.png){kind=icon}
Return
Optimization of flight routes: quantum approximate optimization algorithm for the tail assignment problem
.png){kind=icon}
PRE
AI
DOI：10.1007/s42484-026-00387-4.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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. This work investigates the application of the Quantum Approximate Optimization Algorithm (QAOA) to the TAP, incorporating route costs. A detailed problem formulation is presented, and QAOA is evaluated through numerical simulations on representative instances to assess its ability to identify optimal solutions. The results show that QAOA can solve small instances correctly, with performance improving as circuit depth increases, and that route-graph connectivity has a notable impact on the algorithm’s behavior. QAOA is further compared with classical approaches, including brute force and branch-and-price, as well as with Quantum Annealing (QA).
Keywords:
Tail assignment problem
QAOA
Quantum computing
Optimization
Journal
Q
IF:
4.4
Papers:
445
Citations:
796
Organization
Cited Papers
ShareSave
ShareSaveShareSaveShareSaveEvidence of scaling advantage for the quantum approximate optimization algorithm on a classically intractable problem.png)
SCIENCE ADVANCES
OAAI
SCIENCE ADVANCESIF12.5
OAAIShareSave
View more