Physics-informed offline reinforcement learning eliminates catastrophic fuel waste in maritime routing
物理情報を活用したオフライン強化学習が海事ルーティングにおける壊滅的な燃料浪費を排除 (AI 翻訳)
Aniruddha Bora, J. Chalfant, C. Chryssostomidis
🤖 gxceed AI 要約
日本語
国際海運は世界の温室効果ガス排出量の約3%を占めるが、航路計画は依然としてヒューリスティック手法が主流である。本論文では、物理情報を組み込んだオフライン強化学習フレームワークPIERを提案し、メキシコ湾の7航路で検証した結果、大圏航路と比較して平均CO2排出量を10%削減し、極端な燃料消費（中央値の1.5倍超）を9分の1に低減した。PIERは気象予報に依存せず、局所観測のみで性能を維持する。
English
International shipping accounts for ~3% of global GHG emissions, but routing remains heuristic. This paper presents PIER, an offline reinforcement learning framework integrating physics-informed state construction, that reduces mean CO2 emissions by 10% over great-circle routing on seven Gulf of Mexico routes. It eliminates catastrophic fuel waste (9-fold reduction in extreme fuel consumption) and is forecast-independent, maintaining performance under realistic uncertainty.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本は世界有数の海運国であり、日本郵船や商船三井など大手船社が燃料費削減とGHG排出削減に取り組んでいる。PIERのようなAIベースの航路最適化は、2030年までにGHG排出量を2018年比30%削減するという国際海運の目標達成に貢献し得る。また、日本発の技術としての応用も期待される。
In the global GX context
Global shipping faces pressure to decarbonize under IMO targets. This work offers a data-driven, forecast-independent routing method that reduces emissions and fuel variance significantly. It addresses practical challenges of deploying RL in real-world operations, relevant to the ISSB's focus on value-chain emissions and corporate disclosure of Scope 1 and 3 (shipping) strategies.
👥 読者別の含意
🔬研究者:Demonstrates a robust offline RL framework that maintains performance without online simulators, with transfer potential to other domains like wildfire evacuation.
🏢実務担当者:Provides a fuel-saving, safety-aware routing method that reduces emissions and operational risk, validated on real AIS data.
🏛政策担当者:Highlights the potential of AI in maritime decarbonization, supporting IMO GHG reduction targets through smart routing without reliance on forecasts.
📄 Abstract（原文）
International shipping produces approximately 3% of global greenhouse gas emissions, yet voyage routing remains dominated by heuristic methods. We present PIER (Physics-Informed, Energy-efficient, Risk-aware routing), an offline reinforcement learning framework that learns fuel-efficient, safety-aware routing policies from physics-calibrated environments grounded in historical vessel tracking data and ocean reanalysis products, requiring no online simulator. Validated on one full year (2023) of AIS data across seven Gulf of Mexico routes (840 episodes per method), PIER reduces mean CO2 emissions by 10% relative to great-circle routing. However, PIER's primary contribution is eliminating catastrophic fuel waste: great-circle routing incurs extreme fuel consumption (>1.5x median) in 4.8% of voyages; PIER reduces this to 0.5%, a 9-fold reduction. Per-voyage fuel variance is 3.5x lower (p<0.001), with bootstrap 95% CI for mean savings [2.9%, 15.7%]. Partial validation against observed AIS vessel behavior confirms consistency with the fastest real transits while exhibiting 23.1x lower variance. Crucially, PIER is forecast-independent: unlike A* path optimization whose wave protection degrades 4.5x under realistic forecast uncertainty, PIER maintains constant performance using only local observations. The framework combines physics-informed state construction, demonstration-augmented offline data, and a decoupled post-hoc safety shield, an architecture that transfers to wildfire evacuation, aircraft trajectory optimization, and autonomous navigation in unmapped terrain.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.48550/arxiv.2603.17319first seen 2026-06-29 06:17:09
🔔 こうした論文の新着を逃したくない方は キーワードアラート に登録（無料・3キーワードまで）。
gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。