Airport ground operations under disruption.
The problem.
An airport runs on a forecast that is wrong within twenty minutes. Gates, stands, ground crew, fuel, catering, and turnaround sequencing all assume each other's plan — and a single delayed inbound un-anchors all of them.
Today's tooling helps the operator describe the new state. It does not help them choose between two recovery plans at 16:43 with a queue building. The operator decides anyway; the trace of what they considered is lost.
The shape of the decision.
Variables: gate-to-flight assignments, crew rosters, tow and pushback sequencing. Constraints: minimum connection times · stand compatibility · crew duty rules · noise curfews. Objectives: on-time performance · passenger connections · crew rest.
How Alpha Z helps.
The disruption is framed as a re-optimization against the latest state. The algorithm returns the recovered plan with the alternatives it considered and rejected — the operator sees what they would have given up by choosing differently.
After the disruption, the formulation itself — what bound, what relaxed, where the model was wrong — becomes a modeling insight. The next disruption of the same shape starts with a better frame.