Technology Category
Planning, Routing, and Scheduling: Optimization of Spatio-temporal SystemsSearch and Optimization: Combinatorial OptimizationConstraint Satisfaction and Optimization: Distributed CSP/Optimization
Multi-Agent Route Planning as a QUBO Problem
🤖 AI Summary
This study addresses the multi-agent path planning problem by selecting vehicles and their predefined routes to maximize spatial coverage of a road network while minimizing path overlap. The problem is formally cast for the first time as a Quadratic Unconstrained Binary Optimization (QUBO) model, featuring a single tunable penalty parameter that enables flexible trade-offs between coverage and redundancy, and supports both soft and hard constraint formulations. The authors prove the problem is NP-hard. Experimental evaluation on large-scale instances involving tens of thousands of vehicles in Barcelona demonstrates the approach’s effectiveness: under hard constraints, most solutions are Pareto-optimal, and results from D-Wave’s hybrid quantum annealing solver closely match those from Gurobi in objective value, with only minor differences in runtime.
Technology Category
Application Category
📝 Abstract
Multi-Agent Route Planning considers selecting vehicles, each associated with a single predefined route, such that the spatial coverage of a road network is increased while redundant 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 unique coverage rewards and pairwise overlap penalties. A single penalty parameter 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 an exact mixed-integer solver (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 essentially identical objective values with only minor differences in runtime as problem size grows.
Problem
Research questions and friction points this paper is trying to address.
Multi-Agent Route Planning
Spatial Coverage
Redundant Overlaps
QUBO
NP-hardness
Innovation
Methods, ideas, or system contributions that make the work stand out.
QUBO
Multi-Agent Route Planning
Quantum Annealing
Coverage-Overlap Trade-off
NP-hardness
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