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Area of Science:

  • Quantum Computing
  • Operations Research
  • Artificial Intelligence

Background:

  • Quantum annealing is a computational technique explored for optimization problems.
  • Controlling fleets of automated guided vehicles (AGVs) presents significant logistical challenges.

Purpose of the Study:

  • To develop a quantum annealing formulation for optimizing AGV routes and minimizing travel time.
  • To evaluate the effectiveness of reverse annealing for enhancing AGV fleet management.

Main Methods:

  • A novel formulation for AGV route control using quantum annealing was proposed.
  • Simulations in a virtual plant validated the formulation against a greedy algorithm.
  • Reverse annealing was employed, initializing from greedy algorithm solutions to refine results.

Main Results:

  • The proposed formulation demonstrated faster distribution compared to a greedy approach.
  • Reverse annealing improved performance over standard quantum annealing.
  • The method achieved up to 10x speedup compared to the commercial classical solver Gurobi.

Conclusions:

  • Quantum annealing offers a viable solution for multi-AGV system optimization.
  • Reverse annealing presents a powerful optimization technique for complex routing problems.
  • This study highlights the potential of quantum annealers in real-world logistics and distribution.