QUACOD decomposes drone scheduling into quantum-solvable subproblems via coordinate descent, outperforming prior quantum methods in completion time while scaling to 5x more drones and 35x more routes.
New benchmark instances for the capacitated vehicle routing problem,
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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Regression mapping from instance features to offline-tuned parameters improves Bilevel Late Acceptance Hill Climbing solutions by 0.28% on average over global tuning for the electric capacitated vehicle routing problem.
citing papers explorer
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QUACOD: Quantum Optimization via Coordinate Descent for Scalable Drone Scheduling
QUACOD decomposes drone scheduling into quantum-solvable subproblems via coordinate descent, outperforming prior quantum methods in completion time while scaling to 5x more drones and 35x more routes.
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Instance-Aware Parameter Configuration in Bilevel Late Acceptance Hill Climbing for the Electric Capacitated Vehicle Routing Problem
Regression mapping from instance features to offline-tuned parameters improves Bilevel Late Acceptance Hill Climbing solutions by 0.28% on average over global tuning for the electric capacitated vehicle routing problem.