A Machine Learning-Enhanced Tri-Objective Stowage Optimization Framework for Low-Carbon Finished Steel Maritime Supply Chains
機械学習強化型の三者目的最適化フレームワークによる低炭素成品鉄鋼海上サプライチェーン (AI 翻訳)
Bin Xu, Luyang Wang, Tingting Xiang, Rui Gu
🤖 gxceed AI 要約
日本語
本研究は、機械学習を活用した三者目的最適化フレームワークを提案し、鉄鋼製品の船舶積載計画問題を解決する。積載効率の最大化と炭素排出量の最小化を同時に達成し、実データで99.88%の積載率と95.5%のCO2削減を実証した。DNNサロゲートにより計算速度を3.57倍向上させた。
English
This study proposes a machine learning-enhanced tri-objective optimization framework for stowage planning of finished steel maritime logistics. It simultaneously maximizes deadweight utilization and minimizes carbon emissions, achieving 99.88% utilization and 95.5% CO2 reduction on real data. A DNN surrogate provides a 3.57x speedup.
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
日本の鉄鋼企業の海運ロジスティクスにおけるScope 3排出削減に直接応用可能。SOLASやCII規制にも対応しており、日本企業のGHG排出削減策として有用。
In the global GX context
This framework directly addresses maritime decarbonization under CII/FuelEU regulations, making it relevant for global shipping and steel supply chains. It demonstrates how AI-driven optimization can cut emissions while maintaining operational efficiency.
👥 読者別の含意
🔬研究者:Offers a novel tri-objective optimization with DNN surrogate for ship stowage; useful for logistics optimization and AI applications in decarbonization.
🏢実務担当者:Steel logistics managers can apply this method to reduce fuel costs and carbon emissions; integrates regulatory compliance (CII, FuelEU).
🏛政策担当者:Provides evidence that AI optimization can significantly reduce maritime emissions; could inform regulatory incentives for green shipping.
📄 Abstract（原文）
Decarbonizing downstream steel logistics remains underexplored in sustainable supply chain management. This study proposes a machine learning-enhanced tri-objective optimization framework for the ship stowage planning problem (SSPP). The framework handles heterogeneous finished steel products, including coils, plates, ingots, tubes, and sections. The model simultaneously maximizes deadweight utilization and minimizes a novel Adaptive Weighted Moment Balance (AWMB) index. It also minimizes voyage carbon emissions through a trim-and-heel resistance penalty. A spatial-to-sequential discretization strategy transforms the NP-hard placement problem into a tractable permutation optimization. A deep neural network (DNN) surrogate achieves a 3.57-fold speedup with only 1.52% hypervolume degradation. An improved NSGA-III algorithm with adaptive operators ensures Pareto front exploration. Embedded step-wise moment verification guarantees dynamic stability throughout loading and unloading. Validated on real data from a Chinese steel enterprise, the framework achieves 99.88% deadweight utilization, reduces transverse and longitudinal imbalance by 48.27% and 90.54%, and cuts CO2 emissions by 95.5% per voyage. SOLAS constraints, load line limits, and CII/FuelEU targets are addressed through embedded stability and capacity constraints. Multi-route and weather-dependent validation remains necessary before fleet-scale deployment.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.3390/pr14081233first seen 2026-06-29 06:20:02
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