低炭素志向のハブ港後背地分割と輸送ネットワーク最適化のためのハイパーヒューリスティックアルゴリズム
Hyper-heuristic algorithm for hubport hinterland division and transportation network optimization with low-carbon orientation (原題)
Daofeng Zhong, Chuanzhong Yin, Ying‐En Ge, Zi-ang ZHANG, Shiyuan Zheng
🤖 gxceed AI 要約
日本語
本研究は、港湾間の同質競争による資源非効率を解消し、低炭素なハブ港輸送ネットワークを構築するため、多目的混合整数計画モデルを提案する。タブーリストとエリート解プールを用いたハイパーヒューリスティックアルゴリズムを開発し、中国長江デルタを事例に検証した結果、上海港と寧波・舟山港の後背地がほぼ均等に分割され、炭素排出量を34.2%削減できることを示した。港湾当局へのネットワーク設計指針と炭素取引メカニズムの改善への政策的示唆を提供する。
English
This study proposes a multi-objective mixed-integer programming model to mitigate resource inefficiencies from homogeneous port competition and advance low-carbon hubport transportation networks. A hyper-heuristic algorithm with tabu-list and elite solution pool is developed and validated in China's Yangtze River Delta, showing near-equal hinterland division between Shanghai and Ningbo-Zhoushan ports and a 34.2% carbon emission reduction. It offers policy implications for port network design and carbon trading refinements.
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、港湾の脱炭素化や国際海運のGHG削減が課題となっており、本研究成果は港湾間の役割分担や輸送ネットワーク最適化による炭素削減の考え方が参考になる。特に、カーボンニュートラルポート（CNP）政策や、SSBJ開示におけるScope 3輸送排出量の算定・削減に資する知見を提供する。
In the global GX context
Globally, this research aligns with the decarbonization of freight transport and port operations, relevant to ISSB/CSRD disclosure of Scope 3 emissions. The optimization framework and case study offer insights for port authorities and policymakers in designing low-carbon networks and integrating carbon trading mechanisms, contributing to the broader energy transition in logistics.
👥 読者別の含意
🔬研究者:Provides a novel hyper-heuristic approach for multi-objective port hinterland division and network optimization with carbon reduction, useful for logistics and OR researchers.
🏢実務担当者:Port authorities and logistics firms can apply the network design insights to reduce carbon emissions and optimize hinterland operations.
🏛政策担当者:Offers evidence for refining carbon trading mechanisms and regional spatial planning to achieve transport decarbonization.
📄 Abstract（原文）
To mitigate resource inefficiencies arising from the homogeneous competition among ports and advance a low-carbon transportation network of hubports, multimodal freight transportation systems with low-pollution and low-consumption have gained significant scholarly attention. In the port hinterland transportation network, a reasonable hinterland for the main hubport can effectively eliminate homogeneous competition, realize the optimal allocation of transport resources, and promote the integrated and coordinated development of the region. From the perspective of hinterland division and coordination of transportation organization methods, a multi-objective mixed integer programming model is established to minimize the total transportation cost, total transportation time, and carbon emission cost. A novel CF-based selection Hyper-Heuristic (HH) is developed that employs an initial population generation mechanism based on tabu-list and an elite solution pool to explore the solution space. A case study of the Yangtze River Delta in China is conducted to verify the validity of the model and algorithm. The results show that the hinterland of Shanghai Port and Ningbo-Zhoushan Port are basically equal in scope, the Shanghai Port is in the upper part of the Yangtze River Delta region, mainly in Jiangsu and parts of Anhui and the Ningbo-Zhoushan Port is in the lower part of the Yangtze River Delta region and Jiangxi. Additionally, the carbon emission reduction effect reaches 34.2%. Finally, this research contributes dual policy implications: network design guidelines for port authorities, and carbon trading mechanism refinements through regional spatialization.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3846/transport.2026.27955first seen 2026-09-03 05:09:28
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