目的 设计一种改进的樽海鞘算法求解所构建的模型，并验证该模型和算法的有效性和可行性。方法 建立最小化总运输成本、碳排放成本和最小化风险多目标模型，设计融合混沌映射、信息共享机制、多种群策略的樽海鞘算法求解该模型，并用其求解临沂—沈阳多式联运路径问题。结果 通过随机算例、实际案例验证以及与基本樽海鞘算法对比可知，改进的樽海鞘算法展现出优越的优化性能。结论 采用改进的樽海鞘算法求解低碳冷链多式联运路径优化模型，能够提供高效的解决方案，为决策者在处理多目标决策问题时提供一个有效的解决策略，有助于在实际应用中提供更优的运输路径规划方案。

The work aims to design an improved salp swarm algorithm to solve the model constructed, and verify the validity and practicability of the model and the algorithm. A multi-objective model aiming to minimize total transport costs and carbon emissions while reducing risks was established. The model was solved using a salp swarm algorithm that incorporated chaotic mapping, information sharing mechanism and multiple swarm strategies. Through random examples, actual case verification and comparison with the basic salp swarm algorithm, it could be seen that the improved salp swarm algorithm had superior optimization performance. Solving the low-carbon cold chain multimodal vehicle routing model with the improved salp swarm algorithm can improve efficient solutions, which provide effective strategies for decision makers to face the multi-objective decision problem, and help to formulate better vehicle routing in practical applications.