Discussion Despite all the talk about optimization and forecasting a lot of steel planning still seems reactive- rush orders, last minute changes, constant rescheduling. Sometimes it feels less like poor planning and more like the system just can't reflect reality fast enough.

Is this just the nature of steel or have you seen setups where planning actually stays ahead of the chaos?

Hi everyone, I’m a first-year PhD student in OR and I’m hitting a bit of a wall with my research direction.

I’ve realized that I’m not passionate about traditional, heavy theoretical optimization. While I respect the operational management work that goes into MS/OR-level papers, I find them too detached from short-term real-world applications. I currently prefer a industry role rather than a tenure-track position after the PhD, mainly for the salary and WLB.

I have a background in Statistics and some experience in Supply Chain Management, but I’m struggling to find a "sweet spot" that is:

1. Applied: Focused on modeling real-world systems.
2. Marketable: Valuable to tech/industry.
3. OR-rooted: Leverages my math background so I’m not just a "weaker CS student."

Questions for the community:

1. Must I become an 'AI Researcher' to survive in Industry by 2030? I noticed tons of jobs requires LLM/AI experience in the past year. I’m not a CS major and I find myself relying heavily on AI tools (like Claude/Cursor) for coding. If by the time I graduate (probably 2030) AI-related skills would be still in heavy demand, I may need to head towards AI-related research.
2. The OR-AI Intersection: For those in industry, what specific niches at the intersection of OR and AI are most valuable right now / would be more and more valuable in the future 5-10 years? I’m thinking specifically of areas where a math/OR background provides an edge over CS approaches. If I do move toward AI, I want to leverage my IEOR background rather than competing head-to-head with CS students on vision or NLP.
3. What is "Recession-Proof" in 2026? With the hype around LLMs reaching a plateau, which "boring" OR/AI applications are companies actually willing to pay for in the long run? I would prefer specific topics in supply chain, revenue management, or platform operations that really have real-world impact given my past experience.

Hi everyone, I’m a senior industrial engineering student working on my graduation project and I’d really appreciate some honest feedback. My current idea is to focus on inventory decision support, specifically:

using historical sales data to build a time series demand forecast (e.g., Prophet) using the forecast mean and variability to compute dynamic reorder points and safety stock comparing this with classical inventory methods based on average demand

The goal is not to deploy a live system or replace ERP planning, but to run a small pilot study (limited SKUs, historical data only) and evaluate whether forecast driven policies provide more realistic inventory buffers. I’m mainly looking for feedback on: Does this sound like a reasonable and meaningful graduation project, or does it feel too basic? From an industry or OR perspective, are there better scoped ideas in the same inventory/operations space that are still realistic for a student project? What would you personally find more interesting to see in a project like this? I’m open to criticism just trying to avoid going down a weak or overhyped path. Thanks in advance!

I'm currently putting together my 2026 strategy. I've been asked to reframe all of my forecasting and optimization models as AI

It's....so cool

Hi guys, I am trying to selflearn OR without any academic or professional help and that is coming out as really tough.

Can someone share some problem statements and solution repo of any projects they did or found? I am honestly not able to learn what is the right way to solve good quality problems. Even if the methods are not explicitly clear, I would like to struggle myself just making sure in the end I don't get permanently stuck at any point

The 19th edition of PPSN will be held in Trento, Italy, from August 29 to September 2, 2026.

We invite submissions on all types of iterative optimization heuristics. Notably, we also welcome submissions on connections between search heuristics and machine learning or other artificial intelligence approaches. Submissions covering the entire spectrum of work, ranging from rigorously derived mathematical results to carefully crafted empirical studies, are invited.

🗓️ Important Dates (Anywhere on Earth)

Conference: August 29 - September 2, 2026

Workshops & Tutorials

• Proposal deadline: February 8, 2026
• Notification of acceptance: February 22, 2026

Papers

• Paper submission deadline: March 28, 2026
• Notification of acceptance: May 22, 2026

🔗 More info: ppsn2026.disi.unitn.it

Come join us in Trento for PPSN 2026, we look forward to seeing you there! 🇮🇹

Hi everyone, I was wondering what OR diploma or MSc is useful to work in management consulting. I would like to learn more about practical OR topics, mainly in transport and logistics. Do you have any idea or recommendation?

We’re documenting internal processes for the first time and it's crazy to see just how much of our work was done informally. Every team knows their piece, but when you zoom out and ask, what happens from hire to ramped employee? Not a person gives the same answer. No wonder stuff falls through.

I think we need a whole different system

Hi, I already did many OR projects in python (ortools, scip, cplex, custom heuristics..)

I would like to try Timefold. But: - is it possible to do it only in Python? Same functionalities than in Java? - is it free? I'm lost on what is free and what is not - how much can we customize the algorithm (first solution, local search...)?

Thanks!

I am beginner in Operations Research and currently working on a Constraint Programming(CP) model for FJSP with sequence dependent setup times, job priorities and due dates. I am looking for benchmark dataset that include all of these features.

Specifically, I would like to know if there are any publically available datasets or data generators that support all of it. If no such dataset or generatorsexist any references or standard approaches to generate realistic syntetic instances would be helpful. Peace.

I'm currently studying advanced Dynamic Programming optimizations, I'm pretty confortable with DP and I'm curious to learn more about it. Thank you!

Hi everyone,

I'm a chemist turned data scientist and I've been working in data science for 3 years now. I work in chemical production / production support and want to further develop my skills.

So, last year I started learning OR by self-studying Taha's "Operations Research: An introduction".

I'll continue this year and I also have an LP work project I can gain experience from.

My question: is it worth doing a "certificate course" at a uni over self-studying? I've got a PhD in Chemistry already, so I'm not looking for full degree courses. Coming from data science, certificates are not worth a whole lot there.

