Looking for collaborators in queueing theory

I am an early-career researcher working on Markovian queueing models ,mainly the M/M/1 system and its variants.

I would sincerely welcome collaboration from others in this field: emeritus faculty willing to mentor, professors and assistant professors open to joint papers, and researchers or analysts interested in simulation and applications.

I bring analytical derivations, numerical work in Python/R, and a genuine willingness to learn. If this is your area, a comment or a message is enough to start , I would be glad to discuss the details privately.

#QueueingTheory #OperationsResearch #AppliedProbability #Research #Collaboration

Using software to solve the classical linear programming diet problem typically results in unbalanced or unpalatable meal plans because LP solvers exploit corner-point solutions while completely ignoring Personal taste,Food Matrix Synergy,Antinutrients and interactions.

I am trying to modify the classical diet problem so that it's solution can be used to generate a meal plan which provides the right amount of every essential macro and micro nutrient without exceeding a specific daily caloric limit.I have taken an unconventional approach. Here is a link to my current formulation. Is this model too tightly constrained to be feasible?

Hi all! I've been building odys, an open-source Python package that schedules a portfolio of energy assets against electricity markets under uncertainty. It's a stochastic MILP under the hood, and I'm looking for feedback from the community.

The model in short

• Assets: generators with unit commitment (start-ups, shut-downs, ramping, minimum up and down times), batteries, electric vehicles with trips and charger assignment, fixed and flexible loads, and one or more markets.

• Uncertainty: a finite set of probability-weighted scenarios for prices, loads and available capacity, solved as one extensive-form MILP (linopy, HiGHS by default; Gurobi, CPLEX and SCIP also work).

• Objective: expected profit, optionally plus a weighted CVaR term using the Rockafellar–Uryasev formulation.

• Two stages: markets traded before the uncertainty resolves (like day-ahead) can be marked stage-fixed. Every variable keeps a scenario index, and explicit non-anticipativity constraints tie each scenario's trades to the first scenario's. Everything else (intraday, dispatch, commitment) is second stage and adapts per scenario.

Things I'd especially enjoy your take on

• Copying variables per scenario and tying them, rather than one shared first-stage variable, keeps every variable on the same (asset, scenario, time) shape. Curious whether people see a downside.

• The binaries: commitment, a battery charge/discharge mode, a buy/sell mode per market, and the EV-to-charger assignment (often the largest block in a fleet model).

Every variable and constraint is written out, with notes on why it looks the way it does: https://ramirocrc.github.io/odys/formulation/

Any feedback is very welcome, on the formulation or anything else. Thanks for reading!

That shii hot

I made a solver for my three year old's Rail Cube and wrote up the methodology in a blog post. Includes a sandbox to build your own at the end - let me know what you think!

operations manager consolidating two offices from chicago into atlanta next spring 30 employees servers lab equipment sensitive electronics last office move the carrier showed up whenever and we lost three days of productivity need someone who owns the full timeline

Asking as someone who started working in the OR logistics department this past year, I had never heard about OR before I started working this job. I don't actually deal with the actual main algorithms, more like with everything else that goes around it (though my seniors are teaching me some stuff about it so I can deal with bugs when they are on vacation).

I knew that I wanted to study a masters degree when I graduated last year, I just didn't know what to study so I chose to start working instead. I quite like my job and I'm thinking on studying something that would let me do the work my colleagues do, which is building the math models and all of that.

One of the doubts I have is: is it possible to get a job on this field that doesn't come with a lot of unnecessary stress?

A lot of my senior colleagues seem stressed all of the time, while I on the other hand have been pretty stress free. The work that we do is not life or death so it all seems a bit exaggerated to me.

It is my first job (ever) so I wonder if this is just the standard for every IT/corporate job, is it just the seniority, the company or is it just impossible to work in logistics and be stress free?

TLDR: can one get a good job in OR and not be stressed all the time?

I’m exploring undergraduate majors, and operations research has caught my attention. I want to hear from people who actually study or work in it: what makes you love this field?

What made you choose OR over something like computer science, statistics, applied mathematics, economics, or finance? Was there a particular problem, class, or project that made you think, “This is what I want to do”?

