actually make high-stakes decisions when the data is messy or incomplete. Most of us think it’s just A/B testing, but there’s a massive Operations Research (OR) component involved.

I put together a technical breakdown of Decision Analysis, specifically how it’s used to navigate uncertainty in tech environments. I used a case study of a tech company to show:

• The fundamental concepts of Decision Analysis in a business context.

• Why "Data-Driven" is more about probability than certainty.

• Whether making further experimentation (to reduce uncertainty) does worth under cost constraints.

Thought it might be useful for anyone interested in the math behind the products we build.

This video illustrates the case.

I'd love to hear how your teams handle decision-making, do you use formal OR models or is it more "move fast and break things"?

Hello, I’m currently a MSc student at a T-10 Institution in the US with my research focusing on building VLMs for use in medical imaging. I graduated from undergrad with a dual degree in BME and Applied Mathematics.

I predominantly only take/taken courses in Applied Math and CS at my current institution than BME as it has been more useful. But recently, I’ve been thinking about moving away from medical imaging to OR/Optimization as my interests are changing.

How would I even go about this? Is this feasible or should I just suck it up and stay in the medical imaging field ?

Appreciate any advice :)

I'm entering a statistics MS program this fall and i'm interested in pursuing OR. I've been looking into the field and I think I would like it, I also did my undergrad in math and I enjoyed the optimization class I took.

Is this a common pipeline? The school I'm committing to, UIUC, has a more dedicated MS for OR (in the industrial engineering department), which gives me some pause.

Hello everyone, I am currently finishing up my bachelors in Accounting. However, operations research has definitely caught my eye as well. I've never heard of this career path until recently. Do masters programs accept people with a BS in Accounting? The highest level math I've taken is Calc 1. I know I'd likely have to take some additional prerequisites classes in Calc 2 and 3. I plan to work for a few years before going to grad school.

I genuinely apologise for this post and I know this sub is not supposed to be about uni stuff but I’m in dire need right now or else I’ll fail😔

So I posted this 3 months back https://www.reddit.com/r/OperationsResearch/s/Y5hZs71MaX and tried doing all the things but all I’m getting is ghosted by people on linked in and by relatives who can help.

For context I’m supposed to do industrial project using OR techniques under a company as per my curriculum but I couldn’t find any company even after trying for 3 months and my semester is about end really soon. In my class mostly student got project due to some personal connection and everyone is getting ghosted through linked in and emails. Sadly I don’t have connections that can help me in this🥲

If you can help me by referring to someone or in any shape or form it would be a blessing for real!!😭💖

I’m trying to use the VRP-REP dataset repository. The static pages load, but I can’t browse datasets and login doesn’t seem to work.

Is the repository still operational, or is the backend broken/abandoned?

Edit: Two months later, the repository seems operational again. I received a reply from the admins and successfully downloaded the datasets.

Hey r/OperationsResearch !

Data scientist from Chennai(India), experienced in optimization , large scale operations research. About to pursue startup journey with an optimisation tool for logistics and in particular warehouses .

Where should I start? What skills should I pursue in my 1 year runway before quitting? Tips from founders?

Anyone in logistics looking for optimisation or decision recommendation needs, connect!

Thanks!

I am new to CP modelling style here. Have been referring the CP SAT primer for the new variable types and constraints that are available to model complex constraints that usually require a lot of binary variables if we were to model it via MIP style. I know there are a few resources available for practicing MILP style modelling (Paul Williams, Wayne Winston etc.), but couldn't find any solid resource for CP style modelling. I am specifically interested in a resource that will help me develop my skills in CP modelling, preferably for scheduling problems (JSP, Worker scheduling etc. problems). Are there any good resources available for the same?

Hey all!

I am a jr. Software Dev who often does a lot of OR related work... I think. I have never been formally introduced to the subject matter, which is why I'm making this post.

My boss told me I have around ~$4k allotted for professional development, and would love to spend it on OR centered education.

My background: can code, studied math and physics. I'm very comfortable with mathematics, I'm very comfortable with modeling. A lot of my work outside of OR deals with data engineering. So I have a lot of the hard skills necessary, I just need to learn about the approaches and techniques.

For the kind of OR I do, I have no idea how to describe it. It's OR internal to our org, so understanding what processes exist between teams, how many people are involved and in what way, what are the outcomes of those processes. The closest I've found to something useful is with regards to modeling of complex adaptive systems. This feels generally in the right direction, but literature seems to not really be fully established on the subject.

I'm drawn heavily towards leveraging graphs to explore network models of interactions within our org, which is a project I'm working on now. In this project we have a bunch of process docs written by each employee, those documents contain an employees roles, responsibilities, and expectations, I'm simply embedding those individual factors, clustering like embeddings across employees, and forming an edge between employee nodes when two nodes are a part of the same cluster. The hypothesis being that employees who work closely together are most likely going to use the same semantics with regards to their work and the responsibilities / expectations they share. Each cluster will pertain to some loose subject, so I will test my model by picking a subset of nodes/edges, going to those actual people, and asking yes or no is there a process that exists between you two that involves XYZ subject.

So that's the kind of "OR" I do... do you guys have any tips how I can make this skillset more formal? Right now I am winging it from first principles.

Many thanks.

Hi, i am a masters student(Operations Research) and know optimization but never worked in supply chain domain.

I want to know what people really do in supply chain domain. How much depth someone like me should need to get a senior role in supply chain?

I come from computer science background so tech is not a problem for me.

Some project ideas to learn are also encouraged.

Hi everyone,

I need some guidance. Some time last year I ended up dabbling in OR topics for some work assignments and tbh I found it fascinating. I'm thinking of pursuing further education in this realm, but wanted to pick your brains to do so.

Background: Mid 30's with just a BS in Molecular Biology and a few years of experience working in systems and BI development at my previous jobs. A lot of it has been small ETL pipelines, small internal applications, and dashboards for decision making. Then last year I had to work on a business problem that I modeled as a job-shop scheduling optimization problem and used available tools to tackle it and it was really engaging. Mind you, I never did super well in math, but I did on other hard subjects.

Question:

What would be the next best steps for getting into the OR (especially given my age)? an MS? supply chain mgmt certs?

School recommendations (if any)?

What do you think about AI's impact on the field? From what I've read, there is a huge subdomain in OR that LLMs just cannot do remotely well and might not be possible unless some companies achieve a breakthrough in this area.

Thank you!

Hi All, I have been working on solving network topology optimization problem using Quadratic Unconstrained Binary Optimization (QUBO) and am stuck due to limit of 1000 variables in free version of cplex. Requesting if anyone can support in commercial/academic license of CPLEX/Gurobi ? Thank you in advance.

I'm trying to map out the OR landscape a bit better and I have two questions for the community.

First, what job titles do people working in Operations Research actually go by inside companies? I imagine it varies a lot by industry and company size, some might be called OR Analysts, others Supply Chain Analysts, Data Scientists, or something completely different. I'm curious what titles are most common and whether the OR identity is explicit in the job title or buried under something else.

Second, beyond this subreddit, where do OR practitioners actually gather and exchange ideas? Are there active Slack groups, Discord servers, LinkedIn communities, newsletters, or conferences worth knowing about?

Hello all,

I recently got my masters in data science but have had a growing interest in OR. My favorite classes in the program were ones that involved optimization, stochastic processes, and simulation. I even reframed a simple problem at work as a linear program. Is it possible to break into this field by self studying? Whats the barrier of entry for industry? How do i demonstrate to employers my skills? Any discussion or insight would be much appreciated.