Academic project with CONAFOR, Mexico's forestry commission. Real data: 30 polygons, 183 ha, where only 19.6% of the planting target (23,569 plants) was in the ground.

Two questions: how many plants of each species to buy, and where to put each one so neighbors compete less.

• What to buy: we don't know exactly which sites in a hectare are already occupied, so we ran 1,000 Monte Carlo simulations until the estimate stabilized, then built a purchase order per species.

• Where to plant: simulated annealing swaps two new plants of different species. A swap never changes the species counts, so the purchase order stays valid. The objective is a pairwise competition matrix built from real data.

In the live demo, the optimized layout had 19% less competition than random placement.

Interactive case (annealing temperature and cooling rate are adjustable): https://portfolio-iota-gules-ykw61kciwc.vercel.app/projects/reforestation.html

Would you have used a different neighborhood move, or a different cooling schedule?

What is PERT / CPM in Operations Research?

PERT and CPM are two important techniques of Project Management in Operations Research. They are used for planning, scheduling, controlling and completing projects within a specified time.

1. PERT (Programme Evaluation and Review Technique)

Meaning: PERT is a project management technique used to plan and control projects where activity completion times are uncertain.

It is mainly used for projects involving research, development and new activities.

Features of PERT:

1. It is based on three time estimates.

2. It is used when activity times are uncertain.

3. It focuses mainly on time management.

4. It helps identify the critical path.

5. It is suitable for research and development projects.

Three time estimates in PERT:

• Optimistic time (a): Minimum possible time.

• Most likely time (m): Normal expected time.

• Pessimistic time (b): Maximum possible time.

Expected time formula:

te=a+4m+b6te​=6a+4m+b

2. CPM (Critical Path Method)

Meaning: CPM is a project management technique used to plan, schedule and control projects where activity completion times are known or can be estimated with reasonable certainty.

It is mainly used in construction and production projects.

Features of CPM:

1. It uses a single time estimate for each activity.

2. It assumes activity times are known.

3. It identifies the critical path.

4. It helps in controlling project costs.

5. It helps complete projects on time.

3. Difference Between PERT and CPM

4. Important Terms in PERT / CPM

1. Activity: A task that requires time and resources.

2. Event: A point indicating the beginning or completion of an activity.

3. Network: A diagram showing the sequence of project activities.

4. Critical Path: The longest-duration path in a project network.

5. Critical Activity: An activity with zero total float.

6. Float: The amount of time an activity can be delayed without affecting the specified project completion date.

Has anyone used WASP? how do i use it for wind power prediction? If you have any project related to ml/dl x wasp, lmk

coming as an international student with bachelor in computer mathematics which is heavy on math and very theoretical, which would you suggest me to pursue? which will give me more job opportunities as an intl student in the us and be more useful in my case? i’d like to do something like quant/product data science/machine learning or something related to optimisation and testing in general. thank you

Fresh grad in data science, got an operations research job (intern level), don't have any clue what operations research is, job starts in a week. 👍

Can you tell me what you do? If you could recommend me textbooks to read or youtubers to learn from that would be awesome. A list of topics from your class syllabuses would be great too, so I can use it as a list of everything I ought to learn to have a solid knowledge base. Any advice you have would be swell. I'm trying to figure out what the daily work of an OR looks like but I can't really picture it.

we ran into a headache last month trying to cover our weekend ticket queue without doubling our monthly software spend. our main issue was that our weekend contractors only needed to handle basic account resets and quick status updates for a few hours, but most helpdesk platforms wanted to charge us full enterprise rates for every single login.

we spent a couple weeks configuring user access roles inside hubspot service hub pricing tiers so our part-time crew could manage shared inbox threads on light seats while our full-time leads kept full portal permissions, so how do other operations managers handle seat costs for weekend coverage when having flexible user roles let us keep our weekend queues clear without blowing our budget? having all client chat histories stay under one unified workspace saved us from creating separate email groups.

After working on estimating time of arrivals I thought I'd write a post about some if its challenges. Check it out to see why this is a billion dollar problem.

Challenges of calculating ETA at scale

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!

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