Optimization of Flight Routes: Quantum Approximate Optimization Algorithm for the Tail Assignment Problem

TL;DR

This paper explores applying the Quantum Approximate Optimization Algorithm to the Tail Assignment Problem in airline operations, comparing its performance with classical and other quantum methods to assess its potential and limitations.

Contribution

It formulates the TAP for quantum optimization and evaluates QAOA's performance against classical and quantum approaches on realistic instances.

Findings

QAOA shows potential advantages over classical methods as quantum hardware improves.

Current quantum hardware limitations restrict QAOA's effectiveness.

QAOA's performance is comparable to classical methods on small instances.

Abstract

The Tail Assignment Problem (TAP) is a critical optimization challenge in airline operations, requiring the optimal assignment of aircraft to scheduled flights to maximize efficiency and minimize costs. To address the TAP, this work applies the Quantum Approximate Optimization Algorithm (QAOA), a promising quantum computing algorithm developed for tackling complex combinatorial optimization problems. A detailed formulation of the TAP is provided and QAOA's performance is evaluated on realistic problem instances, examining its strengths and weaknesses. Additionally, QAOA is compared with classical methods such as brute force and branch-and-price, as well as Quantum Annealing (QA), another quantum approach. The analysis reveals the current limitations of quantum hardware but suggests potential advantages as technology advances.