↯HGV Hub LabEN4100 · GROUP 6
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VEHICLES. QUEUES. ENERGY.

Plan a better charging stop.

Explore how arrivals, charger capacity and queue priorities shape a hub’s demand.

01

Study & arrivals

Choose the study period and where truck arrival times come from.

Random Poisson arrivals. A seed repeats the same sample; it does not make the sample representative of real operations.

02

Vehicle & charging

Choose a mixed fleet below, or turn it off to use common vehicle values. Explicit scenario overrides take precedence over CSV / class values.

Mixed fleet for the main scenarios

Vehicle variationSample a vehicle class for each generated arrival. The same sampled trucks are reused across scenarios. Shares are probabilities, so a small sample may not match them exactly. Imported CSV vehicle values are kept unless a scenario explicitly overrides them.
Illustrative vehicle classes · edit to match your study

CSV imports bypass this generator. The target SOC below applies to all generated classes.

Time occupying the bay

A truck can finish charging before its driver is ready. The minimum bay time is a firm break from connection. Extra post-charge time can model optional idle occupancy; configure the yield rule per scenario. These are study assumptions, not a driver-hours compliance check.

03

Priority & journey assumptions

These control experimental urgency ranking and next-hub reachability checks.

Advanced · repeated runs & export intervals

Truck CSV imports use one replication. Synthetic replications use independent seeds, paired across scenarios.