Solving the resource constrained project scheduling problem with quantum annealing - Institut d'électronique, de microélectronique et de nanotechnologie Accéder directement au contenu
Article Dans Une Revue Scientific Reports Année : 2024

Solving the resource constrained project scheduling problem with quantum annealing

Résumé

Quantum annealing emerges as a promising approach for tackling complex scheduling problems such as the resource-constrained project scheduling problem (RCPSP). This study represents the first application of quantum annealing to solve the RCPSP, analyzing 12 well-known mixed integer linear programming (MILP) formulations and converting the most qubit-efficient one into a quadratic unconstrained binary optimization (QUBO) model. We then solve this model using the D-wave advantage 6.3 quantum annealer, comparing its performance against classical computer solvers. Our results indicate significant potential, particularly for small to medium-sized instances. Further, we introduce time-to-target and Atos Q-score metrics to evaluate the effectiveness of quantum annealing and reverse quantum annealing. The paper also explores advanced quantum optimization techniques, such as customized anneal schedules, enhancing our understanding and application of quantum computing in operations research.
Fichier principal
Vignette du fichier
s41598-024-67168-6.pdf (3.59 Mo) Télécharger le fichier
Origine Fichiers éditeurs autorisés sur une archive ouverte

Dates et versions

hal-04659116 , version 1 (07-06-2024)
hal-04659116 , version 2 (22-07-2024)

Identifiants

Citer

Luis Fernando Pérez Armas, Stefan Creemers, Samuel Deleplanque. Solving the resource constrained project scheduling problem with quantum annealing. Scientific Reports, 2024, 14 (1), pp.16784. ⟨10.1038/s41598-024-67168-6⟩. ⟨hal-04659116v2⟩
12 Consultations
8 Téléchargements

Altmetric

Partager

Gmail Mastodon Facebook X LinkedIn More