Optimizing logistics in the oil and gas industry is not just a mathematical exercise—it’s a daily battle against uncertainty. Orders change without warning, vehicles get delayed at loading terminals, wet stock levels fluctuate, and drivers work under strict regulatory limits. And unlike other supply chains, petroleum distribution adds another layer of complexity: trucks aren’t just “one unit of capacity.” They’re divided into multiple compartments, each with its own volume, product compatibility rules, and operational constraints.

This is where I started looking for a more sophisticated solution. After years of working with traditional heuristics and rule-based systems, I integrated NVIDIA cuOpt, a GPU-accelerated optimization engine, into my logistics stack. The experience has been transformative—not just for the systems I’ve built, but for how I think about dispatching itself.

The Challenge: Multi-Compartment Tankers Are Their Own Universe

Anyone who works in fuel distribution knows that a single tanker doesn’t behave like a normal delivery vehicle. A truck may have five compartments: 1,800 gallons in one, 1,000 in another, 800 in a third, and so on. Each compartment has its own rules—gasoline cannot follow diesel, Jet-A must be isolated, octane levels cannot mix, and temperature adjustments change effective volume.

When dispatchers plan manually, they don’t just choose a truck; they mentally solve a puzzle:

• Which orders can be combined without contamination?
• How do I respect each site’s delivery window?
• How do I distribute fuel across compartments without violating product restrictions?
• How do I route the vehicle so that it completes all drops and returns within driver-hour limits?

At scale, that puzzle becomes impossible for a human to solve consistently. I’ve seen dispatch teams spend hours debating a single load plan, only for the situation to change ten minutes later because a terminal went offline or a customer added a last-minute order.

This is why I turned to cuOpt.

Why I Chose NVIDIA cuOpt

cuOpt is designed for one thing: solving incredibly complex routing and dispatch problems at a speed traditional solvers simply can’t match. It leverages GPU acceleration to evaluate millions of possible assignments in parallel, finding near-optimal solutions in milliseconds.

For oil and gas, that speed matters. It means I can:

• Recalculate an entire dispatch plan in real time when a truck gets delayed.
• Respond instantly when a customer adds a last-minute delivery.
• Run dozens of “what-if” scenarios to pick the most efficient load plan.

Most importantly, I can model each truck not as a single entity, but as a group of virtual compartments, each with its own capacity and constraints. Instead of forcing a solver to handle a single large capacity number, I decompose the vehicle into the actual operational units that matter.

Once I started representing tankers this way, the optimization engine understood the problem in the same structure dispatchers do.

Turning a Compartmentalized Truck Into a Computational Model

To integrate cuOpt, I built a framework that translates real-world data into something the optimizer can understand.

Each compartment becomes its own “virtual vehicle” behind the scenes. If a truck has five compartments, cuOpt sees it as five capacity-constrained agents that move together. That allows the solver to decide, for example, that Compartment 3 should carry 2,800 liters of diesel while Compartment 1 carries premium gasoline, all while sending both to the same route.

Then I encode the rules: which products are incompatible, which compartments are restricted, how volume must be allocated, what time windows each customer site allows, and what limitations terminals have on loading throughput. I also incorporate regulatory constraints—like maximum service hours—and the operational constraints that every dispatcher knows by heart but no spreadsheet can capture.

The result is a computational problem that mirrors the chaos of daily fuel logistics, but can now be solved automatically.

What the Optimizer Produces (and Why This Changes Everything)

When all the constraints, orders, and vehicle models are loaded, cuOpt gives back something that looks almost magical to anyone used to legacy systems: a complete operating plan.

I get the exact distribution of product across compartments, a route plan for the truck, timing estimates for every step, and the overall cost or distance impact. And because cuOpt solves every scenario so quickly, I treat the optimization engine as a continuously running assistant, recalculating and improving plans whenever new data arrives.

That ability to re-optimize throughout the day is what changes the nature of dispatch. Instead of building one plan in the morning and hoping it holds, I can deliver a dynamic, always-updated plan that responds to reality in real time.

Why This Matters for Oil & Gas Companies

When I first implemented cuOpt in a real distribution environment, the results were visible almost immediately. Mileage dropped, loading delays decreased, compartment utilization increased, and planners gained something priceless: predictability. Fuel distribution is a low-margin business, which means even small increases in efficiency compound quickly.

But beyond the numbers, something more important happened: dispatch teams stopped fighting with the system. Instead of arguing with static spreadsheets or rigid software, they could rely on an optimizer that understood their world and responded instantly to their needs.

That’s something traditional routing tools simply cannot do.