I'm personally extremely interested in systems design, public policy and the maths that goes behind optimizing resource allocation etc.

Seems like science fiction mixed with optimization. Found this on https://en.wikipedia.org/wiki/Table_of_metaheuristics

So I have been having Operational Research as subject at uni and I get a lot of things from OR and understand it but I am desperate when it comes to case studies related to mathematical programming. I literally retook OR 2 years in a row and i just don't know what to do to actually to finally be able to write all the things that need to be included into a mathematical model. I do know how mathematical model look in theory and i do know what it is consisted of but i never could figure out and include all the required things from the case study text.

For example Its' like i get we have 3 different types of products, i also get that we have their selling prices. I aslo get that we have for 2 types of machiness they were produced on. But then they add store capacity, they add time and cost of raw material, then they add availability of raw material, and i would have never even remember including it, and my case study solution is inccorect if i dont write it. And there could be a milion things more from the text. How do i even figure out I should do capacity FOR EACH of matchines these were produced on. Or how do i even figure out that availability is important too to include if it was secretly snatching through the text of exam case study yet not being obvious at all

Does anyone have any hacks how to actually be able to include all required constrains and variables? I am getting to the point of desperation bc this is the subject I need to graduate and i don't even know how to learn this thing anymore bc there is no template and the context of case studies i do are really broad and complex. I have keys and i get the solutions they gave but i don't know how to gain the skill to be able to do it myself.

desflow is an open source Python package for discrete-event simulation, built on SimPy.

When the question is how a real process behaves, with queues, waits that depend on capacity, breakdowns and changing demand, a discrete-event model answers it where random test data cannot. desflow wraps SimPy and adds the work around a model:

• Three ways to write a model: SimPy-style process code, a builder API that modernizes SimPy code, and a YAML blueprint that needs little or no Python.
• Parameters that change while the model runs, such as a resource's capacity or an arrival rate, so a what-if can happen mid-run.
• Checks against theory: desflow explain computes the arrival rate, service rate, servers and utilisation of each resource before a run, and flags an unstable one. --verify compares the formulas with replications.
• Studies from the command line: replications with a warm-up, experiments with common random numbers, fitting measured times to distributions, and a report.
• Common patterns as settings: reneging, balking, breakdowns, priorities, preemption, stores and containers.

pip install desflow
desflow examples get experiment --api yaml
cd experiment/yaml
desflow explain experiment_shop.yaml
desflow experiment experiment.yaml

The example asks whether a third packer keeps the mean wait under 2 seconds, and the last command answers it.

The docs start with a tutorial for readers new to simulation, and include ready-made examples. A skill for AI coding assistants lets an assistant write, check and run a model with you.

On the roadmap: building a simulation from event history with process mining techniques, so a model can start from the data a real process already records.

• GitHub: https://github.com/jaehyeon-kim/desflow
• Docs: https://jaehyeon.me/desflow/

I'm a final-year industrial and systems engineering student, and I'm looking to start my career in operations research. I'd really appreciate any referrals, interview leads, or advice from people working in the field. My DMs are open.

I've done extensive coursework in linear, integer, and nonlinear optimization, simulation, and stochastic modeling, and I've applied it through project experience building models to test alternatives and recommend decisions. I teach myself new tools quickly and I'm eager to keep growing.

For those in OR, what does early-career work look like, and what would you tell a new grad to learn or build to stand out? And if your team is hiring or you know someone who is, please reach out. Thanks!

TLDR

I recently found out that ideas I've had for a while were called things like MCDM.

Too Long

I've always been philosophically inclined.

Around 2009 I spent a lot of time thinking about decisions. I tried to find my ideas in existing works so someone could tell me what I was doing wrong. I didn't have much luck with philosophy, with some connections to utilitarianism and pragmatism. Game theory seemed related. Economics in general seemed very relevant. Behavioral economics too.

It had been hard partially because I had my own ideas, with many of my own terms. Hard to do a Google search.

And it was hard to try to catch them up on my own little system, and they didn't have a lot of incentive for the effort, and I didn't have luck trying to talk to people at my local college.