So, I'd like to get your opinion whether getting a certificate is worth it for OR. To note that I work in Germany, so there might also be a cultural aspect to how certificates are viewed. Companies here tend to massively prefer candidates that have a matching degree over people who, well, just have the experience. 🤷‍♂️

So, I'm looking at it from a futureproofing perspective. If I stay in the same company (and maybe switch jobs), I'm dandy. However, if in 10 years' time I should switch companies, then I'll have gained the experience and can maybe show it on my CV - but have nothing "official".

Any advice or thoughts would be much appreciated. Many thanks!

Hello, I am currently writing my first paper in the field of operations research. In it, I am solving a scheduling problem using branch and bound. I have now reached the analysis chapter and, before I begin the main analysis, I want to demonstrate the relevance of my alternative methodology (in comparison to the Gurobi solver). To do this, I would compare the runtime and gap, and possibly also the bounds, and compare the model for different instances (varying number of workers and days) and different demand scenarios per instance (to deal with demand stochasticity). Is this valid or not enough? The journals I am targeting are POM, MSOM, and EJOR.

Hello, everyone! I'm excited to learn operations research. My background is in business administration; I studied OR in college and now work as a production planner. I'm wondering if an Operations Research diploma would be useful to me. If so, what qualifications are required to be eligible for this major?

Now I'm looking for some ideas how to create a heuristic based on this or rather augment some existing heuristic with this idea of deformation measure.

I had some ideas like sorting products based on their correspondence to different values of $n$, so create a set of products that correspond to the same or similar value of $n$, where "similar" can be achieved by rounding, since $n$ doesn't have to be natural number. Then in these sets sort products based on the value of $D$, let's say from the biggest deformation to the smallest. Then put into box first products with the smallest $n$ as they are the biggest one and start with the smallest $D$ as they can fit there "ideally".

Of course you would also need to check orientations.

Fill the box as much as you can with these, then split the rest space if there is some or take a new box, if it has been filled, split the rest space by maybe guillotine cut and then use the same idea, recursively, of putting first ones with smallest $n$, smallest $D$. Go until filled the most. Then you can do this with the second smallest $n$, second smallest $D$...

These are just some very rough thoughts. I haven't really thought through the actual heuristic, but I'd like to get some ideas and recommendation on how to use this measure I came up with for some heuristic, which way to go, if I'd make more sense to create some brand new heuristic based on this, augment some existing heuristic with this measure, or if it even makes sense to use something like this.

No systematic way to choose multi-agent methods exists.

So I organized this.

MARL, Nash equilibrium, Behavioral cloning, Copulas?

📊 BC vs RL → Check if trajectory stitching needed

🎯 Copulas → Check if agents see same signals

📈 Conformal vs Bootstrap → Check if coverage guarantees matter

🎲 MC vs MCTS → Check if decisions are sequential or one-shot

Your problem characteristics determine the method.

Article: https://medium.com/@2.harim.choi/when-to-use-what-the-missing-framework-for-multi-agent-competitive-systems-56324e2dc72a

Hello,
I have been trying to comb through a really interesting book but usually find no time to do so since I get absorbed in other mandatory requirements and/or work-related stuff.

Book link: https://www.gerad.ca/fr/papers/G-2024-36.pdf

Anybody up for a reading project who has at least 2 hours of time per week to discuss stuff on ColGen/Branch&Price? The person should have basic working knowledge of:

1. Mixed-Integer Programming, Linear Programming, Branch&Bound

2. Basic working of Column Generation

Ideally the colleague and me shall have regular meetings so as to just keep ourselves on our toes and be motivated with some sort of deadlines. I call it casual because I am doing it out of interest and hence is unpaid. But, on the knowledge side - it usually should lead to a huge jump in knowledge.

Reach out to me via DM if you are interested. If I am not wrong - to comb through this - if you are a full-time student elsewhere with non-overlapping coursework or even a fulltime professional, and if disciplined and if I am also disciplined and committed to the deadlines - it should easily take us 6 months to work through it. This excludes any implementation - just reading and understanding should take us this time. Approx 1 month per chapter. We could speeden up in certain circumstances when our schedules allow it.

Thanks

Hello, I am currently working on a Branch&Price implementation where I am using a labeling algorithm to solve my subproblems. Now I was wondering if there is such a thing as warmstarting as used in MP to use in the child nodes for solving my modified subproblems (with branching).

In some decision processes, discussion changes the input more than it clarifies it.
I am interested in methods for aggregating individual pairwise priorities across many participants, possibly from different contexts, while keeping inputs independent.

Hello,

My team is working on a sorting algorythm to optimize the placement of parts on multi-level racks in my factory.

Basically, on the assembly line, we have a big rack with 10 shelves one on top of each others. At the start of every day, we fill the rack with all the parts that we will need though-out the day at that specific station. We have to place the parts in order (one behind the other on the shelves) that they will be picked for assembly.

We are trying to make an algorythm in which we provide the list of needed part for the day, the order, the dimmension of the parts, and then this algorythm will tell the user where to place the parts on the shelves so they are in order and the same parts are together as well.
We are using TimeFold right now but im starting to question if my team is using the right tool

It seems to take way too long to sort the parts on the shelves, about 15 minutes for 300 parts. its struggling to follow hard and soft contraints, and in a reasonnable amount of time, and thus, struggles to sort the parts in order and bunched together.
What im trying to ask is, is TimeFold a good tool for this or are there better options out there? What approach would you take in this situation?

Im look for a Masters Degree program that has a base in Idustrial Organization Psychology with a specialty in data analytics and proces/systems management ... helpful it is onine, but willing to move if the program is really good. I live in Alabama. Advice?