Optimization of low-carbon multi-temperature joint distribution for fresh agricultural products under 3D loading constraints
3D積載制約下における生鮮農産物の低炭素多温度共同配送の最適化 (AI 翻訳)
Juping Shao, Fan Gao, Yanan Sun
🤖 gxceed AI 要約
日本語
本研究は、3D積載制約下での生鮮農産物の低炭素多温度共同配送の最適化モデルを開発した。遺伝的アルゴリズムとタブーサーチを統合したハイブリッドアルゴリズムを提案し、実データを用いて検証した結果、従来の単温度配送と比較して総コスト30.04%、炭素排出量30.62%削減を達成した。この結果は、冷鎖物流における配送計画と意思決定に実践的な支援を提供する。
English
This paper develops a low-carbon multi-temperature joint distribution optimization model under three-dimensional loading constraints. A hybrid algorithm integrating genetic algorithm and tabu search is proposed and validated with real-world data from a fresh produce supply chain company. Results show that the multi-temperature joint distribution reduces total operating costs by 30.04% and carbon emissions by 30.62% compared to conventional single-temperature distribution. The hybrid algorithm achieves faster convergence and better solution quality, demonstrating the effectiveness of integrating 3D loading, multi-temperature distribution, and low-carbon objectives.
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
日本では冷鎖物流の効率化と脱炭素化が重要課題であり、本研究の多温度共同配送モデルは、食品業界の物流コスト削減とCO2排出削減に貢献する可能性がある。また、3D積載制約を考慮することで、トラック積載率向上にも寄与し、物流2024年問題への対応にも役立つ。
In the global GX context
Globally, cold-chain logistics accounts for a significant share of food supply chain emissions. This paper provides a practical optimization framework that integrates loading, routing, and carbon reduction, which aligns with global efforts to decarbonize transportation and logistics. The hybrid algorithm approach can be extended to other regions and supply chains.
👥 読者別の含意
🔬研究者:This paper demonstrates an effective integration of 3D loading constraints, multi-temperature distribution, and carbon emission reduction in a single optimization model, offering a benchmark for future research in green logistics.
🏢実務担当者:Cold-chain companies can adopt the proposed multi-temperature joint distribution model and hybrid algorithm to reduce operating costs and carbon emissions in distribution planning.
🏛政策担当者:The significant emission reductions shown (30.62%) support policies that incentivize low-carbon logistics and multi-temperature joint distribution in food supply chains.
📄 Abstract（原文）
With the growing demand for fresh agricultural products, improving the efficiency and sustainability of cold-chain distribution has become increasingly important. Multi-temperature joint distribution provides an effective solution for serving products with different temperature requirements, yet its implementation remains challenging due to the need to coordinate vehicle routing, three-dimensional loading, and carbon-emission reduction objectives. To address this issue, this paper develops a low-carbon multi-temperature joint distribution optimization model under three-dimensional loading constraints. A hybrid algorithm integrating genetic algorithm and tabu search is proposed to solve the model efficiently. The proposed approach is validated using real-world data collected from a fresh agricultural products supply chain company. The results show that the multi-temperature joint distribution mode reduces total operating costs by 30.04% and carbon emissions by 30.62% compared with the conventional single-temperature distribution mode. Moreover, the proposed hybrid algorithm achieves faster convergence and better solution quality than the conventional genetic algorithm. These findings demonstrate the effectiveness of integrating three-dimensional loading, multi-temperature distribution, and low-carbon objectives within a unified optimization framework, providing practical support for distribution planning and decision-making in cold-chain logistics.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1371/journal.pone.0353789first seen 2026-07-15 05:00:22
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