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Solving the resource constrained project scheduling problem with quantum annealing.

Luis Fernando Pérez Armas1, Stefan Creemers2,3,4, Samuel Deleplanque5

  • 1Operations Management, IESEG School of Management, CNRS, UMR 9221 - LEM - Lille Economie Management, Univ. Lille, 3 rue de la digue, 59800, Lille, Nord, France. l.perezarmas@ieseg.fr.

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Quantum annealing shows promise for complex scheduling problems like the resource-constrained project scheduling problem (RCPSP). This research is the first to apply quantum annealing to RCPSP, demonstrating potential for small to medium instances.

Keywords:
Quantum annealingQuantum optimizationResource constrained project scheduling problem

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Area of Science:

  • Operations Research
  • Quantum Computing
  • Computational Optimization

Background:

  • Complex scheduling problems, such as the resource-constrained project scheduling problem (RCPSP), are computationally challenging for classical solvers.
  • Quantum annealing offers a novel paradigm for addressing optimization tasks.
  • Previous research has not explored quantum annealing for RCPSP.

Purpose of the Study:

  • To investigate the application of quantum annealing to the resource-constrained project scheduling problem (RCPSP).
  • To compare the performance of quantum annealing with classical solvers for RCPSP.
  • To introduce new metrics for evaluating quantum annealing effectiveness.

Main Methods:

  • Analysis of 12 mixed integer linear programming (MILP) formulations for RCPSP.
  • Conversion of the most qubit-efficient MILP formulation into a quadratic unconstrained binary optimization (QUBO) model.
  • Solving the QUBO model using the D-wave advantage 6.3 quantum annealer and classical solvers.
  • Introduction of time-to-target and Atos Q-score metrics for performance evaluation.

Main Results:

  • Quantum annealing demonstrates significant potential for solving small to medium-sized RCPSP instances.
  • Performance comparison indicates advantages for quantum annealing in specific scenarios.
  • New metrics provide a framework for assessing quantum and reverse quantum annealing effectiveness.

Conclusions:

  • Quantum annealing is a viable and promising approach for tackling the resource-constrained project scheduling problem.
  • Further research into advanced quantum optimization techniques, like customized anneal schedules, can enhance quantum computing applications in operations research.
  • This study lays the groundwork for future quantum-enhanced optimization strategies in project management.