Solving the resource constrained project ...
Type de document :
Compte-rendu et recension critique d'ouvrage
Titre :
Solving the resource constrained project scheduling problem with quantum annealing
Auteur(s) :
Pérez Armas, Luis Fernando [Auteur]
Lille économie management - UMR 9221 [LEM]
Creemers, Stefan [Auteur]
Lille économie management - UMR 9221 [LEM]
Deleplanque, Samuel [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
JUNIA [JUNIA]
Acoustique - IEMN [ACOUSTIQUE - IEMN]
Lille économie management - UMR 9221 [LEM]
Creemers, Stefan [Auteur]

Lille économie management - UMR 9221 [LEM]
Deleplanque, Samuel [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
JUNIA [JUNIA]
Acoustique - IEMN [ACOUSTIQUE - IEMN]
Titre de la revue :
Scientific Reports
Pagination :
16784
Éditeur :
Nature Publishing Group
Date de publication :
2024-07-22
ISSN :
2045-2322
Mot(s)-clé(s) en anglais :
Resource constrained project scheduling problem
Quantum optimization
Quantum annealing
Quantum optimization
Quantum annealing
Discipline(s) HAL :
Computer Science [cs]/Operations Research [math.OC]
Résumé en anglais : [en]
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 ...
Lire la suite >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.Lire moins >
Lire la suite >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.Lire moins >
Langue :
Anglais
Vulgarisation :
Non
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