Solving the resource constrained project scheduling problem with quantum annealing

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 programmin...

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Bibliographic Details
Published in:Scientific reports Vol. 14; no. 1; pp. 16784 - 23
Main Authors: Pérez Armas, Luis Fernando, Creemers, Stefan, Deleplanque, Samuel
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 22.07.2024
Nature Publishing Group
Nature Portfolio
Subjects:
639/705/1041
639/705/1042
639/705/117
639/766/483/481
Computer Science
Computers
Energy
Humanities and Social Sciences
Linear programming
multidisciplinary
Operations Research
Optimization techniques
Project management
Quantum annealing
Quantum computing
Quantum optimization
Quantum physics
Resource constrained project scheduling problem
Scheduling
Science
Science (multidisciplinary)
Resource constrained project scheduling problem
Quantum annealing
Quantum optimization
ISSN:2045-2322, 2045-2322
Online Access:Get full text
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Summary: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.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-67168-6