{"ID":2840296,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.12879","arxiv_id":"2511.12879","title":"Resilient and Efficient Allocation for Large-Scale Autonomous Fleets via Decentralized Coordination","abstract":"Operating large autonomous fleets demands fast, resilient allocation of scarce resources (such as energy and fuel, charger access and maintenance slots, time windows, and communication bandwidth) under uncertainty. We propose a side-information-aware approach for resource allocation at scale that combines distributional predictions with decentralized coordination. Local side information shapes per-agent risk models for consumption, which are coupled through chance constraints on failures. A lightweight consensus-ADMM routine coordinates agents over a sparse communication graph, enabling near-centralized performance while avoiding single points of failure. We validate the framework on real urban road networks with autonomous vehicles and on a representative satellite constellation, comparing against greedy, no-side-information, and oracle central baselines. Our method reduces failure rates by 30-55% at matched cost and scales to thousands of agents with near-linear runtime, while preserving feasibility with high probability.","short_abstract":"Operating large autonomous fleets demands fast, resilient allocation of scarce resources (such as energy and fuel, charger access and maintenance slots, time windows, and communication bandwidth) under uncertainty. We propose a side-information-aware approach for resource allocation at scale that combines distributiona...","url_abs":"https://arxiv.org/abs/2511.12879","url_pdf":"https://arxiv.org/pdf/2511.12879v1","authors":"[\"Ashish Kumar Perukari\",\"Polina Khoroshevskaya\"]","published":"2025-11-17T02:15:12Z","proceeding":"cs.GT","tasks":"[\"cs.GT\"]","methods":"[]","has_code":false}