Agentic CAMA-DRL: A Context-Aware Multi-Agent Deep Reinforcement Learning Framework for Multi-Stakeholder Charging Coordination of Last-Mile Delivery E-Bikes
Agentic CAMA-DRL：ラストマイル配送電動自転車のマルチステークホルダー充電調整のための文脈認識型マルチエージェント深層強化学習フレームワーク (AI 翻訳)
Sharif M, Seker H
🤖 gxceed AI 要約
日本語
都市部のラストマイル物流における電動自転車の充電インフラ最適化のため、文脈認識型マルチエージェント深層強化学習（CAMA-DRL）を提案。配送事業者、充電ステーション運営者、フリートプランナー、環境規制当局の4エージェントが協調し、リアルタイムの需要・電池残量・再生可能エネルギー・交通状況を考慮する。ロンドン都市シミュレーションで、ルールベース比でCO2排出42%削減、フリート稼働率28%向上などを達成。
English
This paper proposes Agentic CAMA-DRL, a context-aware multi-agent deep reinforcement learning framework for optimizing charging infrastructure of last-mile delivery e-bikes. Four stakeholder agents coordinate via DQNs conditioned on real-time signals, achieving 42% CO2 reduction, 28% fleet utilization increase, and 35% congestion reduction in a London simulation. The architecture supports migration to mixed e-bike and electric-van fleets.
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
日本では、都市部のラストマイル物流の脱炭素化が急務であり、電動アシスト自転車や小型EVの導入が進む中、充電インフラの効率的運用は重要。本手法は、再生可能エネルギー利用や系統負荷平準化にも寄与し、物流事業者のScope 1・2排出削減や、自治体の都市計画・環境政策に示唆を与える。
In the global GX context
Globally, this research addresses the growing need for sustainable urban logistics, aligning with TCFD/ISSB climate disclosure expectations for Scope 1 and 2 emissions reduction. The multi-agent coordination and context-aware charging can inform transition finance and infrastructure investment decisions. The demonstrated CO2 reduction potential supports corporate sustainability reporting and net-zero commitments.
👥 読者別の含意
🔬研究者:Provides a novel multi-agent RL framework with context encoding for EV charging coordination, showing synergistic effects of coordination and context.
🏢実務担当者:Offers a scalable, deployment-ready solution for optimizing e-bike fleet charging, reducing costs and emissions, and improving operational efficiency.
🏛政策担当者:Demonstrates the potential of AI-driven charging coordination to reduce urban emissions and congestion, informing sustainable transport policies.
📄 Abstract（原文）
<title>Abstract</title> <p>The rapid electrification of urban last-mile logistics has created an acute need for charging strategies that are simultaneously proactive, stakeholder-aware, and sensitive to real-time operating context. We propose an Agentic AI framework integrating Context-Aware Multi-Agent Deep Reinforcement Learning (CAMA-DRL) to optimise charging infrastructure for last-mile delivery e-bikes in urban micro-mobility ecosystems. Four autonomous stakeholder agents—delivery operators, charging station operators, fleet planners, and environmental regulators—coordinate via Deep Q-Networks (DQN) conditioned on real-time contextual signals including delivery demand, battery state-of-charge, renewable energy availability, and traffic conditions. Unlike reactive rule-based controllers, each agent plans across an extended delivery horizon and shares a common reward structure that internalises the system-wide consequences of locally optimal decisions, enabling coordinated rather than competing charging behaviour. Evaluated in a high-fidelity London urban simulation, CAMA-DRL achieves a 28% increase in fleet utilisation, 35% reduction in station congestion, 32% improvement in delivery punctuality, and a 42% CO2 emission reduction compared to rule-based and single-agent baselines. A component-wise ablation further isolates a 5–8% contribution attributable specifically to real-time context encoding beyond multi-agent coordination alone, confirming that the two mechanisms are synergistic rather than merely additive. These results establish Agentic CAMA-DRL as a scalable, deployment-ready solution for sustainable urban last-mile logistics, and the modular architecture suggests a clear migration path towards mixed e-bike and electric-van fleets sharing common charging hubs.</p>
🔗 Provenance — このレコードを発見したソース
- Research Square https://doi.org/10.21203/rs.3.rs-10115746/v1first seen 2026-08-07 04:37:56
🔔 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。