Low-Carbon Railway Traffic Flow Organization: Integrated and Two-Stage Optimization of Traffic Routing and Train Formation
低炭素鉄道交通流編成：経路選択と列車組成の統合的および二段階最適化 (AI 翻訳)
Yinan Zhao, Hanwen Jiang
🤖 gxceed AI 要約
日本語
本研究は鉄道貨物輸送における低炭素化を目的とし、交通流の経路選択、列車サービス構成、再分類決定を統合的に最適化するモデルを開発した。二段階モデルと比較し、統合モデルがより良い解を得る一方、二段階モデルは計算効率に優れることを実証。感度分析では排出量係数の影響を評価した。
English
This study develops an integrated optimization model for low-carbon railway freight transportation, jointly determining traffic routing, train service configuration, and reclassification decisions with carbon emission penalties. Results show the integrated model outperforms a two-stage sequential model in objective value, while the two-stage model achieves near-optimal solutions with much lower computational time.
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
日本では鉄道貨物の脱炭素化が進むが、本論文は路線単位の排出量を最適化する実用的な枠組みを提供する。SSBJ対応の物流改善にも応用可能。
In the global GX context
This work contributes to the global literature on low-carbon logistics by providing a quantitative optimization framework for railway operators seeking to minimize emissions and costs, relevant to TCFD and ISSB-aligned supply chain decarbonization.
👥 読者別の含意
🔬研究者:Operations researchers and transport modelers can adopt the integrated/two-stage optimization framework for low-carbon freight planning.
🏢実務担当者:Railway freight operators can apply the two-stage model to reduce carbon emissions and operational costs with manageable computation.
🏛政策担当者:Transport policymakers can use the model to assess the impact of carbon pricing on freight modal choice and infrastructure investment.
📄 Abstract（原文）
Railway freight transportation plays an important role in sustainable and low-carbon logistics systems. This study investigates a low-carbon railway traffic flow routing and train formation plan problem. An integrated optimization model is developed to jointly determine traffic flow routing, train service configuration, and reclassification decisions, with section running emissions and yard reclassification emissions included in the objective function as a carbon emission penalty cost. A two-stage optimization model under predetermined traffic flow routing is also constructed to evaluate the solution quality loss and computational efficiency of sequential decision-making. The nonlinear terms are linearized, and the resulting models are solved by Gurobi. A benchmark and eight extended medium-scale instances from 12-node to 19-node are used for numerical analysis. The results show that the integrated model obtains objective values no greater than those of the two-stage model, while the two-stage model provides near-optimal solutions with much shorter computational times. In the extended instances, the relative objective gap remains small, whereas the computational advantage of the two-stage model is evident. Sensitivity analysis further indicates that the carbon emission penalty coefficient mainly affects the objective value through the carbon penalty term, while total emissions remain relatively stable under the tested settings.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/su18126223first seen 2026-06-19 04:53:09
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