Where Qatalyst delivers value.
Optimisation problems appear everywhere. We focus on complex, constrained, operational settings where decisions must be made quickly and reliably.
Ports and terminals
Yard allocation, lane assignment, congestion management, berth planning, and boarding or loading coordination.
Freight and logistics
Routing, time windows, resource allocation, and energy-aware schedules for electrified freight.
Transport systems
Multimodal coordination, disruption response, and resilient service planning with explicit trade-offs.
Featured study case: Electric freight charging coordination
Most freight charging happens at depots, but depots and shared hubs face hard constraints: limited site power, clustered arrivals, and strict readiness deadlines. Qatalyst helps charging operators, depot operators, and fleets coordinate charging so vehicles are ready on time without creating queues or exceeding power limits.
Who uses it
- Depot operators managing limited site power and overnight windows
- Charging operators running shared hubs used by multiple fleets
- Fleet operators needing reliable plans that protect state of charge and delivery schedules
The problem in simple terms
- Too many vehicles arrive at the same time, queues build quickly
- Power caps limit how many vehicles can charge at full rate
- Delays lead to missed departures and delivery windows
- Simple rules can move congestion rather than solving it
What Qatalyst does
- Creates a rolling charging plan for the next 30 to 90 minutes
- Recommends who charges now, who charges later, and at what rate
- Reallocates demand smoothly across chargers and time slots
- Uses classical optimisation by default, with hybrid or quantum methods applied selectively when scenarios are highly coupled and time-critical
Typical inputs (anonymised)
- Charger availability, power limits, session durations, queue estimates
- Vehicle state of charge, arrival time, readiness deadline, minimum required charge
- Operational rules such as priority vehicles, minimum dwell time, and site policies
Outputs users receive
- A site-level charging schedule that stays within the power cap
- Vehicle-level recommendations: charger, time slot, and target charge
- Congestion risk alerts and suggested corrective actions
Success metrics
- Reduced queue time and fewer charging conflicts
- Higher on-time vehicle readiness for departures
- Smoother power demand and fewer peak events
- Improved utilisation across chargers and time periods
Simple scenario
A shared logistics depot hosts vehicles from multiple operators. Demand peaks between 18:00 and 22:00, but the site has a strict power cap. Qatalyst produces a charging schedule that prepares vehicles by their deadlines, avoids queue build-up, and keeps total power draw within limits. Recommendations update every 10 to 15 minutes if conditions change.
Typical objectives
- Minimise queue time and delay
- Reduce operational cost
- Meet service level targets
- Minimise emissions and energy demand
- Improve robustness under disruption
Typical constraints
- Capacity constraints and lane limits
- Processing time and staffing
- Compatibility rules and assignment constraints
- Time windows and deadlines
- Safety and operational policy constraints
Want to apply this to your site or network?
Share a short description of your charging setup, your power constraints, and your operational deadlines. We will map the decision problem and propose a practical optimisation approach, classical first, with hybrid or quantum options only where they add value.