Solving the Resource-Constrained Project ...
Type de document :
Autre communication scientifique (congrès sans actes - poster - séminaire...): Communication dans un congrès avec actes
Titre :
Solving the Resource-Constrained Project Scheduling Problem (RCPSP) with Quantum Annealing
Auteur(s) :
Armas, Luis Fernado Pérez [Auteur]
Deleplanque, Samuel [Auteur]
Acoustique - IEMN [ACOUSTIQUE - IEMN]
Creemers, Stefan [Auteur]
Lille économie management - UMR 9221 [LEM]
Deleplanque, Samuel [Auteur]
Acoustique - IEMN [ACOUSTIQUE - IEMN]
Creemers, Stefan [Auteur]
Lille économie management - UMR 9221 [LEM]
Titre de la manifestation scientifique :
INFORMS ANNUAL MEETING
Organisateur(s) de la manifestation scientifique :
INFORMS
Ville :
Phoenix
Pays :
Etats-Unis d'Amérique
Date de début de la manifestation scientifique :
2023-10-15
Date de publication :
2024-03-06
Mot(s)-clé(s) en anglais :
Qunatum calculus
Quantum Annealing
QUBO
Optimization
Operations Research
Quantum Annealing
QUBO
Optimization
Operations Research
Discipline(s) HAL :
Informatique [cs]
Mathématiques [math]
Physique [physics]
Mathématiques [math]
Physique [physics]
Résumé en anglais : [en]
Quantum annealing offers a promising approach for solving combinatorial optimization problems like the Resource-Constrained Project Scheduling Problem(RCPSP). We investigated its empirical performance by formulating the ...
Lire la suite >Quantum annealing offers a promising approach for solving combinatorial optimization problems like the Resource-Constrained Project Scheduling Problem(RCPSP). We investigated its empirical performance by formulating the RCPSP as QUBO and ISING model. Experimental results are provided using a cutting-edgeannealer (D-Wave Advantage) on RCPSP instances from the CV dataset. We conducted a thorough analysis of the solution energy landscape focusing ondiversity and quality. Results are compared with classical solvers, shedding light on the potential advantages and limitations of using quantum annealing forRCPSP. Our findings suggest that quantum annealing delivers satisfying solutions for smaller to medium-sized problems. It also offers a high diversity ofsolutions making it a viable candidate for hybrid approaches to further enhance solution quality.Lire moins >
Lire la suite >Quantum annealing offers a promising approach for solving combinatorial optimization problems like the Resource-Constrained Project Scheduling Problem(RCPSP). We investigated its empirical performance by formulating the RCPSP as QUBO and ISING model. Experimental results are provided using a cutting-edgeannealer (D-Wave Advantage) on RCPSP instances from the CV dataset. We conducted a thorough analysis of the solution energy landscape focusing ondiversity and quality. Results are compared with classical solvers, shedding light on the potential advantages and limitations of using quantum annealing forRCPSP. Our findings suggest that quantum annealing delivers satisfying solutions for smaller to medium-sized problems. It also offers a high diversity ofsolutions making it a viable candidate for hybrid approaches to further enhance solution quality.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
Source :