目的 为优化冷链物流运输路径,提高整体效率,提出基于粒子群自进化的冷链物流运输路径优化方法。方法 在时间与载重等约束条件下,构建融合惩罚函数的目标函数,以最小化总成本并最大化顾客满意度,提升服务质量。建立由物流中心、供应仓库构成的多层多分支树,更细致地描述冷链物流网络的结构和特点,计算个体粒子的初始位置、速度,获得路径参数。采用交叉算子和变异算子改进粒子群算法,判定更新状态,求出粒子群自进化修正速度,实现冷链物流运输路径优化函数求解,提高冷链物流运输路径优化的效率。结果 实验结果表明,所提方法应用后能高效地完成配送任务,准时率始终保持在98%以上。结论 所提方法在保证服务质量的同时最大限度地利用资源,运输路径优化效果佳。
Abstract
The work aims to propose a cold chain logistics transportation route optimization method based on particle swarm self-evolution, so as to optimize the cold chain logistics transportation route and improve overall efficiency. Under constraints such as time and load, an objective function that integrated penalty functions was constructed to minimize total costs and maximize customer satisfaction, thereby improving service quality. A multi-layer and multi-branch tree composed of logistics centers and supply warehouses was constructed to more meticulously depict the structure and characteristics of the cold chain logistics network. The initial positions and velocities of individual particles were calculated to obtain route parameters. The particle swarm optimization algorithm was improved using crossover and mutation operators. By determining and updating the state, the self-evolution correction velocity of the particle swarm was calculated to solve the cold chain logistics transportation route optimization function, thereby improving the efficiency of cold chain logistics transportation route optimization. Experimental results demonstrated that the proposed method could efficiently complete delivery tasks, with a punctuality rate consistently maintained at over 98%. In conclusion, the proposed method optimizes the transportation route effectively while ensuring service quality and maximizing resource utilization.
关键词
粒子群自进化 /
冷链物流 /
变异算子 /
运输路径优化 /
交叉算子
Key words
particle swarm self-evolution /
cold chain logistics /
mutation operator /
optimization of transportation routes /
crossover operator
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