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Optimal and Heuristic Approaches for Constrained Flight Planning - - PowerPoint PPT Presentation

Sep 20, 2023 641 likes •846 views

Optimal and Heuristic Approaches for Constrained Flight Planning under Weather Uncertainty Florian Geier 1 Guillaume Povda 2 Felipe Trevizan 1 Manon Bondouy 2 Florent Teichteil-Knigsbuch 2 Sylvie Thibaux 1 1 Research School of Computer

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Optimal and Heuristic Approaches for Constrained Flight Planning under Weather Uncertainty

Florian Geißer1 Guillaume Povéda2 Felipe Trevizan1 Manon Bondouy2 Florent Teichteil-Königsbuch2 Sylvie Thiébaux1

1Research School of Computer Science,

The Australian National University

2Airbus - Artificial Intelligence Research

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Constrained Flight Planning under Weather Uncertainty

Flight Planning Compute a flight plan for a given aircraft mission which minimises fuel consumption.

  • F. Geißer, G. Povéda, F. Trevizan, M. Bondouy, F. Teichteil-Königsbuch, S. Thiébaux –

Constrained Flight Planning under Weather Uncertainty 1/16

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Constrained Flight Planning under Weather Uncertainty

  • Convective activity indicates showers and thunderstorms
  • Weather is inherently uncertain
  • Can lead to significant delay in public air transport

Airline operations today are mainly based on deterministic weather forecasts and do not take uncertainty into account when optimising the flight trajectory.

  • F. Geißer, G. Povéda, F. Trevizan, M. Bondouy, F. Teichteil-Königsbuch, S. Thiébaux –

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Constrained Flight Planning under Weather Uncertainty

  • In reality, a plan has to satisfy operational constraints
  • restrict expected travel time through convective areas
  • ensure expected arrival is in a given time window

Important We consider constraints over expectations, which are different to hard constraints.

  • F. Geißer, G. Povéda, F. Trevizan, M. Bondouy, F. Teichteil-Königsbuch, S. Thiébaux –

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Constrained Flight Planning under Weather Uncertainty

  • Ensure time and convection constraints ⇒ Constrained
  • Consider uncertain weather effects ⇒ Stochastic
  • Find a route minimizing fuel ⇒ Shortest Path Problem
  • F. Geißer, G. Povéda, F. Trevizan, M. Bondouy, F. Teichteil-Königsbuch, S. Thiébaux –

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Constrained Flight Planning under Weather Uncertainty

  • Ensure time and convection constraints ⇒ Constrained
  • Consider uncertain weather effects ⇒ Stochastic
  • Find a route minimizing fuel ⇒ Shortest Path Problem

In other words We want to solve a constrained stochastic shortest path problem (C-SSP).

  • F. Geißer, G. Povéda, F. Trevizan, M. Bondouy, F. Teichteil-Königsbuch, S. Thiébaux –

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Stochastic Shortest Path Problem

A stochastic shortest path problem S consists of:

  • a set of states S
  • current position, speed, altitude . . .
  • a set of actions A
  • fly to waypoint, change altitude, change speed
  • a cost function C
  • represents fuel consumption
  • an initial state sI and a set of goal states S⋆
  • departure and arrival airport
  • a probabilistic transition function P(s′|a, s)

⇒ requires access to a weather forecast model

  • we use a black box model that computes state transitions
  • F. Geißer, G. Povéda, F. Trevizan, M. Bondouy, F. Teichteil-Königsbuch, S. Thiébaux –

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Stochastic Shortest Path Problem

A solution for an SSP is a deterministic policy (mapping from states to actions) which minimizes costs.

  • F. Geißer, G. Povéda, F. Trevizan, M. Bondouy, F. Teichteil-Königsbuch, S. Thiébaux –

Constrained Flight Planning under Weather Uncertainty 5/16

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Constrained Stochastic Shortest Path Problem

A constrained stochastic shortest path problem consists of:

  • an SSP S
  • a set of constraints C, where each constraint:
  • comes with a secondary cost function
  • bounds the expected cost of this function by a constant
  • e.g.: E[duration] ≤ 300 minutes
  • F. Geißer, G. Povéda, F. Trevizan, M. Bondouy, F. Teichteil-Königsbuch, S. Thiébaux –

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Constrained Stochastic Shortest Path Problem

A solution for a C-SSP is a potentially stochastic policy which minimizes costs and satisfies constraints over expectation. Existing C-SSP planners are not applicable to our problem:

  • i2-dual: requires factored representation of the state space
  • i-dual: requires a heuristic function for each cost function

Our paper presents a new algorithm for C-SSPs based on Column Generation.

