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Area of Science:

  • Computational Optimization
  • Quantum Computing Applications
  • Operations Research

Background:

  • Industrial transport robot scheduling presents complex optimization challenges.
  • Evaluating emerging quantum and quantum-inspired computing for practical problems is crucial.
  • Classical solvers remain the benchmark for optimization tasks.

Purpose of the Study:

  • To benchmark D-Wave's hybrid quantum-classical framework, Fujitsu's digital annealer, and Gurobi's classical solver.
  • To assess performance in terms of solution quality and runtime for a real-world transport robot scheduling problem.
  • To compare three distinct modeling approaches for the optimization task.

Main Methods:

  • Development of three mathematical models for the transport robot scheduling problem.
  • Implementation and execution of solvers: D-Wave's hybrid framework, Fujitsu's digital annealer, and Gurobi's classical solver.
  • Comparative analysis focusing on solution quality and end-to-end execution time.

Main Results:

  • The quantum-inspired digital annealer demonstrated promising performance.
  • The hybrid quantum annealer showed potential, with specific opportunities identified.
  • Direct comparison highlighted the strengths and weaknesses of each approach against the state-of-the-art classical solver.

Conclusions:

  • This study offers valuable insights into applying different optimization strategies to real-world problems.
  • Findings aid in evaluating the practical applicability and performance of quantum, quantum-inspired, and classical computing paradigms.
  • The research provides a workflow for tackling application-oriented optimization challenges.