Towards quantum-ready optimization: A QUBO model for task assignment to autonomous mobile robots
Ole Christian Prüfer, Jens Heger · Procedia CIRP · 2026
Autonomous Mobile Robots (AMRs) play an increasingly important role in flexible and reconfigurable production systems. Assigning heterogeneous tasks to multiple AMRs constitutes a combinatorial optimization problem that becomes computationally intractable as system complexity grows. This paper presents a Quadratic Unconstrained Binary Optimization (QUBO) model for AMR task assignment, integrating scheduling and resource allocation within a Hamiltonian formulation. The model is parameterized and evaluated using both exact and heuristic solvers to analyze scalability and validity across benchmark scenarios. Results demonstrate the trade-off between computational effort and solution optimality, confirming the feasibility of the QUBO representation for this problem class. The formulated model creates a basis for future implementation on quantum hardware for optimization using Quantum Annealing (QA) or the Quantum Approximate Optimization Algorithm (QAOA), thereby bridging the gap between classical optimization methods and new quantum computing paradigms.