Electric Ground Support Equipment Operations at Airports
Fleetsizing under operational uncertainty: A KLM case study at Schiphol
T.O. de Niet (TU Delft - Mechanical Engineering)
B. Atasoy – Mentor (TU Delft - Mechanical Engineering)
A. Napoleone – Graduation committee member (TU Delft - Mechanical Engineering)
K. Timmermans – Mentor (TU Delft - Mechanical Engineering)
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Abstract
Airports are electrifying Ground Support Equipment (GSE) fleets to reduce local emissions, but electric GSE changes both fleet-capacity and operational-demand planning. This paper investigates how operational requirements, uncertainty, and charging affect the required fleet capacity and operational demand of electric Ground Support Equipment (eGSE) during airport turnaround operations. A rule-based discrete-event simulation represents individual vehicles, service time windows, airport travel, battery state, finite charging infrastructure, and service-specific resources. The focused case study considers two operationally distinct vehicle types at KLM Ground Services at Amsterdam Airport Schiphol: depot-based toilet vehicles and stand-side loaders. The model is extensively verified and validated for fitness for purpose using flight and task data, airport distances, track-and-trace movement data, measured energy consumption, state-of-charge and charging observations, waste and dumping logic, historical arrival deviations, and expert review. Water vehicles were also implemented and validated in the underlying study but are omitted from the detailed paper results to avoid repetition. Under combined task-timing and travel-time uncertainty, the strict fleet requirements are 15 toilet vehicles and 25 loaders. Pragmatic lower bounds of 9 toilet vehicles and 23 loaders achieve 99.90% and 99.93% mean on-time completion, respectively, but retain residual service-window risk. Operational-control experiments show that idle forward staging reduces the strict stochastic toilet requirement from 15 to 10 vehicles and substantially reduces deadheading. Feasibility-aware task triage reduces the number of late tasks under scarcity, but increases the lateness of tasks that are deprioritised. Charging is not the binding cause of lateness in the tested reference configurations, although it materially affects charging sessions, charger occupancy, peak use, and infrastructure demand. The scientific contribution is therefore a decision-support method that estimates fleet capacity and diagnoses how demand timing, positioning, dispatching, uncertainty, and charging resources shape operational performance.