  • F. Geißer, G. Povéda, F. Trevizan, M. Bondouy, F. Teichteil-Königsbuch, S. Thiébaux –

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Column Generation

Column Generation Common approach for constrained deterministic shortest path problems based on linear programming (LP). We generalize Column Generation to the probabilistic case: repeat

  • 1. Solve the problem ignoring constraints
  • 2. Evaluate constraints on current solution
  • 3. Modify problem to improve the current solution

⇒ adaptation of the primary cost function

Take-off Landing Take-off Landing

expensive

Take-off Landing

  • F. Geißer, G. Povéda, F. Trevizan, M. Bondouy, F. Teichteil-Königsbuch, S. Thiébaux –

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Column Generation

Solve the problem ignoring constraints:

  • we can use any SSP algorithm to solve this subproblem
  • computes a deterministic policy π with associated costs

Take-off Landing

  • F. Geißer, G. Povéda, F. Trevizan, M. Bondouy, F. Teichteil-Königsbuch, S. Thiébaux –

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Column Generation

Evaluate constraints on policy π:

  • if no constraint is violated and solution cannot be

improved ⇒ return solution

  • otherwise, modify current subproblem:
  • change problem such that π can not be optimal

→ original problem with shifted cost function

  • shifted costs explore different trade-offs between

constraints and costs

Take-off Landing

expensive

  • F. Geißer, G. Povéda, F. Trevizan, M. Bondouy, F. Teichteil-Königsbuch, S. Thiébaux –

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Column Generation

  • Each policy corresponds to a column in the LP
  • LP solver computes a solution to the LP:
  • solution is a convex combination of policies

⇒ i.e. a probability distribution over deterministic policies

  • guarantees minimum primary cost
  • respects constraints over expectation

Take-off Landing

π1 : 1.0

Take-off Landing

expensive

π1 : 0.1 π2 : 0.9

Take-off Landing

π1 : 0.25 π2 : 0.10 π3 : 0.65

  • F. Geißer, G. Povéda, F. Trevizan, M. Bondouy, F. Teichteil-Königsbuch, S. Thiébaux –

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Stochastic and Deterministic Policies

  • If required, we can select the best deterministic policy
  • Deterministic policy not guaranteed to satisfy constraints
  • Finding an optimal deterministic policy is NP-complete

Alternative approach to Column Generation: Heuristic Decomposition based on Determinisation More details in the paper.

  • F. Geißer, G. Povéda, F. Trevizan, M. Bondouy, F. Teichteil-Königsbuch, S. Thiébaux –

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Empirical Evaluation

  • Evaluate all approaches on real-world data set
  • 3 short, 3 medium, and 3 long distance flights
  • weather forecast ensemble with data from June 2018
  • BADA aircraft performance model
  • Time window constraints and convection constraints
  • Focus on deterministic policies
  • F. Geißer, G. Povéda, F. Trevizan, M. Bondouy, F. Teichteil-Königsbuch, S. Thiébaux –

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Empirical Evaluation - Time Constraints

  • F. Geißer, G. Povéda, F. Trevizan, M. Bondouy, F. Teichteil-Königsbuch, S. Thiébaux –

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Empirical Evaluation - Convection Constraints

3500 4000 4500 5000 5500 3500 4000 4500 5000 Heuristic Decomposition C-SSP Fuel Burn in KG

  • F. Geißer, G. Povéda, F. Trevizan, M. Bondouy, F. Teichteil-Königsbuch, S. Thiébaux –

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Further Details in the paper. Or: visit us in the poster session!

  • F. Geißer, G. Povéda, F. Trevizan, M. Bondouy, F. Teichteil-Königsbuch, S. Thiébaux –

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