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Flight Gate Assignment API Reference

Data

Data model for Flight Gate Assignment use case.

FlightGateAssignmentData

Bases: UcData

Data for the Flight Gate Assignment (FGA) use case.

The Flight Gate Assignment problem assigns flights to airport gates so that total passenger transit time (to baggage claim and check-in) is minimised while respecting time-overlap constraints.

Attributes:

  • name (Literal['flight_gate_assignment']) –

    Identifier for this data type.

  • n_flights (int) –

    Number of flights to assign.

  • n_gates (int) –

    Number of available gates.

  • arrival_passengers (list[int]) –

    Number of arriving passengers per flight.

  • departure_passengers (list[int]) –

    Number of departing passengers per flight.

  • flight_times (list[tuple[float, float]]) –

    (arrival_time, departure_time) per flight.

  • gate_to_terminal (NumPyArray) –

    Shape (n_gates, 2) -- distances from each gate to baggage claim (column 0) and check-in (column 1).

  • buffer_time (float) –

    Minimum buffer time between consecutive flights at the same gate.

plot(*, ax: Axes | None = None) -> Axes

Plot the flight schedule as a timeline.

Parameters:

  • ax (Axes | None, default: None ) –

    Matplotlib axes to draw on. Creates a new figure if None.

Returns:

  • Axes –

    The axes with the plot.

to_string() -> str

Return a string describing the data.

from_schedule(arrival_passengers: list[int], departure_passengers: list[int], flight_times: list[tuple[float, float]], gate_to_terminal: np.ndarray | list[list[float]], buffer_time: float = 0.5) -> FlightGateAssignmentData staticmethod

Create FlightGateAssignmentData from a flight schedule.

Parameters:

  • arrival_passengers (list[int]) –

    Number of arriving passengers per flight.

  • departure_passengers (list[int]) –

    Number of departing passengers per flight.

  • flight_times (list[tuple[float, float]]) –

    (arrival_time, departure_time) per flight.

  • gate_to_terminal (ndarray | list[list[float]]) –

    Shape (n_gates, 2) -- distances from each gate to baggage claim (column 0) and check-in (column 1).

  • buffer_time (float, default: 0.5 ) –

    Minimum buffer time between consecutive flights at the same gate, by default 0.5.

Returns:

generate_random(n_flights: int = 4, n_gates: int = 3, seed: int | None = None) -> FlightGateAssignmentData staticmethod

Generate a random Flight Gate Assignment instance.

Parameters:

  • n_flights (int, default: 4 ) –

    Number of flights, by default 4.

  • n_gates (int, default: 3 ) –

    Number of gates, by default 3.

  • seed (int | None, default: None ) –

    Random seed for reproducibility, by default None.

Returns:

Formulation

Formulation for Flight Gate Assignment use case.

FlightGateAssignmentFormulation

Bases: UcFormulation[FlightGateAssignmentData, FlightGateAssignmentSolution]

Constraint-based formulation for Flight Gate Assignment.

Mathematical Formulation

Decision Variables: x[f,g] binary -- flight f assigned to gate g

Objective: minimize sum_{f,g} (arr_pax[f]dist[g,0] + dep_pax[f]dist[g,1]) * x[f,g]

Constraints: 1. Each flight exactly one gate: sum_g x[f,g] == 1 2. Non-overlapping: x[f1,g] + x[f2,g] <= 1 for overlapping pairs

to_string(data: FlightGateAssignmentData) -> str staticmethod

Return a string describing the formulation.

formulate(data: FlightGateAssignmentData) -> Model staticmethod

Formulate the Flight Gate Assignment problem.

Parameters:

Returns:

  • Model –

    A Luna Model ready to be solved.

interpret(solution: Solution, data: FlightGateAssignmentData) -> FlightGateAssignmentSolution staticmethod

Extract solution from solver result.

Parameters:

Returns:

Solution

Solution model for Flight Gate Assignment use case.

FlightGateAssignmentSolution

Bases: UcSolution

Solution for the Flight Gate Assignment (FGA) use case.

Attributes:

  • name (Literal['flight_gate_assignment']) –

    Identifier for this solution type.

  • assignments (dict[int, int]) –

    Mapping from flight index to gate index.

  • total_transit_time (float) –

    Total weighted transit time (objective value).

  • is_valid (bool) –

    Whether the solution satisfies all constraints.

plot(data: FlightGateAssignmentData | None = None, *, ax: Axes | None = None) -> Axes

Plot the gate assignment as a Gantt chart.

Parameters:

  • data (FlightGateAssignmentData | None, default: None ) –

    Problem data for time information.

  • ax (Axes | None, default: None ) –

    Matplotlib axes to draw on. Creates a new figure if None.

Returns:

  • Axes –

    The axes with the plot.

to_string() -> str

Return a string describing the solution.

Instance

Instance model for FlightGateAssignment use case.

FlightGateAssignmentInstance

Bases: UcInstance[FlightGateAssignmentData, FlightGateAssignmentFormulation, FlightGateAssignmentSolution]

Instance combining data and formulation for FlightGateAssignment.

Collection

Collection of Flight Gate Assignment instances.

FlightGateAssignmentCollection

Bases: UcInstanceCollection[FlightGateAssignmentInstance]

Collection of Flight Gate Assignment instances.

from_random(min_flights: int, max_flights: int, n_gates: int = 3, num_instances: int = 1, *, seed: int | None = None) -> FlightGateAssignmentCollection classmethod

Generate random Flight Gate Assignment instances.

Parameters:

  • min_flights (int) –

    Minimum number of flights.

  • max_flights (int) –

    Maximum number of flights.

  • n_gates (int, default: 3 ) –

    Number of gates, by default 3.

  • num_instances (int, default: 1 ) –

    Number of instances per flight count, by default 1.

  • seed (int | None, default: None ) –

    Random seed for reproducibility, by default None.

Returns: