Authors:
Luis N. Martins
1
;
Ana Paula Rocha
2
and
Antonio J. M. Castro
2
Affiliations:
1
Department of Informatics Engineering (DEI), Faculty of Engineering (FEUP), University of Porto, Porto, Portugal
;
2
Artificial Intelligence and Computer Science Lab (LIACC), University of Porto, Porto, Portugal
Keyword(s):
Tail Assignment Problem, Quantum Annealing, Scheduling Problem.
Abstract:
Tail Assignment is the problem of allocating individual aircraft to a set of flights subject to multiple constraints while optimising an objective function, such as operational costs. Given the enormous amount of possibilities and constraints involved, this problem has been a case study over the last decade. Many solutions have emerged using classical computing, but with limitations. Quantum Annealing (QA) is a heuristic technique to solve combinatorial optimisation problems by finding global minimum energy levels over an energy landscape using quantum mechanics. In this study, Tail Assignment Problem was framed as a Quadratic Unconstrained Binary Optimisation (QUBO) model and was solved using a classical and two hybrid solvers. The considered hybrid solvers made use of the D-Wave 2000Q quantum annealer. Tests were run based on extractions from real-world data, analysing, empirically, the performance of the implementation in terms of quality (i.e., the lowest operational costs) of th
e obtained solutions. We concluded that, for the considered datasets, there was a higher probability of obtaining better solutions for this problem using one of the hybrid solvers when compared with a classical heuristic algorithm such as Simulated Annealing (SA).
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