Solving the resource constrained project scheduling problem with quantum annealing

Luis Fernando Pérez Armas, Stefan Creemers and Samuel Deleplanque
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Stefan Creemers: Université catholique de Louvain, LIDAM/CORE, Belgium

No 3323, LIDAM Reprints CORE from Université catholique de Louvain, Center for Operations Research and Econometrics (CORE)

Abstract: 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.

Keywords: Resource constrained project scheduling problem; Quantum optimization; Quantum annealing (search for similar items in EconPapers)
Pages: 23
Date: 2025-01-01
Note: In: Scientific Reports, 2024, vol. 14, 16784
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Persistent link: https://EconPapers.repec.org/RePEc:cor:louvrp:3323

DOI: 10.1038/s41598-024-67168-6

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