Abstract: In order to reduce the operating costs of logistics system, and improve the operational efficiency of logistics system, this paper establishes the logistics node location models with the aim to minimize logistics system operation costs and maximize customer time satisfaction. In the process of research, aiming at the shortcomings of multiple objective particle swarm optimization, it improves the algorithm from three aspects including external archive update, the choice of learning samples and particle variation. Finally the improved multiple objective particle swarm optimization is used to solve the logistics node location models. The result shows the improved algorithm has better distribution and convergence compared with other optimization algorithms.

Key words: multiple-objective optimization, particle swarm optimization, logistics node location, time satisfaction

摘要： 为了降低物流系统的运营成本，提高物流系统的运作效率，构建了物流系统运营成本最小以及顾客时间满意度最大的多目标物流节点选址模型，并在模型求解过程中针对多目标粒子群算法的不足，从外部存档的更新、粒子学习样本的选择以及粒子的变异三个方面进行改进，将改进的多目标粒子群算法用于物流节点选址模型的求解。仿真结果表明，改进的算法相较于其他优化算法，具有较好的分布性和收敛性。

关键词: 多目标优化, 粒子群算法, 物流节点选址, 时间满意度