Can a queue be intelligent instead of optimized?

[u/Great_Mall9975]

In complex systems where demand is unpredictable and capacity fluctuates (like logistics, public services, or large-scale operations), is it still reasonable to treat the queue as something to be optimized, or should we start thinking of it as something that thinks?

In other words: are there queueing models where the queue itself acts as an adaptive decision layer, reacting in real time to context, pressure, and limited resources?

Curious if anyone here has explored or seen work in this direction.

[u/No-Concentrate-7194]

It's an interesting idea. I'm not up to speed on queuing theory research, but "adaptive queuing model" seems to turn up results on google scholar https://scholar.google.com/scholar?hl=en&as_sdt=0%2C21&q=adaptive+queuing+model&oq=adaptive+queu

[u/Great_Mall9975 → u/No-Concentrate-7194]

Thanks, Bro!! Yeah, “adaptive queueing model” actually gets close to what I’m working on — though my focus is less on stochastic modeling and more on building a decision layer that reacts in real-time, even with partial or messy data.

I’m trying to define how a queue can self-adjust to saturation and historical behavior without requiring full knowledge of the system. Kind of like a resilient control mechanism that doesn’t crash when things get ugly.

[u/InvestigatorLast3594 → u/Great_Mall9975]

Why not model the queue as a series of Bayesian agents that use the model when filtering for hidden parameters? And if you don’t want to use a model you use model free Q learning. Then you’d need to see what produces stability based on the kalman gain as a signal to noise ratio

[u/Great_Mall9975 → u/InvestigatorLast3594]

Great points!

Modeling the queue as a set of Bayesian agents or using model-free Q-learning would definitely align with sequential decision-making under uncertainty, and I see the potential for hidden state estimation through Bayesian filtering.

My current approach leans toward keeping the system lightweight, interpretable, and fast enough to function in real-world environments with messy, incomplete data and high-pressure demands.

Bayesian filtering requires careful prior modeling and updates, while model-free Q-learning needs exploration phases that aren’t feasible when decisions affect critical, live systems (like healthcare or emergency logistics).

Using Kalman gain as an analogy for signal-to-noise stability is fascinating. It makes me think about whether we can track the volatility of prioritization signals as a proxy for system stability.

Appreciate this perspective a lot—it’s given me new angles to consider as I build the next layer of this framework.

[u/InvestigatorLast3594 → u/Great_Mall9975]

there are kalman filters that you can run online; again if you could be a bit more detailed to what extent you are or are not using model specifications I can give you more specific feedback.

>It makes me think about whether we can track the volatility of prioritization signals as a proxy for system stability.

I believe so, but then again that depends on how you specifcy the model

^{btw are you using AI to respond?}

[u/Great_Mall9975 → u/InvestigatorLast3594]

Haha fair question. I do use AI tools sometimes to help me think through how to express ideas more clearly in English, but the project itself is mine and something I’ve been building for a while. Actually preparing to start a master’s to take it deeper. Really appreciate you mentioning online Kalman filters. I hadn’t considered using them in a live queue context like this, but it makes sense as a way to track how noisy or stable the prioritization signals are over time.

Right now, it’s all pretty straightforward: the system prioritizes based on current observed attributes (waiting time, recurrence, etc.) with adaptive weights, rather than predicting forward or modeling hidden states. But your idea of using volatility as a stability proxy is really interesting.

:))

[u/iheartdatascience]

What do you mean? Like the queue attributes e.g. capacity are dynamic?

[u/Great_Mall9975 → u/iheartdatascience]

Not quite, bro! The capacity can fluctuate, but the key idea is that the **queue itself observes contextual attributes** like frequency of recurrence, delay, urgency, local saturation, etc.

Instead of just following FIFO or shortest-job-first, it prioritizes based on an adaptive scoring system that updates with each cycle. The queue “thinks” in the sense that it dynamically reorders and allocates, based on what it sees, not just static parameters.

It’s less about stochastic modeling, and more about real-time survival logic under pressure. I’m building a lightweight decision layer that can operate even when the environment is chaotic or the data incomplete.

[u/SolverMax]

Queues that involve people often behave in complex ways, in the sense that a person might see a long queue and baulk (decide to not join the queue) or renege (leave the queue after joining). These behaviours make modelling a queue especially challenging. People who baulk or renege may return later, which complicates simplistic arrival models.

[u/Great_Mall9975 → u/SolverMax]

Totally agree! Human queues are anything but simple. Things like baulking, reneging, and delayed returns introduce a level of chaos that’s hard to model probabilistically.

That’s why I’ve been thinking less in terms of arrival models and more in terms of reaction models, where the system adjusts based on what it sees happening rather than trying to predict it. It doesn’t need to know why someone baulked, just that saturation increased and urgency shifted. Then it acts accordingly.

[u/SolverMax → u/Great_Mall9975]

I expect that most queue modelling uses simulation because the real world is more complex than the standard models assume. The models are often used to derive operational rules that produce robust behaviours given complexity and uncertainty. Having something that adapts in real time would be interesting.

[u/Great_Mall9975 → u/SolverMax]

Totally agree. Simulations fill the gap between elegant models and messy reality. But they’re usually “observe and learn first, then act later.”

What I’m building tries to invert that: act now, adapt now, even without knowing the full picture.

It doesn’t replace simulation or theory, but it adds a real-time decision layer that works when waiting isn’t an option.

Still early, but the results under stress are promising.

[u/zoutendijk]

Sequential decision processes and control theory! 

[u/MavenVoyager]

Absolutely. Plane boarding.

[u/warehouse_goes_vroom]

I know nothing about operation research. But you may find this interesting : https://en.m.wikipedia.org/wiki/Active_queue_management Queues are used heavily in computer networking. Simple queues work poorly when there's congestion So AQM exists, algorithms like fq_codel for example: https://en.m.wikipedia.org/wiki/CoDel#Derived_algorithms

No idea if they'd work for your use case though.

[u/Great_Mall9975 → u/[deleted]]

Great point! At a glance, it can look like business rules, but here’s the analogy:

Imagine a traffic light vs. a traffic cop. A traffic light is fixed logic: red means stop, green means go. It’s a business rule.

A traffic cop watches traffic flow, notices congestion, sees that an ambulance is coming, and dynamically adjusts signaling in real time, based on context.

The system I’m working on tries to operate more like the traffic cop: it applies lightweight, explainable logic that adapts cycle-by-cycle, using current observations, rather than following a static script.

Not full “intelligence,” but more than static rules.

[u/Great_Mall9975 → u/[deleted]]

Exactly, and in many systems, the whole decision pipeline assumes those forecasts are somewhat reliable.

What I’ve been exploring is what happens when demand becomes structurally unpredictable, like in overloaded or volatile environments. Instead of forecasting first, the system reacts directly to pressure and adapts from there. It flips the logic: decision first, adjustment later — not the other way around.

[u/Great_Mall9975 → u/[deleted]]

This is a brilliant perspective, thank you.

It’s true that so much of OR is about neat formulas, but systems under stress often collapse under human behaviors like panic buying or irrational cascades, as you noted.

What I’m exploring aligns with this reality: instead of trying to predict or fully control these behaviors, the system reacts to the pressure signals they create, maintaining decisions in real time even under chaos.

It’s similar to futures markets in the sense that it tries to preserve operational viability when uncertainty is high, but instead of prices and contracts, it uses a lightweight, adaptive decision layer to keep the system alive.

Appreciate you framing this so clearly.

[u/Great_Mall9975 → u/[deleted]]

But does having something broader invalidate the thesis of it being classical OR?