Low-Carbon Oriented Routing Optimisation in Logistics Distribution Systems with Road Congestion Considerations
Xiao Guo, Fuya Yuan, Yuhan FU
🤖 gxceed AI 要約
日本語
物流セクターの脱炭素化に向け、渋滞を考慮した低炭素ルート最適化の二層計画モデルを提案。炭素税の内部化と遺伝的アルゴリズムによる解法を開発。Sioux Fallsネットワークでの事例分析により、適度な炭素税が排出削減に効果的であることを示した。
English
This study proposes a bi-level programming framework for low-carbon vehicle routing optimization that accounts for road congestion. It internalizes a carbon tax and uses a hybrid genetic algorithm. A case study on the Sioux Falls network shows that moderate carbon taxes effectively reduce emissions.
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
日本でも物流効率化とCO2削減は喫緊の課題。本手法はSSBJ対応のScope1・3排出量削減にも応用可能。政策面では炭素税と混雑税の連動を示唆し、日本版GX推進に示唆を与える。
In the global GX context
This paper addresses the dual challenge of logistics decarbonization and congestion, relevant to global climate disclosure (ISSB, TCFD). The bi-level optimization method with carbon tax internalization offers a practical tool for logistics operators and policymakers under carbon pricing regimes.
👥 読者別の含意
🔬研究者:A novel bi-level optimization framework integrating carbon tax and user equilibrium for logistics routing.
🏢実務担当者:Logistics operators can adopt the route optimization system to reduce both congestion costs and carbon tax liabilities.
🏛政策担当者:The case study supports progressive carbon pricing combined with congestion pricing to lower logistics emissions.
📄 Abstract（原文）
In the context of global decarbonisation initiatives, the logistics sector faces dual challenges: its substantial energy consumption and carbon footprint conflict with societal goals for a low-carbon economy, while escalating pressures from urban traffic congestion also inflate distribution costs. The environmental externalities and economic losses induced by the combination of inefficient routing and congestion have jointly motivated the emerging research field of low-carbon vehicle routing optimisation. To reconcile these issues, this study develops a bi-level programming framework for low-carbon-oriented vehicle routing optimisation that explicitly accounts for road congestion. The upper-level model aims to minimise the total cost (including vehicle fixed cost, transportation cost, carbon emission cost and time-window penalty) by internalising a carbon tax constraint. The lower-level model employs a user equilibrium (UE) model, focusing on minimising travel time from the perspective of road users. A hybrid solution methodology (GA-Tent & Frank-Wolfe) is proposed, integrating an enhanced genetic algorithm with Tent chaos mapping for global optimisation and a modified Frank-Wolfe algorithm for traffic assignment. Finally, a case study using the Sioux Falls network demonstrates that traffic congestion increases carbon emissions, but a moderate carbon tax increase can effectively reduce vehicle carbon emissions. These insights suggest policymakers should implement progressive carbon pricing mechanisms coupled with dynamic congestion pricing, while logistics operators should prioritise route optimisation systems with real-time traffic adaptation capabilities.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.7307/ptt.v38i6.3086first seen 2026-07-01 05:04:04 · last seen 2026-07-01 05:04:15
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