Solving the Resource-Constrained Project ...
Document type :
Compte-rendu et recension critique d'ouvrage
Title :
Solving the Resource-Constrained Project Scheduling Problem (RCPSP) with Quantum Annealing
Author(s) :
Pérez Armas, Luis Fernando [Auteur]
Laboratoire d'Economie et de Management [LEM]
Creemers, Stefan [Auteur]
Laboratoire d'Economie et de Management [LEM]
Deleplanque, Samuel [Auteur]
Acoustique - IEMN [ACOUSTIQUE - IEMN]
JUNIA [JUNIA]
Laboratoire d'Economie et de Management [LEM]
Creemers, Stefan [Auteur]
Laboratoire d'Economie et de Management [LEM]
Deleplanque, Samuel [Auteur]
Acoustique - IEMN [ACOUSTIQUE - IEMN]
JUNIA [JUNIA]
Journal title :
Scientific Reports
Pages :
16784
Publisher :
Nature Publishing Group
Publication date :
2024-07
ISSN :
2045-2322
English keyword(s) :
Resource Constrained Project Scheduling Problem Quantum Optimization Quantum Annealing 1
Resource Constrained Project Scheduling Problem
Quantum Optimization
Quantum Annealing
Resource Constrained Project Scheduling Problem
Quantum Optimization
Quantum Annealing
HAL domain(s) :
Informatique [cs]
English abstract : [en]
Quantum annealing emerges as a viable solution for solving complex problems such as the resource-constrained project scheduling problem (RCPSP). We analyze 12 Mixed Integer Linear Programming (MILP) formulations for solving ...
Show more >Quantum annealing emerges as a viable solution for solving complex problems such as the resource-constrained project scheduling problem (RCPSP). We analyze 12 Mixed Integer Linear Programming (MILP) formulations for solving the RCPSP, and convert the most qubit-efficient formulation into a Quadratic Unconstrained Binary Optimization (QUBO) model. We solve this QUBO model using the D-Wave Advantage 6.3 Quantum Annealing machine and compare its performance with that of classical computer solvers. This pioneering effort marks the first use of quantum annealing for RCPSP, showing promising results, especially for smaller to medium-sized instances.Show less >
Show more >Quantum annealing emerges as a viable solution for solving complex problems such as the resource-constrained project scheduling problem (RCPSP). We analyze 12 Mixed Integer Linear Programming (MILP) formulations for solving the RCPSP, and convert the most qubit-efficient formulation into a Quadratic Unconstrained Binary Optimization (QUBO) model. We solve this QUBO model using the D-Wave Advantage 6.3 Quantum Annealing machine and compare its performance with that of classical computer solvers. This pioneering effort marks the first use of quantum annealing for RCPSP, showing promising results, especially for smaller to medium-sized instances.Show less >
Language :
Anglais
Popular science :
Non
Source :
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