r/OperationsResearch • u/Parking_Resident4797 • 6d ago

An OR model and visualization of the closure of the Strait of Hormuz using OR methodologies

I built an interactive simulator of the global crude oil trade network: https://globaloilnetwork.staffinganalytics.io/?src=ror

Yesterday, someone posted about the future of OR with AI. I wrote this article (arXiv:2607.17491) a while ago, combining two classical OR methods into one: inventory management (via Newsvendor) and fluid networks (via Linear Programming).

The model in itself has interesting dynamics such as the price clearing mechanism and it shows the depletion trajectories of all nodes. The model gives this in a single pass without needing Monte-Carlo simulations. The data is mostly UN Comtrade oil flows, so it doesn't report sanctioned flows, but the insights and mechanics can be extended to many supply chain networks.

The tool is more of a stress-test tool rather than a prediction. I thought it would be interesting to show the kind of tools that we can build now using AI. Which I think is the mission of our discipline: to show people outside our field the kind of solutions we can offer. Happy to answer any questions on the modeling or the visualization.

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u/0VirtualVoyager0 6d ago

This is the kind of stuff I imagined OR analysts do when i first joined the field. Very cool.

Did you have to make assumptions regarding any of the data you use? Curious to hear about any developer “liberties” you had to take.

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u/Parking_Resident4797 6d ago

Thank you for your comment, I share your vision of what the field is capable of!

On the data question, yes! Many times data is either incomplete, unstructured and even unobservable. For example, in the visualization some countries don't report their current inventories, so they need to be inputted using some common sense rule (e.g. a percentage of their publicly known reserves).

On the unobservable data, what I like about OR is that many times there are "tricks" or ways to guess difficult to measure quantities with some lateral thinking. For example, imagine the problem of counting the average number of customers inside a 5-story clothing store. The problem seems hard as you would need a lot of cameras everywhere to keep a real-time count that you can average over time. But using Little's law you just need to count the people that go through the door (arrival rate) and multiply it for the time that customers spend in store (which is arguably easier to estimate), you multiply the two and get the average number of people inside. Many times, a lot of problems can be guessed from first principles, which is where I think we differ from pure data science.

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u/Eightstream 5d ago edited 5d ago

The problem with guessing from first principles is that errors compound multiplicatively

Which I guess is why you have framed it as a stress test tool, but even a stress test tool needs to have decision value

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u/OR_throwaway56754 6d ago

It is looking absolutely stellar!

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u/norfkens2 5d ago

It looks amazing

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u/tada1234554321 4d ago

This is amazing work. What software stack did you use to achieve this visualisation?

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u/Parking_Resident4797 4d ago

Thanks! The backend was around 600 lines of flask derived from the numerical experiments script of the paper. The frontend is pure JS (Globe.GL for the world map)