What question did this study set out to answer?

The research aims to optimize low-carbon cold-chain logistics by balancing efficiency and sustainability.

February 26, 2026MathematicsOpen Access

Multi-Objective Collaborative Optimization for Low-Carbon Cold-Chain Routing with Dynamic Demand

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Authors

QHQiaoying HuXiamen University of Technology XLXi LiuBGI Group (China)XJXiaoyun JiangXiamen University of Technology

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Implication

Multi-objective optimization improves carbon emissions and customer satisfaction in cold-chain logistics, suggesting insights into carbon pricing effects.

Key Points

The research aims to optimize low-carbon cold-chain logistics by balancing efficiency and sustainability.
Developed a Multi-Objective Low-Carbon Cold-Chain Vehicle Routing Problem model.
Applied deep reinforcement learning to enhance optimization algorithms.
Performed numerical experiments on benchmark instances to validate the proposed method.
Achieved improved vehicle routing with reduced carbon emissions.
Enhanced customer satisfaction through optimized service delivery.
Demonstrated significant effects of carbon pricing on operational costs and emissions.

Cite This Study

Hu et al. (2026) studied this question.

synapsesocial.com/papers/699f95a81bc9fecf3dab3ba7 https://doi.org/10.3390/math14050753

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Implication

Multi-objective optimization improves carbon emissions and customer satisfaction in cold-chain logistics, suggesting insights into carbon pricing effects.

Multi-Objective Collaborative Optimization for Low-Carbon Cold-Chain Routing with Dynamic Demand

Discussion

Member takes

Implication

Key Points

Cite This Study

Multi-Objective Collaborative Optimization for Low-Carbon Cold-Chain Routing with Dynamic Demand

Discussion

Member takes

Implication

Key Points

Cite This Study

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