Optimization and decision-making model for product lifecycle carbon footprint driven by reinforcement learning
強化学習による製品ライフサイクルカーボンフットプリントの最適化・意思決定モデル (AI 翻訳)
Qing Liu
🤖 gxceed AI 要約
日本語
本論文は、製品ライフサイクル全体の炭素排出を最小化するため、マルチエージェント強化学習（MADDG-ICS）を提案。データ前処理にPCAを用い、学習安定性を向上。提案手法は模擬環境で24%の排出削減を達成し、MAE 4.6 kg CO2-eq等の精度を示した。
English
This paper proposes a multi-agent reinforcement learning framework (MADDG-ICS) to minimize carbon emissions across the product lifecycle. PCA is used for dimensionality reduction to stabilize learning. The model achieves 24% emissions reduction in simulation, with MAE 4.6 kg CO2-eq and MAPE 3.124%.
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SSBJや有報でのライフサイクル排出開示が進む中、本手法は動的な排出最適化を可能にし、GHG算定の効率化や削減策の立案に貢献できる。企業のサステナビリティ経営に応用が期待される。
In the global GX context
As global frameworks like ISSB and CSRD emphasize lifecycle emissions, this RL-based optimization offers a practical tool for real-time emission reduction decisions. The method integrates well with digital twin and lifecycle assessment practices, supporting corporate climate targets.
👥 読者別の含意
🔬研究者:RL手法をライフサイクル排出最適化に応用した先駆的研究であり、マルチエージェントと群知能の組み合わせに新規性がある。
🏢実務担当者:工場やサプライチェーンの排出削減に強化学習を活用する際の参考モデルとなる。リアルタイム意思決定に有用。
🏛政策担当者:政策としての排出削減目標達成に向けた技術的選択肢として提示可能だが、政策提言にはさらなる実証が必要。
📄 Abstract（原文）
Growing concern over industrial sustainability necessitates intelligent systems capable of minimizing carbon emissions across the entire product lifecycle, including raw material acquisition, manufacturing, logistics, usage, and end-of-life stages. Existing machine learning approaches provide improvements but often lack adaptability in dynamic environments, struggle with high-dimensional data, and exhibit slow convergence. This research proposes a reinforcement learning-based optimization framework for reducing lifecycle carbon footprint while maintaining operational efficiency. A novel Multi-Agent Deep Deterministic Policy Gradient with Intelligent Cockroach Swarm Algorithm (MADDG-ICS) is developed, where MADDG enables adaptive multi-agent policy learning and ICS enhances global exploration to avoid local optima. The model operates in a simulated lifecycle environment constructed from a dataset containing manufacturing, logistics, energy, and recycling parameters. Data preprocessing includes missing value imputation and min–max normalization. Principal Component Analysis (PCA) is applied strictly as a dimensionality reduction technique to transform correlated lifecycle variables into a compact set of orthogonal components, improving computational efficiency and stabilizing the reinforcement learning state representation. The proposed model achieves a 24% reduction in carbon emissions across the product lifecycle. The proposed framework is primarily evaluated using RL metrics such as cumulative reward, convergence, and policy stability. Regression metrics MAE (4.6 kg CO 2 -eq), RMSE (7.5 kg CO 2 -eq), and MAPE (3.124%) are reported as secondary measures to assess the accuracy of emission outcomes produced by the learned policies, not the RL optimization process itself. The MADDG-ICS framework enables real-time, emission-aware decision-making with improved convergence and robust policy learning, demonstrating strong potential for sustainable lifecycle management.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1007/s42452-026-09117-8first seen 2026-07-13 06:15:08
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