改进粒子群算法在多目标物流选址中的应用

摘要

Considering that the location of logistics center has great influence on the capacity of the logistics system, the logistics cost and logistics service capacity must therefore be considered when building a logistics location-allocation model.For the multi-objective location-allocation problem on logistics, this paper proposes a cooperative learning multi-objective particle swarm optimization algorithm of seeking for Perato dominant.The simulation result shows that the model is correct and effective, and the discrete multi-objective particle swarm optimization algorithm can effectively solve the multi-objective location-allocation problem.%考虑到物流选址规划对系统运营的影响,以物流运营成本最小及服务满意度最大为目标,构建工厂-物流中心-分销商三级物流选址规划模型.为了避免粒子群算法容易早熟和容易落入局部最优的缺陷,引入合作学习思想,针对多目标选址规划问题,用多目标合作粒子群算法(MCPSO)求多目标离散型物流选址规划模型的Pareto解.通过对实例进行仿真模拟,求解模型的选址-分派方案,并结合灵敏度分析,证明所提出算法的有效性.