Is Gemini the Ultimate AI for Operations Research?
Z
Zack Saadioui
8/14/2025
Is Gemini Better at Operations Research Problems Than Other AIs?
This is a question I’ve been digging into lately, & it’s a pretty fascinating one. The short answer is… it’s complicated. It’s not a simple "yes" or "no." To really get to the bottom of it, we need to unpack what Operations Research (OR) problems are, how AI is changing the game, & then look at where a powerhouse model like Gemini fits into the picture.
Hope you're ready to dive deep, because we're about to explore the nuts & bolts of AI’s role in one of the most complex & critical fields of decision-making.
First Off, What Exactly Are Operations Research Problems?
Before we can even talk about whether Gemini or any other AI is "better" at it, we need to be on the same page about what we're talking about. Operations Research, or OR, is essentially the science of making better decisions. It's a field that uses advanced analytical methods to help organizations solve complex problems & make more effective choices.
Think of it as the ultimate puzzle-solving discipline for businesses & other large organizations. OR practitioners break down massive problems into their basic components & then use mathematical analysis & modeling to find the best possible solution. This usually involves a few key steps:
Identifying the problem: What's the real issue that needs to be solved?
Building a model: This is a mathematical or computer-based representation of the real-world problem, complete with all the variables & constraints.
Finding solutions: Using the model to derive potential solutions.
Testing & analyzing: Running simulations & tests on the model to see how well each solution works.
Implementation: Putting the best solution into practice in the real world.
The core characteristics of OR are optimization, simulation, & probability/statistics. You're always trying to find the best outcome—the maximum profit, the minimum cost, the fastest route, the most efficient schedule.
The kinds of problems OR tackles are HUGE & notoriously difficult for humans to solve on their own. We're talking about things like:
Airline scheduling: Figuring out the most efficient way to schedule thousands of flights, crews, & planes.
Supply chain management: Optimizing everything from inventory levels to delivery routes for global companies.
Portfolio optimization: Deciding on the best mix of investments to maximize returns while minimizing risk.
Network optimization: Designing communication networks or routing data packets for maximum speed & reliability.
For a long time, these problems were the domain of highly specialized mathematicians & computer scientists using dedicated solvers like Gurobi & CPLEX. But, like with so many other fields, AI is completely changing the landscape.
How AI is Revolutionizing Operations Research
Here's the thing: AI isn't necessarily replacing traditional OR methods. Instead, it's augmenting & supercharging them. The synergy between AI & OR is where the real magic is happening. A fantastic survey paper on arXiv, "Artificial Intelligence for Operations Research," breaks this down beautifully. AI techniques are being integrated into almost every stage of the OR process.
1. Parameter Generation & the "Predict-then-Optimize" Framework
Many OR models rely on parameters that are uncertain. Think about a vehicle routing problem. You need to know the travel time between two points, but that can change based on traffic, weather, & time of day. AI, particularly machine learning, is AMAZING at predicting these kinds of uncertain variables.
This has led to a powerful framework called "predict-then-optimize." First, an AI model predicts the necessary parameters (like travel times, customer demand, or material costs). Then, these predictions are fed into a traditional optimization model to find the best solution. It’s a two-step process that leverages the strengths of both worlds.
2. Enhancing Optimization Algorithms
AI is also being used to make the core optimization algorithms themselves better. This is where it gets really technical & pretty cool.
Graph Neural Networks (GNNs): Many OR problems, like logistics or network design, can be represented as graphs. GNNs are specifically designed to understand the complex relationships in graph-structured data. They can be used to analyze the structure of an optimization problem & help guide the solver to a solution more efficiently.
Recurrent Neural Networks (RNNs): Optimization algorithms are often iterative, meaning they take a series of steps to reach a solution. RNNs, which are great at understanding sequences, can be used to learn from the sequence of steps in an optimization process & make better decisions at each iteration.
Reinforcement Learning (RL): This is a REALLY exciting area. In RL, an AI "agent" learns by doing. It tries different actions, gets rewards or penalties, & over time, learns a policy to maximize its cumulative reward. In OR, this can be used to learn new, better ways of making decisions within complex algorithms like branch-and-bound (a common technique for solving integer programming problems). For instance, RL can learn which variable to branch on to solve the problem faster.
So, AI isn't just one thing in OR. It’s a whole toolkit of techniques that are making the process of solving these incredibly hard problems faster, more accurate, & more adaptive.
So, Where Does Gemini Fit In?
Alright, now we get to the main event. Is Gemini better at this than other AIs?
Based on what we're seeing in the real world, Gemini is proving to be an INCREDIBLY powerful tool for a wide range of OR problems. While you won't find many academic papers with direct benchmark comparisons saying "Gemini is 12.7% better than Model X on the Traveling Salesperson Problem," the evidence of its effectiveness is all over the place in practical applications.
