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Logistics Distribution Center Location Using Multi-swarm Cooperative Particle Swarm Optimizer

机译:多群合作粒子群优化算法的物流配送中心选址

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This study presented a new approach to solve logistics distribution center location problem. Multi-swarm Cooperative Particle Swarm Optimizer (MCPSO) (Niu et al ., 2007) is adopted to selects a certain number of locations as distribution centers in a logistics system so as to minimize the total cost of the whole logistics networks. A hybrid parallel encoding method is used and thus logistics distribution center lacation problem is mapped to the process of is birds (particles) foraging. By competition and collaboration of the individuals in MCPSO the optimal lacation solution is obtained. The experimental result demonstrated that the MCPSO achieves rapid convergence rate and better solutions compared with standard PSO.
机译:该研究提出了一种解决物流配送中心选址问题的新方法。采用多群合作粒子群优化器(MCPSO)(Niu等,2007)在物流系统中选择一定数量的位置作为配送中心,以最大程度地降低整个物流网络的总成本。使用混合并行编码方法,因此将物流配送中心的分层问题映射到鸟类(颗粒)觅食的过程。通过在MCPSO中个体的竞争和协作,可以获得最佳的贴合解决方案。实验结果表明,与标准PSO相比,MCPSO具有更快的收敛速度和更好的解决方案。

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