Multi-Agent Route Planning as a QUBO Problem
For researchers in combinatorial optimization and quantum computing, this provides a new QUBO formulation for a practical route planning problem, though the results are incremental as they show existing solvers perform comparably.
This paper formalizes Multi-Agent Route Planning as a QUBO problem, proving NP-hardness and demonstrating that D-Wave hybrid solvers and Gurobi achieve similar objective values on Barcelona instances with up to 10,000 vehicles, with minor runtime differences as problem size grows.
Multi-Agent Route Planning considers selecting vehicles, each associated with a single predefined route, such that route-level coverage utility is maximized while redundant spatial overlaps are limited. This paper gives a formal problem definition, proves NP-hardness by reduction from the Weighted Set Packing problem, and derives a Quadratic Unconstrained Binary Optimization formulation whose coefficients directly encode route utility rewards and pairwise overlap penalties. A single penalty parameter $λ$ controls the coverage--overlap trade-off. We distinguish between a soft regime, which supports multi-objective exploration, and a hard regime, in which the penalty is strong enough to effectively enforce near-disjoint routes. We describe a practical pipeline for generating city instances, constructing candidate routes, building the QUBO matrix, and solving it with a binary quadratic programming baseline (Gurobi), simulated annealing, and D-Wave hybrid quantum annealing. Experiments on Barcelona instances with up to $10{,}000$ vehicles reveal a clear coverage--overlap knee and show that Pareto-optimal solutions are mainly obtained under the hard-penalty regime, while D-Wave hybrid solvers and Gurobi achieve very similar objective values on matching configurations with only minor runtime differences as problem size grows.