Low-carbon Distribution Path Optimization of Urban Fresh Logistics Based on Improved Ant Colony Algorithm
改良アリコロニーアルゴリズムに基づく都市生鮮物流の低炭素配送経路最適化 (AI 翻訳)
Fang Qi
🤖 gxceed AI 要約
日本語
都市生鮮物流の低炭素配送を目的に、総配送コストと総炭素排出量を双目的とする多目的最適化モデルを構築。フェロモン更新機構とヒューリスティック関数重みを改良したアリコロニーアルゴリズムを提案し、シミュレーションでコスト8.2%削減、炭素排出量11.5%削減を実証。経済性と環境性の両立を図る。
English
This paper constructs a multi-objective optimization model for low-carbon urban fresh logistics distribution, minimizing total cost and carbon emissions. An improved ant colony algorithm with optimized pheromone update and heuristic weights reduces cost by 8.2% and emissions by 11.5% in simulations, enhancing convergence and path rationality.
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
中国の事例だが、日本の物流業界でもカーボンニュートラル対応が急務。配送ルート最適化によるScope 1排出削減は、SSBJ開示や物流効率化に直結する。
In the global GX context
While China-focused, the optimization approach for low-carbon logistics is globally relevant, aligning with Scope 1 emission reduction and operational efficiency under climate disclosure frameworks like ISSB and CSRD.
👥 読者別の含意
🔬研究者:物流最適化と炭素排出削減の統合モデル、改良ACOの有効性を確認できる。
🏢実務担当者:配送ルート最適化によるコスト削減と排出削減の具体的な実装可能性を示す。
🏛政策担当者:都市物流の低炭素化政策への示唆が得られる。
📄 Abstract（原文）
With the rapid expansion of fresh food e-commerce and urban instant retail, urban fresh logistics has become a core component of modern urban circulation system. Different from general commodity logistics, fresh cold chain distribution has strict requirements on delivery time, transportation temperature and service quality, which also brings problems such as repeated vehicle routes, high energy consumption and excessive carbon emissions in actual operation. Traditional fresh logistics path optimization mostly takes the shortest distance or the lowest transportation cost as the single optimization goal, ignoring the low-carbon development requirements under the dual-carbon policy, and the classic optimization algorithm is prone to local optimal solutions and slow convergence speed in complex urban traffic scenarios, resulting in poor practicability of optimization results. Aiming at the above pain points, this paper constructs a multi-objective optimization model of urban fresh logistics low-carbon distribution, which takes total distribution cost and total carbon emission as dual optimization objectives, and sets multi-dimensional constraints including vehicle load limit, customer time window and driving speed limitation. On this basis, an improved ant colony algorithm is proposed by optimizing the pheromone update mechanism and heuristic function weight, which effectively makes up for the defects of the traditional ant colony algorithm. Finally, simulation example analysis is carried out with urban fresh distribution scene data. The results show that compared with the traditional algorithm optimization scheme, the improved algorithm can reduce the total distribution cost by 8.2% and the total carbon emission by 11.5%, and significantly improve the convergence efficiency and path rationality. This research realizes the coordinated optimization of economic benefit and environmental benefit of fresh logistics distribution, and can provide effective theoretical support and practical reference for the intelligent and low-carbon transformation of urban fresh logistics enterprises.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.54097/3asvdf81first seen 2026-08-16 04:57:31
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