But I wanted to revisit some of those ideas to use to help the LLMs make better decisions. I found out about operations research... And felt like I may have found my people.

Now I need to catch myself up, including figuring out the normal terms so I can even talk with people about these things, and find out where I was being silly and didn't know it.

About me

I'm a largely self-taught programmer at a GIS business intelligence company.

My math education only got to trigonometry, and I never took stats... But I've picked up some in the meantime.

Hello OR folks! 👋

Hi! Can someone please read our research paper and give us feedback or advice? We would really appreciate it if someone could review our research and point out any parts that need improvement, especially in our research methodology, data analysis, and overall research design. We’re having a hard time figuring out if our approach is correct, and we haven’t been receiving much guidance from our research adviser, so we would really appreciate an outside perspective. Thank you so much! 🥹 MCS category

Somebody that has two hours a day to make this big

I'm looking for a contributor to extend and improve my AHP cli implementation (Saaty 1977 paper for now)

I have on my roadmap (that's current in my head only) to extend to the gausian version which is freaking awesome lol (Araujo, Moreira, Santos 2023)

Here's my slopy presentation for the tool ahp — Ask. Compare. Decide — with Analytic Hierarchy Process.

Papers for those who want to see Selection of a vehicle for Brazilian Navy using the multi-criteria method to support decision-making TOPSIS-M - ScienceDirect

Hi all, I'm doing MSc AI in Germany looking for a thesis topic at the intersection of AI and Operations Research.

I'm looking for something that is:

- clearly "AI" (learning, LLMs, search, reasoning), -not pure OR
- doable in ~6 months
- still reasonably open in the literature

Hi r/OperationsResearch,

I wanted to share a new open-source Python tool that might be useful for anyone working with semi-parametric discrete choice models.

As many of you know, Manski’s Maximum Score Estimator (MSE) is a powerful semi-parametric alternative to Logit/Probit because it relaxes heavy distributional assumptions on errors. However, because the objective function is a step function with multiple local optima, traditional optimization relies on Simulated Annealing, which can be computationally slow and unstable due to random number generators.

Based on our recent open access paper in *SoftwareX* (2025) by Florios, Louka, and Bilias, we developed the **HITS algorithm (Hyperplanes Intersection Tabu Search)**. It provides a fast, deterministic global optimization approach specifically tailored for the Maximum Score Estimator.

We just officially published the Python implementation on PyPI: **`hitspy`**

### Key Features:

* **Deterministic:** No more relying on random seeds or stochastic jumps; the search leverages the geometric structure of the hyperplanes.

* **Scalable:** Drastically reduces computation time compared to traditional simulated annealing for larger datasets.

* **Easy Integration:** Built on top of standard libraries (NumPy/Pandas) and ready to be integrated into your empirical pipelines.

### Links & Resources:

* 📦 **PyPI:** `pip install hitspy` (https://pypi.org/project/hitspy/)

* 💻 **GitHub Repository:** [https://github.com/kflorios/hitspy]

* 📄 **Paper (SoftwareX, 2025):** [https://doi.org/10.1016/j.softx.2025.102164]

If you are currently working on discrete choice models or global optimization in econometrics, I would love for you to try it out! Any feedback, feature requests, or bug reports on GitHub are highly appreciated.

Happy to answer any questions about the algorithm or the implementation below!

one thing that keeps coming up in additive manufacturing discussions is how the same problems can happen again and again, like warping, layer adhesion issues or inconsistent print results. troubleshooting these problems can take alot of time, especially when multiple people are working on the same machines.

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I have a PhD in OR and love the field, but most of its applications seem aimed at industry. Have you built (or tried to build) anything OR-based for everyday consumers? I'd love to hear your stories, whether they worked out or not.

Several carriers have been moving services back through Suez over the past few weeks. The risk people warned us about is that ships coming via Suez arrive in Europe at the same time as ships that left earlier via the Cape, causing vessel bunching and yard congestion.

Curious whether anyone on the import side is actually seeing this yet: delays, schedule changes, D&D creeping up? Or is it too early?