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Tomasz Śmierzchalski1, Jakub Pawłowski2,3, Artur Przybysz3

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Hybrid solvers effectively schedule Automatic Guided Vehicles (AGVs) in industrial settings. This approach, using classical heuristics potentially aided by quantum processing, offers a practical solution for complex AGV scheduling challenges.

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Area of Science:

  • Operations Research
  • Quantum Computing Applications
  • Industrial Engineering

Background:

  • The development of quantum computing aims to solve complex industrial problems.
  • Hybrid solvers combine classical heuristics with potential quantum processing unit (QPU) assistance.
  • Automatic Guided Vehicles (AGVs) scheduling is a critical optimization challenge in manufacturing.

Purpose of the Study:

  • To evaluate the effectiveness of state-of-the-art hybrid solvers for industrial-scale AGV scheduling.
  • To compare the suitability of AGV scheduling for quantum computing against similar problems like railway scheduling.
  • To demonstrate the practical applicability of hybrid solvers for real-world AGV management.

Main Methods:

  • Utilized D-Wave hybrid solvers, integrating classical heuristics with potential QPU support.
  • Modeled a realistic factory AGV scheduling scenario with space constraints.
  • Mapped the AGV problem to a railway scheduling analogy to assess quantum suitability.

Main Results:

  • Hybrid solvers performed comparably to existing classical solvers for AGV scheduling.
  • The AGV scheduling problem is identified as more amenable to quantum computation than railway scheduling due to constraint density.
  • A hybrid solver efficiently addressed a scenario with up to 21 AGVs, resolving potential deadlocks within seconds.

Conclusions:

  • Hybrid solver approaches show significant potential for solving practical AGV scheduling problems.
  • The study highlights the viability of hybrid methods for industrial optimization tasks, even with proprietary quantum components.
  • Real-world AGV scheduling, even with complex constraints, can be efficiently managed using current hybrid computational techniques.