A Google Cloud blog post lists hundreds of real-world use cases for Gemini, & a ton of them are classic OR problems in disguise. Check out some of these examples:
BMW Group is using Gemini to optimize its industrial planning processes & supply chains. They're creating digital twins of their assets & running thousands of simulations to improve distribution efficiency. This is textbook Operations Research.
UPS is building a digital twin of its ENTIRE distribution network. This allows them to see where packages are in real-time & optimize the whole system. This is a massive-scale logistics & network optimization problem.
Kinaxis, a major supply chain management company, is using Gemini to build data-driven solutions for scenario modeling, planning, & automation.
Papa John's is using Gemini & other Google AI tools to build predictive models that can better anticipate customer orders. This is a demand forecasting problem, a key input for many OR models.
What these examples show is that Gemini is being deployed by major companies to solve the exact kinds of complex, high-stakes problems that Operations Research was created for.
Why Generative AI Like Gemini is a Game Changer for OR
The examples above are just the tip of the iceberg. The reason models like Gemini are so impactful is because they represent the cutting edge of Generative AI. A special issue of the journal Information on "Generative AI in Operations Research" talks about this very trend.
Generative models can be used to create realistic operational data & scenarios. Imagine you're trying to optimize a factory's production schedule. You could use Gemini to generate hundreds of different possible demand scenarios for the next month. You could then run your optimization model on all of these scenarios to find a schedule that's robust & performs well under a wide range of future conditions. This is a HUGE step up from just using historical data.
Furthermore, combining generative AI with traditional OR methods is a major area of research. You can use a model like Gemini to understand the natural language description of a problem & help formulate the mathematical model in the first place, bridging the gap between business experts & OR practitioners.
This is where a tool like Arsturn becomes incredibly relevant. Imagine a business that wants to optimize its customer service operations. They could use Arsturn to build a custom AI chatbot trained on their own data. This chatbot can handle a huge volume of customer inquiries instantly, 24/7. This frees up human agents to focus on more complex issues. From an OR perspective, this is a resource allocation problem—how do you best allocate your human & AI resources to minimize customer wait times & maximize satisfaction? Arsturn provides a powerful tool to implement one side of that optimized solution. By automating a significant portion of the workload, it changes the parameters of the entire customer service system, allowing for a more efficient overall operation.
The Verdict: Is Gemini "Better"?
So, back to the original question. Is Gemini better?
Here's my take: Direct, quantitative comparisons are hard to come by. The field of AI is moving so fast that by the time a benchmark study is published, the models have already been updated.
However, we can say this: Gemini has demonstrated, through a vast array of real-world deployments, that it is EXCEPTIONALLY capable of tackling complex Operations Research problems. It's being used for supply chain optimization, logistics, predictive maintenance, resource allocation, & more by some of the biggest companies in the world.
What makes models like Gemini so powerful isn't just their ability to solve a specific mathematical problem. It's their versatility & their generative capabilities. They can:
Process & analyze massive, unstructured datasets to provide the inputs for OR models.
Power predictive analytics that are crucial for the "predict-then-optimize" framework.
Help in the modeling process itself, understanding natural language descriptions of problems.
Enable large-scale simulations by generating realistic future scenarios.
So, instead of thinking about whether Gemini is "better" than a specialized solver at a single, well-defined task, it's more accurate to see it as a powerful, flexible platform that enhances the entire Operations Research workflow. Other AI models, especially specialized ones, might be better at very specific, narrow tasks. But for the broad, messy, data-rich problems that modern organizations face, a large, multimodal model like Gemini offers a set of capabilities that is hard to match.
For businesses looking to apply these advanced capabilities, the path forward is becoming clearer. For instance, when it comes to lead generation & customer engagement—problems of optimizing website traffic for conversions—a tool like Arsturn can be a game-changer. It allows businesses to build no-code AI chatbots trained on their own data. This means you can create a personalized, intelligent agent on your website that can answer customer questions, qualify leads, & guide users to the right information. This is a form of real-time optimization of the customer journey, powered by the same kind of AI technology we've been discussing.
Wrapping It Up
Honestly, the question of which AI is "best" is a moving target. What's more important is understanding the trend: AI is fundamentally changing Operations Research. It’s making it more data-driven, more adaptive, & more powerful.
Gemini is clearly at the forefront of this transformation. The sheer number of companies using it for OR-related tasks is a testament to its capabilities. It might not be the "best" for every single OR problem out there, but it's proving to be an incredibly versatile & effective tool for a huge range of them.
The future of OR isn't about one AI model winning out over another. It's about the powerful combination of AI techniques—prediction, generation, reinforcement learning—with classic optimization methods. And in that future, models like Gemini are set to play a starring role.
Hope this was helpful & gave you a good overview of a pretty complex topic! Let me know what you think.